| kircIMR | IntegMultiReg | Reduced TCGA-KIRC Multi-Platform Survival Example | list | | |
| simIMR | IntegMultiReg | Simulated Multi-Platform Example Data | list | | |
| aml | lrstat | Acute myelogenous leukemia survival data from the survival package | data.frame | 23 | 3 |
| heart | lrstat | Stanford heart transplant data from the survival package | data.frame | 172 | 8 |
| immdef | lrstat | Simulated CONCORDE trial data from the rpsftm package | data.frame | 1000 | 9 |
| ingots | lrstat | The binary data from Cox and Snell (1989, pp. 10-11). | tbl_df | 25 | 4 |
| liver | lrstat | The liver data used in SAS PROC PHREG documentation examples. | tbl_df | 418 | 7 |
| rawdata | lrstat | A simulated time-to-event data set with 10 replications | data.frame | 4910 | 7 |
| sexagg | lrstat | Urinary tract infection data from the logistf package | data.frame | 36 | 9 |
| shilong | lrstat | The randomized clinical trial SHIVA data in long format from the ipcwswitch package | data.frame | 602 | 19 |
| six | lrstat | The repeated measures data from the "Six Cities" study of the health effects of air pollution (Ware et al. 1984). | tbl_df | 64 | 6 |
| tobin | lrstat | Tobin's tobit data from the survival package | data.frame | 20 | 3 |
| gisco_coastal_lines | giscoR | Coastal lines 2016 'sf' object | sf | 2129 | 2 |
| gisco_countries_2024 | giscoR | Countries 2024 'sf' object | sf | 263 | 12 |
| gisco_countrycode | giscoR | Database with different country code schemes and world regions | tbl_df | 249 | 13 |
| gisco_db | giscoR | Cached GISCO database | tbl_df | 10987 | 11 |
| gisco_nuts_2024 | giscoR | NUTS 2024 'sf' object | sf | 1798 | 10 |
| ACTG175 | mets | ACTG175, block randomized study from speff2trial package | data.frame | 2139 | 28 |
| bmt | mets | The Bone Marrow Transplant Data | data.frame | 408 | 5 |
| calgb8923 | mets | CALGB 8923, twostage randomization SMART design | data.frame | 593 | 30 |
| CPH_HPN_CRBSI | mets | Rates for HPN program for patients of Copenhagen Cohort | list | | |
| dermalridges | mets | Dermal ridges data (families) | data.frame | 106 | 10 |
| dermalridgesMZ | mets | Dermal ridges data (monozygotic twins) | data.frame | 36 | 5 |
| diabetes | mets | The Diabetic Retinopathy Data | data.frame | 394 | 7 |
| haplo | mets | haplo fun data | list | | |
| hfactioncpx12 | mets | hfaction, subset of block randomized study HF-ACtion from WA package | data.frame | 2132 | 7 |
| melanoma | mets | The Melanoma Survival Data | data.frame | 205 | 6 |
| mena | mets | Menarche data set | data.frame | 2000 | 7 |
| migr | mets | Migraine data | data.frame | 4065 | 6 |
| multcif | mets | Multivariate Cumulative Incidence Function example data set | data.frame | 400 | 8 |
| np | mets | np data set | data.frame | 10000 | 7 |
| prt | mets | Prostate data set | data.frame | 29222 | 6 |
| sTRACE | mets | The TRACE study group of myocardial infarction | data.frame | 500 | 9 |
| TRACE | mets | The TRACE study group of myocardial infarction | data.frame | 1878 | 9 |
| ttpd | mets | ttpd discrete survival data on interval form | data.frame | 1000 | 6 |
| tTRACE | mets | The TRACE study group of myocardial infarction | data.frame | 1000 | 9 |
| twinbmi | mets | BMI data set | data.frame | 11188 | 7 |
| twinstut | mets | Stutter data set | data.frame | 32894 | 6 |
| indipsa | tvGarchKF | Selective Stock Price Index | ts | 3186 | 1 |
| hospital_staff | couplr | Hospital staff scheduling example dataset | list | | |
| metals | RCtest | Forecasts of Base Metals Prices | matrix | 165 | 15 |
| example_records | scopusflow | A worked example harvest, in the shape 'Scopus' records take | scopus_records | 138 | 10 |
| blanden | markovchain | Mobility between income quartiles | table | 4 | 4 |
| craigsendi | markovchain | CD4 cells counts on HIV Infects between zero and six month | table | 3 | 3 |
| holson | markovchain | Holson data set | data.frame | 1000 | 12 |
| kullback | markovchain | Example from Kullback and Kupperman Tests for Contingency Tables | list | | |
| preproglucacon | markovchain | Preprogluccacon DNA protein bases sequences | data.frame | 1572 | 2 |
| rain | markovchain | Alofi island daily rainfall | data.frame | 1096 | 2 |
| sales | markovchain | Sales Demand Sequences | matrix | 269 | 5 |
| tm_abs | markovchain | Single Year Corporate Credit Rating Transititions | matrix | 8 | 8 |
| bw | np | Cross Country Growth Panel | rbandwidth | | |
| bw.all | np | Cross-Sectional Data on Wages | rbandwidth | | |
| bw.subset | np | Cross-Sectional Data on Wages | rbandwidth | | |
| cps71 | np | Canadian High School Graduate Earnings | data.frame | 205 | 2 |
| Engel95 | np | 1995 British Family Expenditure Survey | data.frame | 1655 | 10 |
| Italy | np | Italian GDP Panel | data.frame | 1008 | 2 |
| oecdpanel | np | Cross Country Growth Panel | data.frame | 616 | 7 |
| wage1 | np | Cross-Sectional Data on Wages | data.frame | 526 | 24 |
| AF92Lt | lifecontingencies | Uk AM AF 92 life tables | lifetable | | |
| AM92Lt | lifecontingencies | Uk AM AF 92 life tables | lifetable | | |
| de_angelis_di_falco | lifecontingencies | Italian Health Insurance Data | list | | |
| demoCanada | lifecontingencies | Canada Mortality Rates for UP94 Series | data.frame | 120 | 7 |
| demoChina | lifecontingencies | China Mortality Rates for life table construction | data.frame | 106 | 8 |
| demoFrance | lifecontingencies | French population life tables | data.frame | 113 | 5 |
| demoGermany | lifecontingencies | German population life tables | data.frame | 112 | 3 |
| demoIta | lifecontingencies | Italian population life tables for males and females | data.frame | 121 | 18 |
| demoJapan | lifecontingencies | Japan Mortality Rates for life table construction | data.frame | 110 | 3 |
| demoUk | lifecontingencies | UK life tables | data.frame | 74 | 3 |
| demoUsa | lifecontingencies | United States Social Security life tables | data.frame | 114 | 7 |
| soa08 | lifecontingencies | Society of Actuaries Illustrative Life Table object. | lifetable | | |
| soa08Act | lifecontingencies | Society of Actuaries Illustrative Life Table with interest rate at 6 | actuarialtable | | |
| SoAISTdata | lifecontingencies | SoA illustrative service table | data.frame | 41 | 6 |
| soaLt | lifecontingencies | Society of Actuaries life table | data.frame | 111 | 2 |
| gdp | reviser | Vintages Data | tbl_df | 47980 | 4 |
| Ames | coursekata | Ames, Iowa housing data | tbl_df | 185 | 21 |
| class_data | coursekata | Generated "class data" for exploring pairwise tests | tbl_df | 105 | 2 |
| er | coursekata | Emergency room canine therapy | tbl_df | 84 | 53 |
| fevdata | coursekata | Forced Expiratory Volume (FEV) Data | tbl_df | 654 | 5 |
| Fingers | coursekata | Data from introductory statistics students at a university. | data.frame | 157 | 17 |
| FingersMessy | coursekata | Raw data from introductory statistics students at a university. | data.frame | 210 | 17 |
| game_data | coursekata | Simulated math game data. | data.frame | 105 | 2 |
| penguins | coursekata | A modified form of the 'palmerpenguins::penguins' data set. | tbl_df | 333 | 7 |
| Smallville | coursekata | Simulated housing data | data.table | 32 | 4 |
| Survey | coursekata | Students at a university were asked to enter a random number between 1-20 into a survey. | data.frame | 211 | 1 |
| Tables | coursekata | Tables data | data.frame | 44 | 2 |
| tip_exp | coursekata | Simulated data for an experiment about smiley faces and tips | tbl_df | 89 | 3 |
| TipExperiment | coursekata | Data from an experiment about smiley faces and tips | data.frame | 44 | 5 |
| World | coursekata | Data on countries from the Happy Planet Index project. | tbl_df | 130 | 14 |
| inter | mizer | Alias for 'NS_interaction' | matrix | 12 | 12 |
| NS_interaction | mizer | Example interaction matrix for the North Sea example | matrix | 12 | 12 |
| NS_params | mizer | Example MizerParams object for the North Sea example | MizerParams | | |
| NS_sim | mizer | Example MizerSim object for the North Sea example | MizerSim | | |
| NS_species_params | mizer | Example species parameter set based on the North Sea | given_species_params | 12 | 8 |
| NS_species_params_gears | mizer | Example species parameter set based on the North Sea with different gears | given_species_params | 12 | 9 |
| collection_catalog | torchvision | Dataset Collection Catalog | data.frame | 43 | 17 |
| example_admin_claims | dataganger | Example administrative claims dataset | tbl_df | 300 | 9 |
| example_health_survey | dataganger | Example health survey dataset | tbl_df | 200 | 10 |
| example_registry | dataganger | Example disease registry dataset | tbl_df | 150 | 10 |
| geographic_sample | dataganger | Geographic synthetic sample data | data.frame | 50 | 5 |
| individual_sample | dataganger | Individual-level synthetic sample data | data.frame | 200 | 7 |
| temporal_sample | dataganger | Temporal synthetic sample data | data.frame | 365 | 5 |
| Abortion | vcdExtra | Abortion Opinion Data | table | | |
| Accident | vcdExtra | Traffic Accident Victims in France in 1958 | data.frame | 80 | 5 |
| AirCrash | vcdExtra | Air Crash Data | data.frame | 439 | 5 |
| Alligator | vcdExtra | Alligator Food Choice | data.frame | 80 | 5 |
| Asbestos | vcdExtra | Effect of Exposure to Asbestos | matrix | 5 | 4 |
| Bartlett | vcdExtra | Bartlett Data on Plum Root Cuttings | table | | |
| Burt | vcdExtra | Burt (1950) Data on Hair, Eyes, Head and Stature | data.frame | 36 | 5 |
| Caesar | vcdExtra | Risk Factors for Infection in Caesarian Births | table | | |
| Cancer | vcdExtra | Survival of Breast Cancer Patients | table | | |
| Cormorants | vcdExtra | Advertising Behavior by Males Cormorants | data.frame | 343 | 8 |
| CrabSatellites | vcdExtra | Horseshoe Crab Mating | data.frame | 173 | 5 |
| CyclingDeaths | vcdExtra | London Cycling Deaths | data.frame | 208 | 2 |
| DaytonSurvey | vcdExtra | Dayton Student Survey on Substance Use | data.frame | 32 | 6 |
| Depends | vcdExtra | Dependencies of R Packages | table | | |
| Detergent | vcdExtra | Detergent Preference Data | table | | |
| Donner | vcdExtra | Survival in the Donner Party | data.frame | 90 | 5 |
| Draft1970 | vcdExtra | USA 1970 Draft Lottery Data | data.frame | 366 | 3 |
| Draft1970table | vcdExtra | USA 1970 Draft Lottery Table | table | 12 | 3 |
| Dyke | vcdExtra | Sources of Knowledge of Cancer | table | | |
| Fungicide | vcdExtra | Carcinogenic Effects of a Fungicide | array | | |
| Geissler | vcdExtra | Geissler's Data on the Human Sex Ratio | data.frame | 90 | 4 |
| Gilby | vcdExtra | Clothing and Intelligence Rating of Children | table | 6 | 4 |
| Glass | vcdExtra | British Social Mobility from Glass(1954) | data.frame | 25 | 3 |
| GSS | vcdExtra | General Social Survey- Sex and Party affiliation | data.frame | 6 | 3 |
| HairEyePlace | vcdExtra | Hair Color and Eye Color in Caithness and Aberdeen | array | | |
| Hauser79 | vcdExtra | Hauser (1979) Data on Social Mobility | data.frame | 25 | 3 |
| Heart | vcdExtra | Sex, Occupation and Heart Disease | table | | |
| Heckman | vcdExtra | Labour Force Participation of Married Women 1967-1971 | table | | |
| HospVisits | vcdExtra | Hospital Visits Data | table | 3 | 3 |
| HouseTasks | vcdExtra | Household Tasks Performed by Husbands and Wives | table | 13 | 4 |
| Hoyt | vcdExtra | Minnesota High School Graduates | table | | |
| ICU | vcdExtra | ICU data set | data.frame | 200 | 22 |
| JobSat | vcdExtra | Cross-classification of job satisfaction by income | table | 4 | 4 |
| Mammograms | vcdExtra | Mammogram Ratings | matrix | 4 | 4 |
| Mental | vcdExtra | Mental Impairment and Parents SES | data.frame | 24 | 3 |
| Mice | vcdExtra | Mice Depletion Data | data.frame | 30 | 4 |
| Mobility | vcdExtra | Social Mobility data | table | 5 | 5 |
| PhdPubs | vcdExtra | Publications of PhD Candidates | data.frame | 915 | 6 |
| Reinis | vcdExtra | Risk Factors for Coronary Heart Disease | table | | |
| ShakeWords | vcdExtra | Shakespeare's Word Type Frequencies | data.frame | 100 | 2 |
| Titanicp | vcdExtra | Passengers on the Titanic | data.frame | 1309 | 6 |
| Toxaemia | vcdExtra | Toxaemia Symptoms in Pregnancy | data.frame | 60 | 5 |
| TV | vcdExtra | TV Viewing Data | array | | |
| Vietnam | vcdExtra | Student Opinion about the Vietnam War | data.frame | 40 | 4 |
| Vote1980 | vcdExtra | Race and Politics in the 1980 Presidential Vote | data.frame | 28 | 4 |
| WorkerSat | vcdExtra | Worker Satisfaction Data | data.frame | 8 | 4 |
| Yamaguchi87 | vcdExtra | Occupational Mobility in Three Countries | data.frame | 75 | 4 |
| AFY | capn | Abbott-Fenichel-Yun Data (1-D Stochastic Example) | list | | |
| forest | capn | Douglas-Fir Forest Data in Western Oregon (1-D Example) | list | | |
| GOM | capn | Gulf of Mexico Reef Fish Example Data (1-D Deterministic) | list | | |
| LV | capn | Prey-Predator (Lotka-Volterra) Example Data (2-D Deterministic) | list | | |
| AddHealth | heplots | Adolescent Mental Health Data | data.frame | 4344 | 3 |
| Adopted | heplots | Adopted Children | data.frame | 62 | 6 |
| Bees | heplots | Captive and maltreated bees | data.frame | 246 | 6 |
| Diabetes | heplots | Diabetes Dataset | data.frame | 145 | 6 |
| dogfood | heplots | Dogfood Preferences | data.frame | 16 | 3 |
| FootHead | heplots | Head measurements of football players | data.frame | 90 | 7 |
| Headache | heplots | Treatment of Headache Sufferers for Sensitivity to Noise | data.frame | 98 | 6 |
| Hernior | heplots | Recovery from Elective Herniorrhaphy | data.frame | 32 | 9 |
| Iwasaki_Big_Five | heplots | Personality Traits of Cultural Groups | tbl_df | 203 | 7 |
| mathscore | heplots | Math scores for basic math and word problems | data.frame | 12 | 3 |
| MockJury | heplots | Effects Of Physical Attractiveness Upon Mock Jury Decisions | data.frame | 114 | 17 |
| NeuroCog | heplots | Neurocognitive Measures in Psychiatric Groups | data.frame | 242 | 10 |
| NLSY | heplots | National Longitudinal Survey of Youth Data | data.frame | 243 | 6 |
| oral | heplots | Effect of Delay in Oral Practice in Second Language Learning | tbl_df | 56 | 5 |
| Oslo | heplots | Oslo Transect Subset Data | data.frame | 332 | 14 |
| Overdose | heplots | Overdose of Amitriptyline | data.frame | 17 | 7 |
| Parenting | heplots | Father Parenting Competence | data.frame | 60 | 4 |
| peng | heplots | Size measurements for penguins near Palmer Station, Antarctica | tbl_df | 333 | 8 |
| Plastic | heplots | Plastic Film Data | data.frame | 20 | 5 |
| Pottery2 | heplots | Chemical Analysis of Romano-British Pottery | data.frame | 48 | 12 |
| Probe1 | heplots | Response Speed in a Probe Experiment | data.frame | 11 | 5 |
| Probe2 | heplots | Response Speed in a Probe Experiment | data.frame | 20 | 6 |
| RatWeight | heplots | Weight Gain in Rats Exposed to Thiouracil and Thyroxin | data.frame | 27 | 6 |
| ReactTime | heplots | Reaction Time Data | data.frame | 10 | 6 |
| Rohwer | heplots | Rohwer Data Set | data.frame | 69 | 10 |
| RootStock | heplots | Growth of Apple Trees from Different Root Stocks | data.frame | 48 | 5 |
| Sake | heplots | Taste Ratings of Japanese Rice Wine (Sake) | data.frame | 30 | 10 |
| schooldata | heplots | School Data | data.frame | 70 | 8 |
| schoolsites | heplots | Schooldata Sites | data.frame | 70 | 7 |
| Skulls | heplots | Egyptian Skulls | data.frame | 150 | 5 |
| SocGrades | heplots | Grades in a Sociology Course | data.frame | 40 | 10 |
| SocialCog | heplots | Social Cognitive Measures in Psychiatric Groups | data.frame | 139 | 5 |
| TIPI | heplots | Data on the Ten Item Personality Inventory | data.frame | 1799 | 16 |
| VocabGrowth | heplots | Vocabulary growth data | data.frame | 64 | 4 |
| WeightLoss | heplots | Weight Loss Data | data.frame | 34 | 7 |
| datsoilspc | soilVAE | Soil spectroscopy example dataset used in the soilVAE vignettes | data.frame | 391 | 5 |
| rxReservedKeywords | rxode2 | A list and description of rxode2 supported reserved keywords | data.frame | 31 | 3 |
| rxResidualError | rxode2 | A description of Rode2 supported residual errors | data.frame | 183 | 6 |
| rxSyntaxFunctions | rxode2 | A list and description of Rode supported syntax functions | data.frame | 106 | 3 |
| hitop_artifacts | hitop | Distribution Artifact Manifest | tbl_df | 33 | 7 |
| hitopbr_items | hitop | HiTOP-BR Item Data | spec_tbl_df | 45 | 8 |
| hitopbr_scales | hitop | HiTOP-BR Scale Data | tbl_df | 8 | 5 |
| hitophsum_choices | hitop | HiTOP-HSUM Choice Sets | spec_tbl_df | 185 | 3 |
| hitophsum_items | hitop | HiTOP-HSUM Item Data | spec_tbl_df | 650 | 9 |
| hitopsr_definitions | hitop | HiTOP-SR Definitions | spec_tbl_df | 93 | 4 |
| hitopsr_items | hitop | HiTOP-SR Item Data | tbl_df | 405 | 6 |
| hitopsr_scales | hitop | HiTOP-SR Scale Data | tbl_df | 76 | 5 |
| hitopsr_subscales | hitop | HiTOP-SR Subscale Data | tbl_df | 17 | 6 |
| ku_hitopbr | hitop | Real HiTOP-BR Data | tbl_df | 411 | 47 |
| ku_hitopsr | hitop | Real HiTOP-SR Data | tbl_df | 411 | 407 |
| ku_pid5sf | hitop | Real PID-5-SF Data | spec_tbl_df | 386 | 101 |
| pid_domains | hitop | Personality Inventory for DSM-5 Domain Data | tbl_df | 5 | 4 |
| pid_items | hitop | Personality Inventory for DSM-5 Item Data | spec_tbl_df | 220 | 15 |
| pid_norms | hitop | Personality Inventory for DSM-5 Normative Tables | tbl_df | 4606 | 5 |
| pid_scales | hitop | Personality Inventory for DSM-5 Scale Data | list | | |
| sim_hitopbr | hitop | Simulated HiTOP-BR Data | tbl_df | 100 | 45 |
| sim_hitopsr | hitop | Simulated HiTOP-SR Data | tbl_df | 100 | 405 |
| sim_pid5 | hitop | Simulated PID-5 Data | tbl_df | 100 | 220 |
| sim_pid5bf | hitop | Simulated PID-5-BF Data | tbl_df | 100 | 25 |
| sim_pid5sf | hitop | Simulated PID-5-SF Data | tbl_df | 100 | 100 |
| Kexample | prova | Example 'K'nowledge object produced by learn() | prova_K | | |
| meta_penguins | prova | Metadata file for "penguins" dataset | data.frame | 8 | 10 |
| metadataExample | prova | Example metadata file | data.frame | 2 | 10 |
| are | vitals | An R Eval | tbl_df | 29 | 7 |
| GrowthNUTS2 | lagsarlmtree | Determinants of Regional Economic Growth | data.frame | 255 | 58 |
| WeightsNUTS2 | lagsarlmtree | Spatial Weights for European Union NUTS2 Regions | list | | |
| ad_data | modeldata | Alzheimer's disease data | tbl_df | 333 | 131 |
| ames | modeldata | Ames Housing Data | tbl_df | 2930 | 74 |
| attrition | modeldata | Job attrition | data.frame | 1470 | 31 |
| biomass | modeldata | Biomass data | data.frame | 536 | 8 |
| bivariate_test | modeldata | Example bivariate classification data | tbl_df | 710 | 3 |
| bivariate_train | modeldata | Example bivariate classification data | tbl_df | 1009 | 3 |
| bivariate_val | modeldata | Example bivariate classification data | tbl_df | 300 | 3 |
| car_prices | modeldata | Kelly Blue Book resale data for 2005 model year GM cars | tbl_df | 804 | 18 |
| cat_adoption | modeldata | Cat Adoption | tbl_df | 2257 | 20 |
| cells | modeldata | Cell body segmentation | tbl_df | 2019 | 58 |
| check_times | modeldata | Execution time data | tbl_df | 13626 | 25 |
| chem_proc_yield | modeldata | Chemical manufacturing process data set | tbl_df | 176 | 58 |
| Chicago | modeldata | Chicago ridership data | tbl_df | 5698 | 50 |
| cls_data_2026 | modeldata | Two classes, Two predictors 2026 Edition | tbl_df | 1000 | 3 |
| concrete | modeldata | Compressive strength of concrete mixtures | tbl_df | 1030 | 9 |
| covers | modeldata | Raw cover type data | data.frame | 40 | 1 |
| credit_data | modeldata | Credit data | data.frame | 4454 | 14 |
| crickets | modeldata | Rates of Cricket Chirps | tbl_df | 31 | 3 |
| deliveries | modeldata | Food Delivery Time Data | tbl_df | 10012 | 31 |
| drinks | modeldata | Sample time series data | tbl_df | 309 | 2 |
| grants_2008 | modeldata | Grant acceptance data | integer | | |
| grants_other | modeldata | Grant acceptance data | data.frame | 8190 | 1503 |
| grants_test | modeldata | Grant acceptance data | data.frame | 518 | 1503 |
| hepatic_injury_qsar | modeldata | Predicting hepatic injury from chemical information | tbl_df | 281 | 377 |
| hotel_rates | modeldata | Daily Hotel Rate Data | tbl_df | 15402 | 28 |
| hpc_cv | modeldata | Class probability predictions | data.frame | 3467 | 7 |
| hpc_data | modeldata | High-performance computing system data | tbl_df | 4331 | 8 |
| ischemic_stroke | modeldata | Clinical data used to predict ischemic stroke | tbl_df | 126 | 29 |
| leaf_id_flavia | modeldata | Leaf identification data (Flavia) | tbl_df | 1907 | 59 |
| lending_club | modeldata | Loan data | tbl_df | 9857 | 23 |
| meats | modeldata | Fat, water and protein content of meat samples | tbl_df | 215 | 103 |
| mlc_churn | modeldata | Customer churn data | tbl_df | 5000 | 20 |
| oils | modeldata | Fatty acid composition of commercial oils | tbl_df | 96 | 8 |
| parabolic | modeldata | Parabolic class boundary data | tbl_df | 500 | 3 |
| pathology | modeldata | Liver pathology data | data.frame | 344 | 2 |
| pd_speech | modeldata | Parkinson's disease speech classification data set | tbl_df | 252 | 752 |
| penguins | modeldata | Palmer Station penguin data | tbl_df | 344 | 7 |
| permeability_qsar | modeldata | Predicting permeability from chemical information | tbl_df | 165 | 1108 |
| Sacramento | modeldata | Sacramento CA home prices | tbl_df | 932 | 9 |
| scat | modeldata | Morphometric data on scat | tbl_df | 110 | 19 |
| Smithsonian | modeldata | Smithsonian museums | tbl_df | 20 | 3 |
| solubility_test | modeldata | Solubility predictions from MARS model | data.frame | 316 | 2 |
| stackoverflow | modeldata | Annual Stack Overflow Developer Survey Data | tbl_df | 5594 | 21 |
| stations | modeldata | Chicago ridership data | character | | |
| steroidogenic_toxicity | modeldata | Predicting steroidogenic toxicity with assay data | tbl_df | 162 | 13 |
| tate_text | modeldata | Tate Gallery modern artwork metadata | tbl_df | 4284 | 5 |
| taxi | modeldata | Chicago taxi data set | tbl_df | 10000 | 7 |
| testing_data | modeldata | Fine foods example data | tbl_df | 1000 | 3 |
| training_data | modeldata | Fine foods example data | tbl_df | 4000 | 3 |
| two_class_dat | modeldata | Two class data | tbl_df | 791 | 3 |
| two_class_example | modeldata | Two class predictions | data.frame | 500 | 4 |
| wa_churn | modeldata | Watson churn data | tbl_df | 7043 | 20 |
| aids | JMbayes2 | Didanosine versus Zalcitabine in HIV Patients | data.frame | 1405 | 12 |
| aids.id | JMbayes2 | Didanosine versus Zalcitabine in HIV Patients | data.frame | 467 | 12 |
| pbc2 | JMbayes2 | Mayo Clinic Primary Biliary Cirrhosis Data | data.frame | 1945 | 20 |
| pbc2.id | JMbayes2 | Mayo Clinic Primary Biliary Cirrhosis Data | data.frame | 312 | 20 |
| prothro | JMbayes2 | Prednisone versus Placebo in Liver Cirrhosis Patients | data.frame | 2968 | 9 |
| prothros | JMbayes2 | Prednisone versus Placebo in Liver Cirrhosis Patients | data.frame | 488 | 4 |
| igo_year_format3 | igoR | IGO-year membership data | data.frame | 19335 | 235 |
| state_year_format3 | igoR | Country-year IGO membership data | data.frame | 15557 | 537 |
| states2016 | igoR | State system membership (v2016) | data.frame | 243 | 11 |
| sppEquivalencies_CA | LandR | Table of species name equivalencies for Canadian trees | data.table | 204 | 30 |
| checkerboard | geomander | Checkerboard | sf | 64 | 4 |
| checkerboard_adj | geomander | Checkerboard Adjacency | list | | |
| nrcsd | geomander | nrcsd | sf | 1 | 15 |
| orange | geomander | orange | sf | 118 | 26 |
| precincts | geomander | precincts | sf | 278 | 4 |
| rockland | geomander | rockland | sf | 4764 | 26 |
| towns | geomander | towns | sf | 7 | 6 |
| va_blocks | geomander | va_blocks | sf | 6354 | 26 |
| va_vtd | geomander | va_vtd | sf | 93 | 17 |
| va18sub | geomander | va18sub | sf | 90 | 9 |
| frt_attr | scfmutils | Attributes and fire metrics of the 15 Fire Regime Units (FRTs) | data.frame | 15 | 10 |
| fru_attr | scfmutils | Attributes and fire metrics of the 60 Fire Regime Units (FRUs) | data.frame | 60 | 10 |
| foray_counts | tulpaObs | Repeated point-count abundance survey (synthetic) | list | | |
| meadow_cover | tulpaObs | Grassland vegetation-cover panel (synthetic) | data.frame | 150 | 6 |
| peatland_occu | tulpaObs | Peatland occupancy survey (synthetic) | list | | |
| fips_2000 | censable | Counties FIPS 2000 | tbl_df | 3141 | 3 |
| fips_2010 | censable | Counties FIPS 2010 | tbl_df | 3143 | 3 |
| fips_2020 | censable | Counties FIPS 2020 | tbl_df | 3143 | 3 |
| mt_county | censable | Montana County Data | sf | 56 | 21 |
| stata | censable | stata (State Data) | data.frame | 57 | 6 |
| returns | svines | Stock returns of 20 companies | data.frame | 1296 | 20 |
| absorp | caret | Fat, Water and Protein Content of Meat Samples | matrix | 215 | |
| bbbDescr | caret | Blood Brain Barrier Data | data.frame | 208 | 134 |
| cars | caret | Kelly Blue Book resale data for 2005 model year GM cars | data.frame | 804 | 18 |
| cox2Class | caret | COX-2 Activity Data | factor | | |
| cox2Descr | caret | COX-2 Activity Data | data.frame | 462 | 255 |
| cox2IC50 | caret | COX-2 Activity Data | numeric | | |
| dhfr | caret | Dihydrofolate Reductase Inhibitors Data | data.frame | 325 | 229 |
| endpoints | caret | Fat, Water and Protein Content of Meat Samples | matrix | 215 | |
| fattyAcids | caret | Fatty acid composition of commercial oils | data.frame | 96 | 7 |
| GermanCredit | caret | German Credit Data | data.frame | 1000 | 62 |
| logBBB | caret | Blood Brain Barrier Data | numeric | | |
| mdrrClass | caret | Multidrug Resistance Reversal (MDRR) Agent Data | factor | | |
| mdrrDescr | caret | Multidrug Resistance Reversal (MDRR) Agent Data | data.frame | 528 | 342 |
| oilType | caret | Fatty acid composition of commercial oils | factor | | |
| potteryClass | caret | Pottery from Pre-Classical Sites in Italy | factor | | |
| Sacramento | caret | Sacramento CA Home Prices | data.frame | 932 | 9 |
| scat | caret | Morphometric Data on Scat | data.frame | 110 | 19 |
| scat_orig | caret | Morphometric Data on Scat | data.frame | 122 | 20 |
| segmentationData | caret | Cell Body Segmentation | data.frame | 2019 | 61 |
| ex_survey | saros.base | ex_survey: Mockup dataset of a survey. | tbl_df | 300 | 32 |
| ex_survey_ch_overview | saros.base | ex_survey_ch_overview: Mock overview of chapter structure | data.frame | 5 | 5 |
| sheepFrequencyStats | sonicscrewdriver | Sheep frequencyStats | list | | |
| STP | sonicscrewdriver | STP: Standard Temperature and Pressure | list | | |
| academic_awards | gorica | Academic awards data | data.frame | 200 | 4 |
| hox_2010 | gorica | Sesame Street data based on Hox (2010) | data.frame | 2000 | 6 |
| nederhof_2014 | gorica | Data based on Nederhof, Ormel, and Oldehinkel (2014) | data.frame | 310 | 4 |
| reading_ach | gorica | Reading achievement data | data.frame | 10320 | 5 |
| school_admissions | gorica | High School Admissions Data | data.frame | 30 | 3 |
| stevens_1999 | gorica | Sesame Street data based on Stevens (1999) | data.frame | 240 | 14 |
| wechsler | gorica | Wechsler intelligence test data | data.frame | 1680 | 10 |
| cran_to_spdx | cffr | Mapping between 'License' fields and SPDX | data.frame | 86 | 2 |
| datos_administrativos | lupa | Datos administrativos sintéticos con problemas sembrados | data.frame | 13 | 10 |
| datos_operativos | lupa | Datos operativos sintéticos y neutrales | data.frame | 13 | 10 |
| fmri_benchmark_datasets | fmrireg | Benchmark fMRI datasets | list | | |
| coffee_data | modelbased | Sample dataset from a course about analysis of factorial designs | data.frame | 120 | 5 |
| efc | modelbased | Sample dataset from the EFC Survey | data.frame | 908 | 28 |
| fish | modelbased | Sample data set | data.frame | 250 | 9 |
| puppy_love | modelbased | More puppy therapy data | data.frame | 30 | 4 |
| real_experiment | glyexp | Real glycoproteomics experiment | GlycoproteomicSE | | |
| real_experiment2 | glyexp | Real glycomics experiment | GlycomicSE | | |
| glydb_data | glydb | GlyGen Glycan Data | tbl_df | 19436 | 6 |
| sampleBGN | Ruido | Sample noise.matrix object | noise.matrix | | |
| population | weightflow | Synthetic target population (sampling frame) | data.frame | 4495 | 8 |
| sample_one | weightflow | Synthetic address sample with one selected person per household | data.frame | 417 | 17 |
| sample_survey | weightflow | Synthetic person sample with a take-all household roster | data.frame | 467 | 11 |
| DSIVdata | naivereg | The data generating for the DSIV | matrix | 100 | 72 |
| DSLIVEdata | naivereg | The data generating for the DSLIVE | matrix | 100 | 222 |
| LIVEdata | naivereg | The data generating for the LIVE | matrix | 200 | |
| naivedata | naivereg | The data generating for the naivereg | matrix | 1000 | 22 |
| TradeAndGrowthData | naivereg | Trade and growth data | data.frame | 159 | 23 |
| Aloe14 | metaSEM | Multivariate effect sizes between classroom management self-efficacy (CMSE) and other variables reported by Aloe et al. (2014) | data.frame | 16 | 14 |
| BCG | metaSEM | Dataset on the Effectiveness of the BCG Vaccine for Preventing Tuberculosis | data.frame | 13 | 16 |
| Becker09 | metaSEM | Ten Studies of Correlation Matrices used by Becker (2009) | list | | |
| Becker83 | metaSEM | Studies on Sex Differences in Conformity Reported by Becker (1983) | data.frame | 10 | 5 |
| Becker92 | metaSEM | Six Studies of Correlation Matrices reported by Becker (1992; 1995) | list | | |
| Becker94 | metaSEM | Five Studies of Ten Correlation Matrices reported by Becker and Schram (1994) | list | | |
| Berkey98 | metaSEM | Five Published Trails from Berkey et al. (1998) | data.frame | 5 | 8 |
| Boer16 | metaSEM | Correlation Matrices from Boer et al. (2016) | list | | |
| Bornmann07 | metaSEM | A Dataset from Bornmann et al. (2007) | data.frame | 66 | 9 |
| Chan17 | metaSEM | Dataset from Chan, Jones, Jamieson, and Albarracin (2017) | data.frame | 34 | 10 |
| Cheung00 | metaSEM | Fifty Studies of Correlation Matrices used in Cheung and Chan (2000) | list | | |
| Cheung09 | metaSEM | A Dataset from TSSEM User's Guide Version 1.11 by Cheung (2009) | list | | |
| Cooke16 | metaSEM | Correlation Matrices from Cooke et al. (2016) | list | | |
| Cooper03 | metaSEM | Selected effect sizes from Cooper et al. (2003) | data.frame | 56 | 5 |
| Digman97 | metaSEM | Factor Correlation Matrices of Big Five Model from Digman (1997) | list | | |
| Gleser94 | metaSEM | Two Datasets from Gleser and Olkin (1994) | list | | |
| Gnambs18 | metaSEM | Correlation Matrices from Gnambs, Scharl, and Schroeders (2018) | list | | |
| HedgesOlkin85 | metaSEM | Effects of Open Education Reported by Hedges and Olkin (1985) | data.frame | 4 | 6 |
| Hox02 | metaSEM | Simulated Effect Sizes Reported by Hox (2002) | data.frame | 20 | 4 |
| Hunter83 | metaSEM | Fourteen Studies of Correlation Matrices reported by Hunter (1983) | list | | |
| issp05 | metaSEM | A Dataset from ISSP (2005) | list | | |
| issp89 | metaSEM | A Dataset from Cheung and Chan (2005; 2009) | list | | |
| Jaramillo05 | metaSEM | Dataset from Jaramillo, Mulki and Marshall (2005) | data.frame | 61 | 11 |
| Kalaian96 | metaSEM | Multivariate effect sizes reported by Kalaian and Raudenbush (1996) | data.frame | 47 | 13 |
| Mak09 | metaSEM | Eight studies from Mak et al. (2009) | data.frame | 8 | 10 |
| Mathieu15 | metaSEM | Correlation Matrices from Mathieu et al. (2015) | list | | |
| Nam03 | metaSEM | Dataset on the Environmental Tobacco Smoke (ETS) on children's health | data.frame | 59 | 12 |
| Nohe15A1 | metaSEM | Correlation Matrices from Nohe et al. (2015) | list | | |
| Nohe15A2 | metaSEM | Correlation Matrices from Nohe et al. (2015) | list | | |
| Norton13 | metaSEM | Studies on the Hospital Anxiety and Depression Scale Reported by Norton et al. (2013) | list | | |
| Roorda11 | metaSEM | Studies on Students' School Engagement and Achievement Reported by Roorda et al. (2011) | list | | |
| Scalco17 | metaSEM | Correlation Matrices from Scalco et al. (2017) | list | | |
| Stadler15 | metaSEM | Correlations from Stadler et al. (2015) | data.frame | 60 | 8 |
| Tenenbaum02 | metaSEM | Correlation coefficients reported by Tenenbaum and Leaper (2002) | data.frame | 48 | 12 |
| vanderPol17 | metaSEM | Dataset on the effectiveness of multidimensional family therapy in treating adolescents with multiple behavior problems | data.frame | 61 | 17 |
| wvs94a | metaSEM | Forty-four Studies from Cheung (2013) | data.frame | 42 | 7 |
| wvs94b | metaSEM | Forty-four Covariance Matrices on Life Satisfaction, Job Satisfaction, and Job Autonomy | list | | |
| dice | netCoin | Data: Roll a die (100 times). | data.frame | 100 | 11 |
| ess | netCoin | Data: European Social Survey, Round-8. | data.frame | 1000 | 5 |
| events | netCoin | Data: Attributes of the dice events. | data.frame | 10 | 4 |
| families | netCoin | Data: Italian families in the Renaissance. | data.frame | 16 | 6 |
| finches | netCoin | Data: Finches'attributes in Galapagos islands. | data.frame | 13 | 4 |
| Galapagos | netCoin | Data: Finches' presence in Galapagos Islands. | data.frame | 17 | 13 |
| links | netCoin | Data: Links between Italian families in the Renaissance. | data.frame | 36 | 17 |
| sociologists | netCoin | Data: Classical sociologists. | data.frame | 16 | 11 |
| works | netCoin | Data: Classical sociological works. | tbl_df | 54 | 4 |
| AEJapp | sreg | Replication data for: Iron Deficiency and Schooling Attainment in Peru (Chong et al, 2016) | tbl_df | 215 | 62 |
| NKdata | APRScenario | Example Dataset NKdata | data.frame | 244 | 4 |
| veteran | survalis | Veteran's Administration Lung Cancer Trial Data | data.frame | 137 | 8 |
| phq9 | easyRasch2 | PHQ-9 Depression Screener (NHANES Subsample) | data.frame | 600 | 12 |
| alco1 | highdir | Alcohol Consumption Data | data.frame | 14 | 9 |
| alco2 | highdir | Alcohol Consumption by Gender | data.frame | 28 | 10 |
| sochealth | spicy | Simulated social-health survey | tbl_df | 1200 | 24 |
| AozoraBunkoSnapshot | ldccr | Meta data of text files published on Aozora Bunko | tbl_df | 19502 | 55 |
| NekoText | ldccr | Whole text of ‘Wagahai Wa Neko Dearu’ written by Natsume Souseki from Aozora Bunko | character | | |
| hiroba | audubon | Whole tokens of 'Porano no Hiroba' written by Miyazawa Kenji from Aozora Bunko | data.frame | 26849 | 5 |
| polano | audubon | Whole text of 'Porano no Hiroba' written by Miyazawa Kenji from Aozora Bunko | character | | |
| lines | jprailway | List of railway lines | tbl_df | 860 | 13 |
| polylines | jprailway | Polylines of Japanese railway | sf | 1890 | 10 |
| stations | jprailway | List of railway stations | sf | 11439 | 18 |
| Shapiro_Wilk_coef_table | normality | Shapiro-Wilk normality test (coefficients) | data.frame | 50 | 25 |
| Shapiro_Wilk_pval_table | normality | Shapiro-Wilk normality test (p-values) | data.frame | 50 | 11 |
| BFIGritHope | semFromKeys | BFI, Grit, and Hope data from Bainbridge, Ludeke, & Smillie (2022), Study 2b. | data.frame | 388 | 80 |
| cross_blended_hypsometric_tints_db | tidyterra | Cross-blended hypsometric tints | tbl_df | 41 | 6 |
| grass_db | tidyterra | GRASS color tables | tbl_df | 2920 | 6 |
| hypsometric_tints_db | tidyterra | Hypsometric palettes database | tbl_df | 1223 | 6 |
| princess_db | tidyterra | Princess palettes database | tbl_df | 75 | 5 |
| volcano2 | tidyterra | Updated topographic information on Auckland's Maungawhau volcano | matrix | 174 | |
| Population_Data | KSgeneral | The proportion of inhabitants living within a 200 kilometer wide costal strip in 232 countries in the year 2010 | data.frame | 232 | 1 |
| truncatedscore | targeted | Scores truncated by death | data.frame | 3500 | 11 |
| motivation | tsn | Repeated Motivation Measurements | data.frame | 4871 | 13 |
| steps | tsn | Daily Step Counts | data.frame | 34089 | 3 |
| radf_crit | exuber | Stored Monte Carlo Critical Values | crit | | |
| sim_data | exuber | Simulated dataset | tbl_df | 100 | 5 |
| sim_data_wdate | exuber | Simulated dataset | tbl_df | 100 | 6 |
| bugs_long | ggstatsplot | Tidy version of the "Bugs" dataset. | tbl_df | 372 | 6 |
| iris_long | ggstatsplot | Edgar Anderson's Iris Data in long format. | tbl_df | 600 | 6 |
| movies_long | ggstatsplot | Movie information and user ratings from IMDB.com (long format). | tbl_df | 1579 | 8 |
| Titanic_full | ggstatsplot | Titanic dataset. | tbl_df | 2201 | 5 |
| identity | rankingQ | Identity-ranking data analyzed in Atsusaka and Kim (2025) | tbl_df | 1082 | 16 |
| identity_w | rankingQ | Identity-ranking data with estimated weights based on inverse probability weighting | tbl_df | 1082 | 18 |
| sp500 | autotune | S&P 500 Stock Data | list | | |
| dsciat1 | implicitMeasures | Data set with SC-IAT D-scores (Dark) | data.frame | 162 | 15 |
| dsciat2 | implicitMeasures | Data set with SC-IAT D-scores (Milk) | data.frame | 162 | 15 |
| iatdscores | implicitMeasures | Data set with IAT D-scores | data.frame | 162 | 7 |
| raw_data | implicitMeasures | Dataset with one IAT and two SC-IATs | data.frame | 84726 | 6 |
| brfss_states | brfssdata | BRFSS reporting jurisdictions: FIPS codes, names, and Census regions | tbl_df | 56 | 5 |
| brfss_std_pop_2000 | brfssdata | The 2000 projected U.S. standard population | tbl_df | 25 | 6 |
| dummy_enrol | heims | Dummy enrolment file | data.table | 5 | 56 |
| E089_decoder | heims | Decoders | data.table | 2 | 2 |
| E095_decoder | heims | Decoders | data.table | 2 | 2 |
| E306_decoder | heims | Decoders | data.table | 632 | 4 |
| E310_decoder | heims | Decoders | data.table | 26 | 3 |
| E312_decoder | heims | Decoders | data.table | 7 | 2 |
| E316_decoder | heims | Decoders | data.table | 5 | 2 |
| E327_decoder | heims | Decoders | data.table | 8 | 2 |
| E329_decoder | heims | Decoders | data.table | 6 | 2 |
| E330_decoder | heims | Decoders | data.table | 3 | 2 |
| E331_decoder | heims | Decoders | data.table | 3 | 3 |
| E337_decoder | heims | Decoders | data.table | 3 | 2 |
| E346_decoder | heims | Decoders | data.table | 299 | 3 |
| E348_decoder | heims | Decoders | data.table | 13 | 2 |
| E355_decoder | heims | Decoders | data.table | 5 | 2 |
| E358_decoder | heims | Decoders | data.table | 7 | 2 |
| E386_decoder | heims | Decoders | data.table | 139 | 9 |
| E392_decoder | heims | Decoders | data.table | 5 | 2 |
| E461_decoder | heims | Decoders | data.table | 12 | 4 |
| E463_decoder | heims | Decoders | data.table | 439 | 2 |
| E464_decoder | heims | Decoders | data.table | 439 | 2 |
| E490_decoder | heims | Decoders | data.table | 53 | 2 |
| E551_decoder | heims | Decoders | data.table | 3 | 2 |
| E562_decoder | heims | Decoders | data.table | 12 | 2 |
| E919_decoder | heims | Decoders | data.table | 10 | 2 |
| E920_decoder | heims | Decoders | data.table | 10 | 2 |
| E922_decoder | heims | Decoders | data.table | 2 | 2 |
| first_levels | heims | First levels | data.table | 8 | 2 |
| FOE_uniter | heims | Decoders | data.table | 439 | 4 |
| HE_Provider_decoder | heims | Decoders | data.table | 632 | 4 |
| heims_data_dict | heims | HEIMS data dictionary | list | | |
| U490_decoder | heims | Decoders | data.table | 6 | 2 |
| welfare | pcdid | Welfare caseloads data | data.frame | 5967 | 18 |
| Qaqish | mipfp | Qaqish | list | | |
| spnamur | mipfp | Synthetic population of Namur (Belgium) | data.frame | 105248 | 6 |
| aw2009 | circumplex | Standardized octant scores on hypothetical circumplex scales | data.frame | 5 | 8 |
| cais | circumplex | Child and Adolescent Interpersonal Survey | circumplex_instrument | | |
| csie | circumplex | Circumplex Scales of Interpersonal Efficacy | circumplex_instrument | | |
| csig | circumplex | Circumplex Scales of Intergroup Goals | circumplex_instrument | | |
| csip | circumplex | Circumplex Scales of Interpersonal Problems | circumplex_instrument | | |
| csiv | circumplex | Circumplex Scales of Interpersonal Values | circumplex_instrument | | |
| iei | circumplex | Interpersonal Emotion Inventory | circumplex_instrument | | |
| igicr | circumplex | Interpersonal Goals Inventory for Children, Revised Version | circumplex_instrument | | |
| iip32 | circumplex | Inventory of Interpersonal Problems, Brief Version | circumplex_instrument | | |
| iip64 | circumplex | Inventory of Interpersonal Problems | circumplex_instrument | | |
| iipsc | circumplex | Inventory of Interpersonal Problems, Short Circumplex | circumplex_instrument | | |
| iis32 | circumplex | Inventory of Interpersonal Strengths, Brief Version | circumplex_instrument | | |
| iis64 | circumplex | Inventory of Interpersonal Strengths | circumplex_instrument | | |
| iitc | circumplex | Inventory of Influence Tactics Circumplex | circumplex_instrument | | |
| ipipipc | circumplex | IPIP Interpersonal Circumplex | circumplex_instrument | | |
| isc | circumplex | Interpersonal Sensitivities Circumplex | circumplex_instrument | | |
| jz2017 | circumplex | Raw octant scores on real circumplex scales with covariates | data.frame | 1166 | 19 |
| raw_iipsc | circumplex | Raw item responses on real circumplex scales | data.frame | 10 | 32 |
| simulated_items | circumplex | Simulated item responses on octant circumplex scales | data.frame | 500 | 32 |
| us_fiscal_cond_forecasts | bsvars | A matrix to be used in a conditional forecasting example including the projected values of total tax revenue that are projected to increase at an average quarterly sample growth rate. The other two columns are filled with 'NA' values, which implies that the future values of the corresponding endogenous variables, namely government spending and GDP, will be forecasted given the provided projected values of total tax revenue. The matrix includes future values for the forecast horizon of two years for the US fiscal model for the period 2024 Q3 - 2026 Q1. | mts | 8 | 3 |
| us_fiscal_ex | bsvars | A 3-variable system of exogenous variables for the US fiscal model for the period 1948 Q1 - 2026 Q1 | mts | 313 | 3 |
| us_fiscal_ex_forecasts | bsvars | A 3-variable system of exogenous variables' future values for the forecast horizon of two years for the US fiscal model for the period 2026 Q2 - 2028 Q1 | mts | 8 | 3 |
| us_fiscal_lsuw | bsvars | A 3-variable US fiscal system for the period 1948 Q1 - 2026 Q1 | mts | 313 | 3 |
| us_fiscal_sww | bsvars | A 10-variable US fiscal system for the period 1948 Q1 - 2025 Q3 | mts | 266 | 10 |
| epic_v1_manifest | RnBeads | XenoMix EPICv1 (hg38) | data.frame | 142137 | 5 |
| epic_v2_manifest | RnBeads | XenoMix EPICv2 (hg38) | data.frame | 126967 | 8 |
| interspecies_probes_v1 | RnBeads | XenoMix interspeciesprobes (v1) | character | | |
| interspecies_probes_v2 | RnBeads | XenoMix interspeciesprobes (v2) | character | | |
| lump.hg19 | RnBeads | LUMP Support | list | | |
| lump.hg38 | RnBeads | LUMP Support (hg38) | list | | |
| argo_float | ngme2 | Argo float dataset | data.frame | 274 | 4 |
| cienaga | ngme2 | The swamp of Cienaga Grande in Santa Marta, Colombia | tbl_df | 228 | 6 |
| cienaga.border | ngme2 | The x y location of the border of the swamp of Cienaga Grande in Santa Marta, Colombia | data.frame | 472 | 2 |
| pm25_quarterly_2022 | ngme2 | | data.frame | 2640 | 9 |
| data_MVA | dendroTools | Mean vessel area example proxy from 2012 - 1940 | data.frame | 73 | 1 |
| data_TRW | dendroTools | Tree-ring width (TRW) example proxy from 1981 - 1757 | data.frame | 225 | 1 |
| data_TRW_1 | dendroTools | Tree-ring width (TRW) data from 2012 - 1961 | data.frame | 52 | 1 |
| dataset_MVA | dendroTools | MVA and mean April temperature | data.frame | 79 | 2 |
| dataset_MVA_individual | dendroTools | Example of dataset with individual chronologies of MVA and mean April temperature | data.frame | 56 | 11 |
| dataset_TRW | dendroTools | TRW and mean June - July temperature from Albania | data.frame | 59 | 2 |
| dataset_TRW_complete | dendroTools | The complete dataset of standardized tree-ring chronology from Albania | data.frame | 551 | 1 |
| example_dataset_1 | dendroTools | Example of dataset as required for compare_methods() | data.frame | 58 | 3 |
| example_proxies_1 | dendroTools | Tree-ring example proxies 1 from 2015 - 1961 | data.frame | 55 | 3 |
| example_proxies_individual | dendroTools | Example of dataset with individual chronologies of MVA. | data.frame | 56 | 10 |
| KRE_daily_temperatures | dendroTools | Daily mean temperatures for Kredarica (Alps in Slovenia) from 2017 - 1955 | data.frame | 63 | 366 |
| LJ_daily_precipitation | dendroTools | Daily precipitation for Ljubljana from 2017 - 1900 | data.frame | 43067 | 3 |
| LJ_daily_temperatures | dendroTools | Daily mean temperatures for Ljubljana from 2016 - 1930 | data.frame | 87 | 366 |
| LJ_monthly_precipitation | dendroTools | Monthly sums of precipitation for Ljubljana from 2018 - 1900. Tidy format. | data.frame | 1417 | 3 |
| LJ_monthly_temperatures | dendroTools | Monthly mean air temperatures for Ljubljana from 2015 - 1900 | data.frame | 116 | 12 |
| swit272 | dendroTools | Standardised tree-ring width chronology swit272, Larix decidua Mill. | crn | 273 | 1 |
| swit272_daily_precipitation | dendroTools | Daily precipitation for swit272 chronology | data.frame | 25414 | 2 |
| swit272_daily_temperatures | dendroTools | Daily temperatures for swit272 chronology | data.frame | 25414 | 2 |
| fluentPeople | shiny.fluent | A dataset of sample people based on Fluent UI examples | tbl_df | 7 | 11 |
| fluentSalesDeals | shiny.fluent | A randomly generated dataset of imaginary sales deals | tbl_df | 100 | 30 |
| tamoxifen | CoxAalenCR | Tamoxifen Breast Cancer Competing Risks Clinical Trial Dataset | data.frame | 641 | 5 |
| BTflow | exdqlm | Monthly streamflow at the Big Trees gauge | ts | | |
| climateIndices | exdqlm | Monthly climate indices for streamflow examples | data.frame | 516 | 3 |
| ELIanoms | exdqlm | Daily time-series of ELI anomalies. | ts | | |
| scIVTmag | exdqlm | Time series of daily average magnitude IVT in Santa Cruz, CA. | ts | | |
| covid_deaths | TieFreeCensor | COVID-19 Daily Death Counts Dataset | numeric | | |
| spinal_tumors | TieFreeCensor | Spinal Tumors Count Dataset in Neurofibromatosis Type 2 (NF2) Patients | data.frame | 22 | 2 |
| lalonde_data | AugBalWeight | LaLonde (1986) Job Training Demonstration Dataset | data.frame | 445 | 12 |
| electro_explosive | OneShotEM | Electro-Explosive Device Accelerated Life Test Data | data.frame | 9 | 4 |
| amltest | hdcuremodels | AML test data | data.frame | 40 | 322 |
| amltrain | hdcuremodels | AML training data | data.frame | 306 | 322 |
| pediatric_flt3 | hdcuremodels | Pediatric acute myeloid leukemia patients with FLT3-ITD rearrangement data | data.frame | 246 | 202 |
| population_us | msSPChelpR | US Populations Data | data.frame | 4320 | 7 |
| standard_population | msSPChelpR | Standard Populations Data | data.frame | 246 | 5 |
| us_refrates_icd2 | msSPChelpR | US Reference Rates for Cancer Data (ICD-O 2digit code) | tbl_df | 41472 | 11 |
| us_second_cancer | msSPChelpR | US Second Cancer Data | tbl_df | 113999 | 16 |
| CucurbitaData | GapAnalysis | Cucurbita occurrences dataset | data.frame | 1203 | 4 |
| CucurbitaRasts | GapAnalysis | Cucurbita species distribution models dataset | PackedSpatRaster | | |
| ecoregions | GapAnalysis | Ecoregions vector | PackedSpatVector | | |
| ProtectedAreas | GapAnalysis | Protected areas dataset in raster format | PackedSpatRaster | | |
| adam_spec | artoo | Bundled CDISC specifications (ADaM and SDTM) | artoo::artoo_spec | | |
| cdisc_adae | artoo | Demo adverse events analysis dataset (ADaM ADAE) | data.frame | 60 | 55 |
| cdisc_adam_datasets | artoo | CDISC demo specification tables (one standard per pair) | data.frame | 1 | 3 |
| cdisc_adam_variables | artoo | CDISC demo specification tables (one standard per pair) | data.frame | 48 | 7 |
| cdisc_adsl | artoo | Demo subject-level analysis dataset (ADaM ADSL) | data.frame | 60 | 48 |
| cdisc_codelists | artoo | CDISC demo specification tables (one standard per pair) | data.frame | 4 | 4 |
| cdisc_dm | artoo | Demo demographics dataset (SDTM DM) | data.frame | 60 | 25 |
| cdisc_sdtm_datasets | artoo | CDISC demo specification tables (one standard per pair) | data.frame | 1 | 3 |
| cdisc_sdtm_variables | artoo | CDISC demo specification tables (one standard per pair) | data.frame | 25 | 7 |
| cdisc_suppdm | artoo | Demo supplemental qualifiers dataset (SDTM SUPPDM) | data.frame | 60 | 10 |
| cdisc_ts | artoo | Demo trial summary dataset (SDTM TS) | data.frame | 33 | 6 |
| cdisc_vs | artoo | Demo vital signs dataset (SDTM VS) | data.frame | 60 | 24 |
| sdtm_spec | artoo | Bundled CDISC specifications (ADaM and SDTM) | artoo::artoo_spec | | |
| geese_data | simmr | Geese stable isotope mixing data set | list | | |
| geese_data_day1 | simmr | A smaller version of the Geese stable isotope mixing data set | list | | |
| simmr_data_1 | simmr | A simple fake stable isotope mixing data set | list | | |
| simmr_data_2 | simmr | A 3-isotope fake stable isotope mixing data set | list | | |
| square_data | simmr | An artificial data set used to indicate effect of priors | list | | |
| cca.example | corclass | Sample data for Correlational Class Analysis. | matrix | 753 | |
| BRCA_genes | miRSM | BRCA genes | SummarizedExperiment | | |
| ceRExp | miRSM | ceRNA expression data | SummarizedExperiment | | |
| miRExp | miRSM | miRNA expression data | SummarizedExperiment | | |
| miRTarget | miRSM | miRNA-target ineractions | SummarizedExperiment | | |
| mRExp | miRSM | mRNA expression data | SummarizedExperiment | | |
| .Random.seed | FLBEIA | | integer | | |
| advice | FLBEIA | | list | | |
| advice.ctrl.fixed | FLBEIA | | list | | |
| advice.ctrl.ices | FLBEIA | | list | | |
| advice.ctrl.ices_lo | FLBEIA | | list | | |
| advice.ctrl.msmsy_lo | FLBEIA | | list | | |
| advice.default | FLBEIA | | list | | |
| advice.msmsy | FLBEIA | | list | | |
| advice.steps | FLBEIA | | list | | |
| assess.ctrl | FLBEIA | | list | | |
| biols | FLBEIA | | FLBiols | | |
| biols.ctrl | FLBEIA | | list | | |
| catch | FLBEIA | FLBEIA datasets | data.frame | 40 | 8 |
| covars | FLBEIA | | list | | |
| covars.ctrl | FLBEIA | | list | | |
| evhoe | FLBEIA | FLBEIA datasets | data.frame | 18 | 4 |
| fleets | FLBEIA | | list | | |
| fleets.ctrl.mpro | FLBEIA | | list | | |
| fleets.ctrl.mpro_lo | FLBEIA | | list | | |
| fleets.ctrl.trad | FLBEIA | | list | | |
| fleets.ctrl.trad_lo | FLBEIA | | list | | |
| main.ctrl | FLBEIA | | list | | |
| multiAdv | FLBEIA | FLBEIA datasets | list | | |
| multiAdvC | FLBEIA | FLBEIA datasets | list | | |
| multiAssC | FLBEIA | FLBEIA datasets | list | | |
| multiBD | FLBEIA | FLBEIA datasets | list | | |
| multiBio | FLBEIA | FLBEIA datasets | FLBiols | | |
| multiBioC | FLBEIA | FLBEIA datasets | list | | |
| multiCv | FLBEIA | FLBEIA datasets | list | | |
| multiCvC | FLBEIA | FLBEIA datasets | list | | |
| multiFl | FLBEIA | FLBEIA datasets | FLFleetsExt | | |
| multiFlC | FLBEIA | FLBEIA datasets | list | | |
| multiMainC | FLBEIA | FLBEIA datasets | list | | |
| multiObsC | FLBEIA | FLBEIA datasets | list | | |
| multiRes | FLBEIA | FLBEIA datasets | list | | |
| multiSR | FLBEIA | FLBEIA datasets | list | | |
| multistk | FLBEIA | | FLStock | | |
| obs.ctrl | FLBEIA | | list | | |
| oneAdv | FLBEIA | FLBEIA datasets | list | | |
| oneAdvC | FLBEIA | FLBEIA datasets | list | | |
| oneAssC | FLBEIA | FLBEIA datasets | list | | |
| oneBio | FLBEIA | FLBEIA datasets | FLBiols | | |
| oneBioC | FLBEIA | FLBEIA datasets | list | | |
| oneCv | FLBEIA | FLBEIA datasets | list | | |
| oneCvC | FLBEIA | FLBEIA datasets | list | | |
| oneFl | FLBEIA | FLBEIA datasets | FLFleetsExt | | |
| oneFlC | FLBEIA | FLBEIA datasets | list | | |
| oneIndAge | FLBEIA | FLBEIA datasets | list | | |
| oneIndBio | FLBEIA | FLBEIA datasets | list | | |
| oneItAdv | FLBEIA | FLBEIA datasets | list | | |
| oneItAdvC | FLBEIA | FLBEIA datasets | list | | |
| oneItAssC | FLBEIA | FLBEIA datasets | list | | |
| oneItBio | FLBEIA | FLBEIA datasets | FLBiols | | |
| oneItBioC | FLBEIA | FLBEIA datasets | list | | |
| oneItCv | FLBEIA | FLBEIA datasets | list | | |
| oneItCvC | FLBEIA | FLBEIA datasets | list | | |
| oneItFl | FLBEIA | FLBEIA datasets | FLFleetsExt | | |
| oneItFlC | FLBEIA | FLBEIA datasets | list | | |
| oneItIndAge | FLBEIA | FLBEIA datasets | list | | |
| oneItIndBio | FLBEIA | FLBEIA datasets | list | | |
| oneItMainC | FLBEIA | FLBEIA datasets | list | | |
| oneItObsC | FLBEIA | FLBEIA datasets | list | | |
| oneItObsCIndAge | FLBEIA | FLBEIA datasets | list | | |
| oneItObsCIndBio | FLBEIA | FLBEIA datasets | list | | |
| oneItRes | FLBEIA | FLBEIA datasets | list | | |
| oneItSR | FLBEIA | FLBEIA datasets | list | | |
| oneMainC | FLBEIA | FLBEIA datasets | list | | |
| oneObsC | FLBEIA | FLBEIA datasets | list | | |
| oneObsCIndAge | FLBEIA | FLBEIA datasets | list | | |
| oneObsCIndBio | FLBEIA | FLBEIA datasets | list | | |
| oneRes | FLBEIA | FLBEIA datasets | list | | |
| oneSR | FLBEIA | FLBEIA datasets | list | | |
| SRs | FLBEIA | | list | | |
| ir_sample_data | ir | Sample object of class 'ir' | ir | 58 | 7 |
| castle | did2s | Data from Cheng and Hoekstra (2013) | data.frame | 550 | 7 |
| df_het | did2s | Simulated data with two treatment groups and heterogenous effects | data.frame | 46500 | 14 |
| df_hom | did2s | Simulated data with two treatment groups and homogenous effects | data.frame | 46500 | 15 |
| df | moveVis | White Stork LifeTrack tracks | data.frame | 155173 | 5 |
| m | moveVis | White Stork LifeTrack tracks | move2 | 155173 | 3 |
| move_data | moveVis | Example simulated movement tracks | move2 | 458 | 3 |
| cluster52 | GoMiner | GoMiner data set | character | | |
| GOGOA3small | GoMiner | GoMiner data set | list | | |
| HCCS66 | GoMiner | GoMiner data set | character | | |
| Housekeeping_Genes | GoMiner | GoMiner data set | data.frame | 2833 | 4 |
| x_fdr | GoMiner | GoMiner data set | list | | |
| x_hyper1 | GoMiner | GoMiner data set | numeric | | |
| x_m | GoMiner | GoMiner data set | matrix | 802 | 3 |
| x_sampleList1 | GoMiner | GoMiner data set | character | | |
| x_tablePop3 | GoMiner | GoMiner data set | list | | |
| x_tablePop31 | GoMiner | GoMiner data set | list | | |
| x_tableSample3 | GoMiner | GoMiner data set | list | | |
| x_thresh | GoMiner | GoMiner data set | data.frame | 18 | 6 |
| AcceptableRange | DspikeIn | Acceptable Range Data | data.frame | 113631 | 10 |
| metadata_full | DspikeIn | Metadata for Microbiome Samples | data.frame | 312 | 46 |
| physeq | DspikeIn | Example Phyloseq Object with Tree and Reference Sequences | phyloseq | | |
| physeq_16SOTU | DspikeIn | Example Phyloseq Object for 16S OTUs | phyloseq | | |
| physeq_ITSOTU | DspikeIn | Example Phyloseq Object for ITS OTUs | phyloseq | | |
| tse | DspikeIn | Example TreeSummarizedExperiment (TSE) Object with Tree and Reference Sequences | TreeSummarizedExperiment | | |
| bugs_long | statsExpressions | Tidy version of the "Bugs" dataset. | tbl_df | 372 | 6 |
| iris_long | statsExpressions | Edgar Anderson's Iris Data in long format. | tbl_df | 600 | 6 |
| movies_long | statsExpressions | Movie information and user ratings from IMDB. | tbl_df | 1579 | 8 |
| profitSWG | ceRtainty | Profit SWG | data.frame | 8 | 4 |
| DepressionDemo | glmertree | Artificial depression treatment dataset | data.frame | 150 | 7 |
| GrowthCurveDemo | glmertree | Artificial dataset for partitioning of linear growth curve models | data.frame | 1250 | 11 |
| MHserviceDemo | glmertree | Artificial mental-health service outcomes dataset | data.frame | 350 | 8 |
| HarzTraffic | gamlss2 | Traffic Counts at Sonnenberg in the Harz Region | data.frame | 1057 | 16 |
| SpirometryUS | gamlss2 | Spirometry Measurements from NHANES 2007-2012 | data.frame | 16596 | 13 |
| centroidi | BioIndex | centroidi | PackedSpatVector | | |
| cgpmgrid | BioIndex | cgpmgrid | PackedSpatVector | | |
| continent | BioIndex | continent | PackedSpatVector | | |
| med_bathy | BioIndex | Mediterranean and Black Sea bathymetry (0–1000 m, bathy object) | bathy | 2867 | 1020 |
| strata_scheme | BioIndex | stratification scheme | data.frame | 126 | 5 |
| strata_scheme_rapana | BioIndex | stratification scheme (rapa whelk) | data.frame | 7 | 5 |
| stratification | BioIndex | stratification | data.frame | 276 | 6 |
| stratification_rapana | BioIndex | stratification (rapa whelk) | data.frame | 7 | 6 |
| stratum_0_125 | BioIndex | stratum_0_125 | PackedSpatVector | | |
| stratum_0_200 | BioIndex | stratum_0_200 | PackedSpatVector | | |
| stratum_0_35 | BioIndex | stratum_0_35 | PackedSpatVector | | |
| stratum_0_45 | BioIndex | stratum_0_45 | PackedSpatVector | | |
| stratum_0_800 | BioIndex | stratum_0_800 | PackedSpatVector | | |
| stratum_200_800 | BioIndex | stratum_200_800 | PackedSpatVector | | |
| TA | BioIndex | TA table example | data.frame | 100 | 43 |
| TA_cols | BioIndex | TA table headings | character | | |
| TB | BioIndex | TB table example | data.frame | 3059 | 19 |
| TB_cols | BioIndex | TB table headings | character | | |
| TC | BioIndex | TC table example | data.frame | 11185 | 22 |
| TC_cols | BioIndex | TC table headings | character | | |
| codebreak200 | tipse | Patient level data from dummy trial | data.frame | 345 | 6 |
| extenet | tipse | Patient level data from dummy trial | data.frame | 2840 | 6 |
| acb_age_profile_data_2526 | BAwiR | ACB age profile, 2025-2026, Valencia Basket | tbl_df | 15 | 7 |
| acb_combs | BAwiR | Lineups in a given game. | data.frame | 150 | 38 |
| acb_games_1718 | BAwiR | ACB games 2017-2018 | tbl_df | 3939 | 38 |
| acb_games_2223_coach | BAwiR | ACB coaches in the 2022-2023 season. | tbl_df | 612 | 4 |
| acb_games_2223_info | BAwiR | ACB games 2022-2023, days and codes. | tbl_df | 306 | 3 |
| acb_players_1718 | BAwiR | ACB players 2017-2018 | tbl_df | 255 | 7 |
| acb_players_2425 | BAwiR | ACB players 2024-2025 | tbl_df | 30 | 5 |
| acb_shields | BAwiR | Shields of the ACB teams | tbl_df | 20 | 2 |
| acb_shooting_data_2425 | BAwiR | ACB shooting data, 2024-2025 | tbl_df | 4277 | 23 |
| acb_sticker_data_2526 | BAwiR | ACB sticker data, 2025-2026 | tbl_df | 11 | 9 |
| acb_usage_act_data_2526 | BAwiR | ACB usage action data, 2025-2026 | data.frame | 75 | 8 |
| acb_usage_data_2526 | BAwiR | ACB usage data, 2025-2026 | data.frame | 62 | 9 |
| acb_vbc_cz_pbp_2223 | BAwiR | ACB play-by-play data, 2022-2023, Valencia Basket-Casademont Zaragoza | tbl_df | 466 | 9 |
| acb_vbc_cz_sl_2223 | BAwiR | ACB starting lineups, 2022-2023, Valencia Basket-Casademont Zaragoza | tbl_df | 40 | 9 |
| eurocup_games_1718 | BAwiR | Eurocup games 2017-2018 | tbl_df | 3604 | 38 |
| eurocup_players_1718 | BAwiR | Eurocup players 2017-2018 | tbl_df | 351 | 7 |
| euroleague_games_1718 | BAwiR | Euroleague games 2017-2018 | tbl_df | 3932 | 38 |
| euroleague_players_1718 | BAwiR | Euroleague players 2017-2018 | tbl_df | 245 | 7 |
| metrics_player_zone | BAwiR | ACB players 2024-2025 | tbl_df | 8 | 5 |
| assTL | RoME | TL association between categories and sub-categories | data.frame | 74 | 2 |
| classes | RoME | Class of fields | data.frame | 123 | 4 |
| DataTargetSpecies | RoME | Length and weight ranges for relevant species | data.frame | 1318 | 24 |
| GSAs | RoME | List of GFCM Geographical subareas (GSAs) | spec_tbl_df | 31 | 3 |
| list_g1_g2 | RoME | List of G1 and G2 species | data.frame | 88 | 17 |
| LW | RoME | Table of the Length-Weight parameters | data.frame | 476 | 5 |
| mat_stages | RoME | Table of maturity stages | data.frame | 190 | 6 |
| Maturity_parameters | RoME | Maturity parameters | data.frame | 182 | 12 |
| MedSea | RoME | Shapefile of Mediterranean and Black Sea area | SpatialPolygonsDataFrame | | |
| stratification_scheme | RoME | stratification_scheme | data.frame | 474 | 7 |
| stratification_scheme_rapana | RoME | stratification_scheme_rapana | data.frame | 7 | 7 |
| TA | RoME | TA | data.frame | 100 | 43 |
| TB | RoME | TB | data.frame | 3059 | 19 |
| TC | RoME | TC | data.frame | 11185 | 22 |
| TE | RoME | TE | data.frame | 3801 | 25 |
| templateTA | RoME | Template haul data table (TA). | data.frame | | 43 |
| templateTB | RoME | Template catch data table (TB). | data.frame | | 19 |
| templateTC | RoME | Template biological data table (TC). | data.frame | | 22 |
| templateTE | RoME | Template individual data table (TE). | data.frame | | 25 |
| templateTL | RoME | TL table template | data.frame | | 14 |
| time | RoME | allowed values for SHOOTING_TIME and HAULING_TIME | integer | | |
| TL | RoME | TL | data.frame | 177 | 14 |
| TM_list | RoME | TM list | data.frame | 1617 | 11 |
| metaMatMetformin | metadeconfoundR | Documentation for the metaMatMetformin RData in /data | data.frame | 753 | 5 |
| reduced_feature | metadeconfoundR | Documentation for the reduced_feature RData in /data | data.frame | 753 | 50 |
| Gilroyetal2015 | ggsced | Plotting data from Gilroy et al. (2015) | data.frame | 40 | 6 |
| Gilroyetal2021 | ggsced | Plotting data from Gilroy et al. (2021) | data.frame | 69 | 7 |
| LozyEtAl2020Data | ggsced | Plotting data from Lozy et al. (2020) | tbl_df | 182 | 5 |
| BDIkey | KernSmoothIRT | The Beck Depression Inventory Data | numeric | | |
| BDIkey | KernSmoothIRT | The Beck Depression Inventory Data | numeric | | |
| BDIresponses | KernSmoothIRT | The Beck Depression Inventory Data | matrix | 242 | 21 |
| BDIresponses | KernSmoothIRT | The Beck Depression Inventory Data | matrix | 242 | 21 |
| HIV | KernSmoothIRT | The HIV Data | data.frame | 4282 | 18 |
| HIV | KernSmoothIRT | The HIV Data | data.frame | 4282 | 18 |
| HIVkey | KernSmoothIRT | The HIV Data | numeric | | |
| HIVkey | KernSmoothIRT | The HIV Data | numeric | | |
| Psychkey | KernSmoothIRT | The Introductory Psychology Data | numeric | | |
| Psychkey | KernSmoothIRT | The Introductory Psychology Data | numeric | | |
| Psychresponses | KernSmoothIRT | The Introductory Psychology Data | matrix | 379 | 100 |
| Psychresponses | KernSmoothIRT | The Introductory Psychology Data | matrix | 379 | 100 |
| rbmi_test_data | tern.rbmi | Example dataset for 'tern.rbmi' package. This is an pool object from the rbmi analysis, see 'browseVignettes(package = "tern.rbmi")' | pool | | |
| JSON_sample | vDiveR | DiMA (v5.0.9) JSON Output File | list | | |
| metadata | vDiveR | Metadata Input Sample | data.frame | 500 | 3 |
| protein_2hosts | vDiveR | DiMA (v5.0.9) JSON converted-CSV Output Sample 2 | data.frame | 200 | 17 |
| proteins_1host | vDiveR | DiMA (v5.0.9) JSON converted-CSV Output Sample 1 | data.frame | 300 | 17 |
| model.output | RLumModel | Example data (TL curve) simulated with parameter set from Pagonis 2007 | RLum.Analysis | | |
| snooker_events | comperank | Snooker events | tbl_df | 79 | 10 |
| snooker_matches | comperank | Snooker matches | tbl_df | 5192 | 14 |
| snooker_players | comperank | Snooker players | tbl_df | 487 | 6 |
| cprd | doseminer | Sample electronic prescribing dataset | data.frame | 714 | 6 |
| Bdists | GOstats | Distance matrices for the BCR/ABL and NEG subgroups. | matrix | 206 | 206 |
| Ndists | GOstats | Distance matrices for the BCR/ABL and NEG subgroups. | matrix | 206 | 206 |
| chr1_genes | tadar | Genomic feature example data | GRanges | | |
| chr1_tt | tadar | Differential expression example data | tbl_df | 716 | 5 |
| simulPU | PUlasso | simulated PU data | list | | |
| classAbbreviations | birdscanR | Default class abbreviations table of the birdscanR package | data.frame | 9 | 2 |
| manualBlindTimes | birdscanR | Example file on how to include manual blind times for your 'Birdscan MR1' database. | data.frame | 3 | 3 |
| diabetes | gaawr2 | Diabetes Dataset | data.frame | 1000 | 14 |
| DiaHealth | gaawr2 | DiaHealth | data.frame | 5437 | 15 |
| cma | RTL | metadata for WTI CMA | tbl_df | 192 | 7 |
| crudeOil | RTL | dataset: crude assays | list | | |
| cushing | RTL | dataset: WTI Cushing Futures and storage utilization | list | | |
| dflong | RTL | dataset: commodity prices in a long dataframe format | tbl_df | 556557 | 3 |
| dfwide | RTL | dataset: commodity prices in a wide dataframe format | tbl_df | 4883 | 121 |
| eiaStocks | RTL | dataset: EIA weekly stocks | grouped_df | 5061 | 3 |
| eiaStorageCap | RTL | dataset: EIA working storage capacity | tbl_df | 367 | 4 |
| expiry_table | RTL | dataset: expiry of common commodity futures contract. | tbl_df | 3272 | 9 |
| fizdiffs | RTL | dataset: randomised physical crude differentials | tbl_df | 1605 | 23 |
| futuresRef | RTL | dataset: futures contracts metadata | list | | |
| holidaysOil | RTL | dataset: NYMEX and ICE holiday calendars | tbl_df | 209 | 2 |
| ohlc | RTL | dataset: randomiser to convert settlement into OHLC | tbl_df | 2733 | 5 |
| planets | RTL | dataset: IR compounding | tbl_df | 10 | 18 |
| refineryLPdata | RTL | dataset: refinery LP model sample inputs and outputs | list | | |
| spot2futConvergence | RTL | dataset: spot to futures convergence | tbl_df | 1041 | 3 |
| spot2futCurve | RTL | dataset: spot to futures convergence curve | tbl_df | 13 | 4 |
| steo | RTL | dataset: EIA Short Term Energy Outlook | plotly | | |
| stocks | RTL | dataset: Yahoo Finance data sets | list | | |
| tickers_eia | RTL | datasest: metadata of key EIA tickers grouped by products. | data.frame | 197 | 5 |
| tradeCycle | RTL | dataset: Canadian and US physical crude trading calendars | tbl_df | 317 | 8 |
| tradeHubs | RTL | dataset: GIS locations for crude oil trading hubs | tbl_df | 6 | 3 |
| tsQuotes | RTL | dataset: interest rate curve data for RQuantlib . | list | | |
| usSwapCurves | RTL | dataset: US bootstrapped interest rate curve. | DiscountCurve | | |
| usSwapCurvesPar | RTL | dataset: US bootstrapped interest rate curve parallel sample. | DiscountCurve | | |
| wtiSwap | RTL | dataset: WTI Calendar Month Average Swap pricing data | tbl_df | 556557 | 3 |
| example_ame | memes | Example runAme() output | list | | |
| example_ame_large | memes | runAme() output for example_chip_summits split by binding description | list | | |
| example_chip_summits | memes | Annotated Transcription Factor ChIP-seq summits | GRanges | | |
| example_dreme | memes | Example runDreme() output | universalmotif_df | 2 | 26 |
| example_dreme_by_binding | memes | runDreme() output for example_chip_summits split by binding description | list | | |
| example_dreme_tomtom | memes | Example runDreme() output after passing to runTomTom() | universalmotif_df | 2 | 36 |
| example_fimo | memes | Example runFimo() output | GRanges | | |
| example_peaks | memes | Example ChIP-seq peaks | GRanges | | |
| example_rnaseq | memes | RNAseq data from Early and Late Drosophila wings | grouped_df | 744 | 3 |
| example_tomtom | memes | Example runTomTom() output | universalmotif_df | 1 | 26 |
| DCtracts2020 | ndi | Formatted U.S. Census American Community Survey 5-year estimate data for DC census tracts (2020) from the 'tidycensus' package | tbl_df | 206 | 23 |
| grid_state_correspondence_table | geobr | A correspondence table indicating what quadrants of IBGE's statistical grid intersect with each Brazilian state | data.frame | 139 | 3 |
| bulk_test_example | METAFLUX | Bulk test example | data.frame | 58581 | 5 |
| cell_medium | METAFLUX | This file contains the hams medium nutrients information | data.table | 44 | 2 |
| human_blood | METAFLUX | This file contains the hams medium nutrients information in human blood | data.frame | 64 | 2 |
| human_gem | METAFLUX | Human-GEM Metabolic Model Data | tbl_df | 13082 | 16 |
| nutrient_lookup_files | METAFLUX | 1648 exchange reactions:mathematical representation of uptake/secrete metabolites into the extracellular space | tbl_df | 1648 | 3 |
| sc_test_example | METAFLUX | single cell test seurat object example | Seurat | | |
| amt_fisher | amt | GPS tracks from four fishers | track_xyt | 14230 | 6 |
| amt_fisher_covar | amt | Environmental data for fishers | list | | |
| deer | amt | Relocations of 1 red deer | track_xyt | 826 | 4 |
| sh | amt | Relocations of one Red Deer | data.frame | 1500 | 4 |
| sh_forest | amt | Forest cover | PackedSpatRaster | | |
| uhc_hab | amt | Simulated habitat rasters for demonstrating UHC plots | data.frame | 6400 | 9 |
| uhc_hsf_locs | amt | Simulated HSF location data for demonstrating UHC plots | data.frame | 1894 | 2 |
| uhc_issf_locs | amt | Simulated iSSF location data for demonstrating UHC plots | data.frame | 2135 | 3 |
| congCombined | krige | Congressional District Public Opinion Ideology in 2010 | data.frame | 442 | 12 |
| ContrivedData | krige | Contrived Example Data | data.frame | 500 | 5 |
| lowerCombined | krige | State Legislative District (Lower Chambers) Public Opinion Ideology in 2010 | data.frame | 5446 | 10 |
| NY_subset | krige | New York State CCES Respondents in 2008 | data.frame | 1108 | 26 |
| NYcity_subset | krige | New York City CCES Respondents in 2008 | data.frame | 568 | 26 |
| stateCombined | krige | State Public Opinion Ideology in 2010 | data.frame | 100 | 13 |
| upperCombined | krige | State Legislative District (Upper Chambers) Public Opinion Ideology in 2010 | data.frame | 1989 | 10 |
| WVwells | krige | West Virginia Oil and Gas Production in 2012 | data.frame | 1949 | 18 |
| dates | aion | Sample Data from Reingold and Dershowitz (2018) | data.frame | 33 | 14 |
| acep_bases | ACEP | Coleccion de notas y recursos de prueba | list | | |
| acep_diccionarios | ACEP | Colección de diccionarios. | list | | |
| acep_prompt_gpt | ACEP | Colección de instrucciones para GPT. | list | | |
| acep_rs | ACEP | Cadenas de caracteres para limpiar y normalizar textos. | list | | |
| DGIdb_interactions_df | PANACEA | DGIdb Interactions Expert-curated Sources | data.frame | 11323 | 2 |
| example_driveR_res | PANACEA | Example driveR Result | data.frame | 106 | 3 |
| example_scores_dist | PANACEA | Example PANACEA "distance-based" Method Result | numeric | | |
| example_scores_RWR | PANACEA | Example PANACEA "RWR" Method Result | numeric | | |
| STRING_adj_df | PANACEA | Adjacency List for STRING v11.5 - High Confidence Interactions | data.frame | 249816 | 3 |
| toy_W_mat | PANACEA | Toy Adjacency Matrix (for examples) | matrix | 84 | 84 |
| banana_2dEx | levelSets | Negative of Rosenbrock's Banana Function in Two Dimensions | list | | |
| circuitFailure_3dEx | levelSets | Example of a 3D Confidence Region as a Level Set | list | | |
| dbl_ellipse_2dEx | levelSets | Example of a Multi-Part Level Set in a 2D Input Space | list | | |
| dbl_ellipse_X1bnd_2dEx | levelSets | Example of a Multi-Part Level Set in a 2D Input Space | list | | |
| movie | vcrpart | Movie critics | data.frame | 372 | 3 |
| PL | vcrpart | Effect of parental leave policy | data.frame | 6180 | 24 |
| poverty | vcrpart | Poverty in Switzerland | data.frame | 576 | 16 |
| schizo | vcrpart | National Institute of Mental Health shizophrenia study | data.frame | 1603 | 5 |
| unemp | vcrpart | Synthetic data sets | data.frame | 200 | 8 |
| vcrpart_1 | vcrpart | Synthetic data sets | data.frame | 200 | 8 |
| vcrpart_2 | vcrpart | Synthetic data sets | data.frame | 100 | 5 |
| vcrpart_3 | vcrpart | Synthetic data sets | data.frame | 100 | 3 |
| ksarakil | ananke | Ksâr 'Akil Radiocarbon Dates | data.frame | 16 | 5 |
| Data_Maize | decompML | Monthly International Maize Price | ts | 252 | 1 |
| majors | ggalluvial | Students' declared majors across several semesters | data.frame | 80 | 3 |
| vaccinations | ggalluvial | Influenza vaccination survey responses | data.frame | 117 | 6 |
| data_dictionary | r2dii.plot | Data Dictionary | tbl_df | 32 | 4 |
| market_share_demo | r2dii.plot | An example of a 'market_share_demo'-like dataset | spec_tbl_df | 802 | 10 |
| palette_colours | r2dii.plot | Colour datasets | tbl_df | 9 | 2 |
| scenario_colours | r2dii.plot | Colour datasets | tbl_df | 5 | 2 |
| sda_demo | r2dii.plot | An example of an 'sda_demo'-like dataset | spec_tbl_df | 110 | 6 |
| sector_colours | r2dii.plot | Colour datasets | tbl_df | 8 | 2 |
| technology_colours | r2dii.plot | Colour datasets | tbl_df | 18 | 3 |
| barnacle5 | clustcurv | Barnacle data | data.frame | 5000 | 3 |
| C19dNUTSdata | swash | Regional cumulative COVID-19 deaths | tbl_df | 1143 | 10 |
| COVID19Cases_geoRegion | swash | Switzerland Daily COVID-19 cases by region | spec_tbl_df | 30247 | 36 |
| did_fatalities_splm_coef | swash | Results from a Difference-in-Differences Model | tbl_df | 30 | 7 |
| Infections | swash | Infections | data.frame | 107 | 35 |
| Oesterreich_Faelle | swash | Austria Daily COVID-19 cases by region 2020-02-26 to 2020-05-31 | data.frame | 3360 | 3 |
| RKI_Corona_counties | swash | German Counties with COVID-19 Cases | sf | 412 | 38 |
| hospiceALPHA | medicare | Sample Medicare Hospice Cost Report 2014 data | data.frame | 61820 | 5 |
| hospiceNMRC | medicare | Sample Medicare Hospice Cost Report 2014 data | data.frame | 200202 | 5 |
| hospiceRPT | medicare | Sample Medicare Hospice Cost Report 2014 data | data.frame | 500 | 18 |
| pos2010 | medicare | Sample Medicare Provider of Service 2010 data for hospices | data.frame | 402 | 530 |
| CRD1 | TukeyC | Completely Randomized Design (CRD) | list | | |
| CRD2 | TukeyC | Completely Randomized Design ('CRD') | list | | |
| FE | TukeyC | Factorial Experiment (FE) | list | | |
| LSD | TukeyC | Latin Squares Design (LSD) | list | | |
| RCBD | TukeyC | Randomized Complete Block Design (RCBD) | list | | |
| sorghum | TukeyC | Completely Randomized Design (CRD) | list | | |
| SPE | TukeyC | Split-plot Experiment (SPE) | list | | |
| SPET | TukeyC | Split-plot Experiment in Time (SPET) | list | | |
| SSPE | TukeyC | Split-split-plot Experiment (SSPE) | list | | |
| data.tdcm01 | TDCM | Several data sets for the 'TDCM' package. | list | | |
| data.tdcm02 | TDCM | Several data sets for the 'TDCM' package. | list | | |
| data.tdcm03 | TDCM | Several data sets for the 'TDCM' package. | list | | |
| data.tdcm04 | TDCM | Several data sets for the 'TDCM' package. | list | | |
| data.tdcm05 | TDCM | Several data sets for the 'TDCM' package. | list | | |
| errorCodes | IAPWS95 | Error Codes | data.frame | 21 | 2 |
| CRD1 | ScottKnott | Completely Randomized Design (CRD) | list | | |
| CRD2 | ScottKnott | Completely Randomized Design (CRD) | list | | |
| FE | ScottKnott | Factorial Experiment (FE) | list | | |
| LSD | ScottKnott | Latin Squares Design (LSD) | list | | |
| RCBD | ScottKnott | Randomized Complete Block Design (RCBD) | list | | |
| sorghum | ScottKnott | Sorghum Yield: Balanced Squared Lattice Design | list | | |
| SPE | ScottKnott | Split-Plot Experiment (SPE) | list | | |
| SPET | ScottKnott | Split-plot Experiment in Time (SPET) | list | | |
| SSPE | ScottKnott | Split-Split-Plot Experiment (SSPE) | list | | |
| alps | DATAstudio | Swiss Alps Temperature Data | data.frame | 3190 | 5 |
| beatenberg | DATAstudio | Beatenberg Forest Temperature Data (In Unit Fréchet Scale) | data.frame | 2839 | 2 |
| brainwave | DATAstudio | Brainwave Data | data.frame | 7506 | 10 |
| brexit | DATAstudio | Brexit Poll Tracker | data.frame | 272 | 6 |
| challenger | DATAstudio | Space Shuttle Challenger Data | data.frame | 23 | 2 |
| claims | DATAstudio | Initial Claims of Unemployment | tis | | |
| cortical | DATAstudio | Brain Shape Data | list | | |
| cyclone_sst | DATAstudio | Tropical Cyclone and Sea Surface Temperature Data | data.frame | 2679 | 6 |
| diabetes | DATAstudio | Diabetes Diagnosis Data | data.frame | 286 | 3 |
| earthquake_tsunami | DATAstudio | Earthquake-Tsunami Data | data.frame | 3370 | 5 |
| ecg200 | DATAstudio | Electrocardiogram Data | tbl_df | 200 | 97 |
| fire | DATAstudio | Danish Fire Insurance Claims Database | data.frame | 1502 | 5 |
| GDP | DATAstudio | GDP of the US Economy | ts | 240 | 1 |
| GDPIP | DATAstudio | A Real-time Vintage of GDP and IP for the US Economy | mts | 268 | 2 |
| hurricane | DATAstudio | Hurricane Tracking Data | spec_tbl_df | 43122 | 8 |
| lse | DATAstudio | Selected Stocks from the London Stock Exchange | data.frame | 6894 | 27 |
| lungcancer | DATAstudio | Lung Cancer Diagnosis | data.frame | 241 | 4 |
| madeira | DATAstudio | Rainfall Data from Madeira | data.frame | 544 | 8 |
| marketsUS | DATAstudio | NASDAQ and NYSE Indices | data.frame | 12562 | 3 |
| merval | DATAstudio | MERVAL Stock Market Data | data.frame | 353 | 5 |
| metsynd | DATAstudio | Metabolic Syndrome Data | list | | |
| passengers | DATAstudio | International Airline Traffic Data | ts | 144 | 1 |
| psa | DATAstudio | Prostate Cancer Diagnosis Data | data.frame | 683 | 6 |
| santiago | DATAstudio | Santiago Temperature Data | mpp | | |
| sp500 | DATAstudio | Standard & Poor's 500 | data.frame | 5043 | 2 |
| sydney | DATAstudio | Monthly Sea Levels for Fort Denison | data.frame | 1317 | 8 |
| tmt | DATAstudio | Trail Making Test | data.frame | 245 | 2 |
| unemployment | DATAstudio | US Unemployment Rate | ts | | |
| wildfire | DATAstudio | Portugal Wildfire Data | data.frame | 14609 | 11 |
| cc176.y.adj | microplot | Adjusted response values and their five number summaries by treatment level for one model using the cc176 dataset. This dataset is used in two demos: "bwplot" and "boxplot-ggplot". | numeric | | |
| cc176fivenumsd | microplot | Adjusted response values and their five number summaries by treatment level for one model using the cc176 dataset. This dataset is used in two demos: "bwplot" and "boxplot-ggplot". | matrix | 4 | 5 |
| LegendrePolyMatrices | microplot | Legendre Orthogonal Polynomials for various values of alpha and beta. The dataset is used in the demo("LegendrePolynomials"). | matrix | 3 | 5 |
| toxicity | microplot | Simulated toxicity data. Dataset is used in demo("tablesPlusGraphicColumn"). | data.frame | 4 | 5 |
| canada_income_hale | dceasimR | Canada income quintile HALE data | tbl_df | 5 | 12 |
| england_imd_hale | dceasimR | England IMD quintile Health-Adjusted Life Expectancy data | tbl_df | 5 | 14 |
| england_imd_qol | dceasimR | England IMD quintile EQ-5D utility norms | tbl_df | 40 | 6 |
| example_cea_output | dceasimR | Example CEA model output | list | | |
| nsclc_dcea_example | dceasimR | NSCLC DCEA worked example | list | | |
| who_regions_hale | dceasimR | WHO regional HALE data | tbl_df | 6 | 10 |
| charity | mlim | some items about attitude towards charity | data.frame | 832 | 5 |
| manifest | mlim | Manifest Anxiety Scale | data.frame | 4469 | 52 |
| data_sim1 | LCCR | Simulated data 1 | matrix | 177 | |
| data_sim2 | LCCR | Simulated data 2 | list | | |
| data_sim3 | LCCR | Simulated data 3 | list | | |
| data_sim4 | LCCR | Simulated data 4 | list | | |
| EzGP_data | EzGP | Dataset for the example in function 'EzGP_fit' | matrix | 1296 | 7 |
| LEzGP_data | EzGP | Dataset for the example in function 'LEzGP_fit' | matrix | 8250 | 19 |
| simdata | groupWQS | Simulated data of chemical concentrations and one binary outcome variable | data.frame | 1000 | 15 |
| WQSdata | groupWQS | Simulated data of chemical concentrations and one continuous outcome variable | data.frame | 1000 | 10 |
| slesions | ZINARp | Skin lesions dataset | data.frame | 84 | 1 |
| timevisData | timevis | Timevis sample data | data.frame | 11 | 6 |
| timevisDataGroups | timevis | Timevis sample group data | data.frame | 3 | 2 |
| firms | dfvad | Sample data for firm dynamics | data.frame | 18 | 4 |
| mining | dfvad | Sample Data for Value Added Decomposition | data.frame | 28 | 7 |
| prices | dfvad | Sample Data for Price Indexes | list | | |
| sector | dfvad | Sample Data for Weighted Average Aggregation | data.frame | 336 | 9 |
| data_cary | OxSR | Diffuse soil reflectance via Cary equipment | tbl_df | 5417 | 50 |
| soil_refle | OxSR | Diffuse reflectance of soils from Brazil | tbl_df | 4241 | 24 |
| acl | mokken | Adjective Checklist Data | matrix | 433 | 218 |
| autonomySupport | mokken | Autonomy Support Data | data.frame | 259 | 8 |
| balance | mokken | Balance Data | data.frame | 484 | 25 |
| cavalini | mokken | Coping Strategies | data.frame | 828 | 17 |
| DS14 | mokken | DS14 | matrix | 541 | 16 |
| mcmi | mokken | Millon Clinical Multiaxial Inventory | data.frame | 1208 | 44 |
| SWMD | mokken | SWMD Data Subset | data.frame | 651 | 8 |
| SWMDK | mokken | SWMDK Data Subset | data.frame | 639 | 14 |
| transreas | mokken | Transitive Reasoning | data.frame | 425 | 13 |
| transreas2 | mokken | Transitive Reasoning Data | data.frame | 606 | 16 |
| trog | mokken | trog Data | data.frame | 210 | 80 |
| AgPalik | dielectric | Silver in the visible, from Palik | dielectric | | |
| AlRakic | dielectric | Aluminium in the visible | dielectric | | |
| aSi | dielectric | Chromium in the visible | dielectric | | |
| AuJC | dielectric | Gold in the visible | dielectric | | |
| Chromium | dielectric | Chromium in the visible | dielectric | | |
| constants | dielectric | Various physical constants | list | | |
| sapphire | dielectric | Sapphire in the visible | dielectric | | |
| Ti | dielectric | Amorphous silicon in the visible | dielectric | | |
| actuary_salaries | ExamPAData | DW Simpson actuarial salary data | spec_tbl_df | 138 | 6 |
| apartment_apps | ExamPAData | Apartment Apps | data.frame | 1430 | 41 |
| auto_claim | ExamPAData | Automotive claims | spec_tbl_df | 10296 | 29 |
| bank_loans | ExamPAData | Bank Loans | spec_tbl_df | 41188 | 21 |
| bike_sharing_demand | ExamPAData | Bike sharing demand | data.frame | 17376 | 10 |
| boston | ExamPAData | Boston | spec_tbl_df | 506 | 14 |
| customer_phone_calls | ExamPAData | Customer Phone Calls | spec_tbl_df | 10000 | 14 |
| customer_value | ExamPAData | Customer Value | spec_tbl_df | 48842 | 8 |
| exam_pa_titanic | ExamPAData | Exam PA Titanic | spec_tbl_df | 906 | 11 |
| health_insurance | ExamPAData | Health insurance | spec_tbl_df | 1338 | 7 |
| june_pa | ExamPAData | June_pa | spec_tbl_df | 23137 | 14 |
| patient_length_of_stay | ExamPAData | Patient Length of Stay | spec_tbl_df | 10000 | 13 |
| patient_num_labs | ExamPAData | Patient Number of Labs | spec_tbl_df | 10000 | 14 |
| pedestrian_activity | ExamPAData | Pedestrian activity | data.frame | 11373 | 7 |
| readmission | ExamPAData | Readmission | spec_tbl_df | 66782 | 9 |
| student_success | ExamPAData | Student Success | spec_tbl_df | 585 | 33 |
| travel_insurance | ExamPAData | Travel insurance data | data.frame | 10000 | 7 |
| travel_spending | ExamPAData | Travel spending data | data.frame | 4884 | 11 |
| data.q1 | ArCo | A generated dataset used in the examples | matrix | 100 | 20 |
| data.q2 | ArCo | A dataset used in the examples | list | | |
| inflationNFP | ArCo | Dataset used on the empirical example by Carvalho, Masini and Medeiros (2016). | list | | |
| cora_view1 | ORKM | The first view of Cora data set. | matrix | 2708 | |
| cora_view2 | ORKM | The second view of Cora data set. | matrix | 2708 | |
| cora_view3 | ORKM | The third view of Cora data set. | matrix | 2708 | |
| cora_view4 | ORKM | The fourth view of Cora data set. | matrix | 2708 | |
| cornell_cites | ORKM | The first view of Cornell data set. | matrix | 195 | |
| cornell_content | ORKM | The second view of Cornell data set. | matrix | 195 | |
| cornell_inbound | ORKM | The third view of Cornell data set. | matrix | 195 | |
| cornell_outbound | ORKM | The fourth view of Cornell data set. | matrix | 195 | |
| labelcora | ORKM | True clustering labels for Cora data set. | character | | |
| labelcornell | ORKM | True clustering labels for Cornell data set. | matrix | 195 | 1 |
| labelTexas | ORKM | True clustering labels for Texas data set. | numeric | | |
| labelWashington | ORKM | True clustering labels for Washington data set. | numeric | | |
| labelWisconsin | ORKM | True clustering labels for Wisconsin data set. | numeric | | |
| movie_1 | ORKM | The first view of Movie data set. | matrix | 617 | |
| movie_2 | ORKM | The second view of Movie data set. | matrix | 617 | |
| QCM | ORKM | The QCM data set with K=5. | data.frame | 125 | 15 |
| seed | ORKM | A single-view data set named Seeds. | data.frame | 210 | 8 |
| sobar | ORKM | A single-view data set named Sobar. | matrix | 72 | |
| texas_cites | ORKM | The first view of Texas data set. | matrix | 187 | |
| texas_content | ORKM | The second view of Texas dataset. | matrix | 187 | |
| texas_inbound | ORKM | The third view of Texas data set. | matrix | 187 | |
| texas_outbound | ORKM | The fourth view of Texas data set. | matrix | 187 | |
| turelabel | ORKM | Ture label of Movie data set. | data.frame | 617 | 1 |
| Washington_cites | ORKM | The third view of Washington data set. | matrix | 230 | |
| Washington_content | ORKM | The second view of Washington data set. | matrix | 230 | |
| Washington_inbound | ORKM | The third view of Washington data set. | matrix | 230 | |
| Washington_outbound | ORKM | The fourth view of Washington data set. | matrix | 230 | |
| Wisconsin_cites | ORKM | The first view of Wisconsin data set. | matrix | 265 | |
| Wisconsin_content | ORKM | The second view of Wisconsin data set. | matrix | 265 | |
| Wisconsin_inbound | ORKM | The third view of Wisconsin data set. | matrix | 265 | |
| Wisconsin_outbound | ORKM | The fourth view of Wisconsin data set. | matrix | 265 | |
| notesBorrowing | dBlockmodeling | The notes borrowing network between social-informatics students | matrix | 13 | |
| nyt | dBlockmodeling | The New York Times advertisement data | matrix | 108 | 25 |
| sampsonT3 | dBlockmodeling | Sampson's monastery data (time period 3) | matrix | 18 | 18 |
| Data2D | clusTransition | Synthetic Datasets (Two Dimensional) | list | | |
| Data3D | clusTransition | Synthetic Datasets (Three Dimensional) | list | | |
| elec_prices | fdaACF | Daily electricity price profiles from the Day-Ahead Spanish Electricity Market | data.frame | 365 | 24 |
| MedUnits | gdata | Table of conversions between Intertional Standard (SI) and US 'Conventional' Units for common medical measurements. | data.frame | 167 | 5 |
| baseline | exploreSE | DE results for airway - baseline | DESeqResults | | |
| baseline_down_go | exploreSE | Example GO enrichments for the baseline model | enrichResult | | |
| baseline_up_go | exploreSE | Example GO enrichments for the baseline model | enrichResult | | |
| cell_controlled | exploreSE | DE results for airway - cell-controlled | DESeqResults | | |
| controlled_down_go | exploreSE | Example GO enrichments for the cell_controlled model | enrichResult | | |
| controlled_up_go | exploreSE | Example GO enrichments for the cell controlled model | enrichResult | | |
| sample_ili | WarnEpi | Simulated ILI Surveillance Data | data.frame | 200 | 2 |
| ad_cost | SPORTSCausal | Advertising cost: a real experimental data under spillover effect | data.frame | 49 | 3 |
| data | NegativeControlOutcomeAdjustment | Data for examples | data.frame | 1000 | 5 |
| soil_data | SQIpro | Hypothetical Soil Quality Dataset | data.frame | 100 | 14 |
| Bean | ZeBook | Bean gene-based models dataset | list | | |
| carcass_data | ZeBook | Data of growth of beef cattle for Carcass model | list | | |
| chicks_data | ZeBook | Data of growth of chicks | data.frame | 600 | 4 |
| maize.data_EuropeEU | ZeBook | Maize Biomass and Leaf Area Data | data.frame | 40 | 6 |
| maize.data_MetaModelling | ZeBook | dataset of simulation for maize final biomass | data.frame | 680 | 9 |
| seedweight.data | ZeBook | Wheat grain weight measurements after anthesis | data.frame | 31 | 3 |
| Sunflower_Phomopsis | ZeBook | Phomopsis stem canker observations for Sunflower | data.frame | 43 | 2 |
| watbal.simobsdata | ZeBook | Soil water content measurements and associated simulations with WaterBalance model | data.frame | 123 | 10 |
| weather_EuropeEU | ZeBook | Weather Series for Europe (EU) from NASA POWER Agroclimatology | data.frame | 292160 | 8 |
| weather_FranceWest | ZeBook | Weather series for western France from NASA POWER agroclimatology | data.frame | 248360 | 10 |
| weather_GNS | ZeBook | Weather series for Gainesville (FL, USA) years 1982 and 1983 | data.frame | 730 | 11 |
| weather_SouthAsia | ZeBook | Weather series for southern Asia from NASA POWER agroclimatology | data.frame | 496808 | 9 |
| Wheat_GPC | ZeBook | Grain Protein Contents in Wheat Grains | data.frame | 43 | 13 |
| WheatYieldGreece | ZeBook | National Wheat Yield evolution for Greece from FAO | data.frame | 50 | 2 |
| CreditMDR | DiSSMod | Credit cards derogatory reports data | data.frame | 13444 | 8 |
| DoctorRWM | DiSSMod | German doctor first visits data | data.frame | 7293 | 26 |
| monetary | bsvarSIGNs | A 6-variable US monetary policy data, from 1965 Jan to 2007 Aug | mts | 515 | 6 |
| optimism | bsvarSIGNs | A 5-variable US business cycle data, from 1955 Q1 to 2004 Q4 | mts | 224 | 5 |
| rainfall_multi | rainerosr | Multiple Storm Events Rainfall Data | data.frame | 22 | 2 |
| rainfall_single | rainerosr | Single Storm Event Rainfall Data | data.frame | 12 | 2 |
| x_bounds | clickableImageMap | clickableImageMap data sets | list | | |
| x_cal.m | clickableImageMap | clickableImageMap data sets | list | | |
| x_cal.pullDown | clickableImageMap | clickableImageMap data sets | list | | |
| x_cal2 | clickableImageMap | clickableImageMap data sets | list | | |
| x_clickCoord | clickableImageMap | clickableImageMap data sets | matrix | 2 | 2 |
| x_gtab | clickableImageMap | clickableImageMap data sets | gtable | | |
| x_l | clickableImageMap | clickableImageMap data sets | list | | |
| x_m | clickableImageMap | clickableImageMap data sets | matrix | 2 | 10 |
| x_mtab | clickableImageMap | clickableImageMap data sets | gtable | | |
| x_mtab2 | clickableImageMap | clickableImageMap data sets | gtable | | |
| x_rcnames | clickableImageMap | clickableImageMap data sets | logical | | |
| x_rows | clickableImageMap | clickableImageMap data sets | numeric | | |
| x_tab | clickableImageMap | clickableImageMap data sets | gtable | | |
| x_y | clickableImageMap | clickableImageMap data sets | numeric | | |
| bscoup | spduration | B&S 2003 coup data | data.frame | 5463 | 14 |
| coups | spduration | Global coups, 1979 to 2010 | data.frame | 5828 | 9 |
| model.coups | spduration | Model of global coups from 1979 to 2010 | spdur | | |
| messy_employees | llmclean | Hypothetical Messy Employee Records Dataset | data.frame | 20 | 8 |
| messy_survey | llmclean | Hypothetical Messy Survey Response Dataset | data.frame | 15 | 5 |
| agri_trial | agriDQ | Simulated wheat variety trial dataset (RCBD) | data.frame | 20 | 7 |
| AsahiTodai | emIRT | Asahi-Todai Elite Survey | list | | |
| dwnom | emIRT | Poole-Rosenthal DW-NOMINATE data and scores, 80-110 U.S. Senate | list | | |
| manifesto | emIRT | German Manifesto Data | list | | |
| mq_data | emIRT | Martin-Quinn Judicial Ideology Scores | list | | |
| ustweet | emIRT | U.S. Twitter Following Data | list | | |
| nhanes1518 | mpower | NHANES data from 2015-2016 and 2017-2018 cycles | tbl_df | 19225 | 52 |
| farmer_adoption | swcEcon | Simulated farm-level SWC adoption survey dataset | data.frame | 120 | 10 |
| rainfall_erosivity_india | swcEcon | Rainfall erosivity (R-factor) for 20 Indian watershed districts | data.frame | 20 | 8 |
| swc_benchmarks | swcEcon | State-wise SWC watershed economic benchmarks for India | data.frame | 10 | 10 |
| swc_cost_norms | swcEcon | SWC measure unit cost norms (PMKSY-WDC 2015, updated to 2024) | data.frame | 18 | 7 |
| usle_india_soils | swcEcon | USLE erodibility and nutrient parameters for Indian soil orders | data.frame | 8 | 11 |
| watershed_projects | swcEcon | Simulated watershed SWC project evaluation dataset | data.frame | 50 | 18 |
| Mena | dtp | Middle East and North Africa yearly data of economic growth, inflation rate,foreign direct investment and trade | tbl_df | 273 | 6 |
| motdat | qcv | Example data used in Furr & Heuckeroth, on Impression Motivation (IM) | data.frame | 90 | 13 |
| case.study.psychiatrist | ClusBoot | Patient by psychiatric symptom data | data.frame | 30 | 28 |
| admix | adwave | Simulated Admixed Population Data | list | | |
| mtscr_creativity | mtscr | Creativity assessment through semantic distance dataset | tbl_df | 4585 | 4 |
| mtscr_self_rank | mtscr | Self-chosen best answers | tbl_df | 3225 | 4 |
| parkinson | imputeR | Parkinsons Data Set | data.frame | 195 | 22 |
| spect | imputeR | SPECT Heart Data Set | data.frame | 266 | 23 |
| tic | imputeR | Insurance Company Benchmark (COIL 2000) Data Set | data.frame | 5822 | 86 |
| idata | causal.decomp | Synthetic Data for illustrating optimal treatment regimes and individualized effects | data.frame | 2000 | 7 |
| sdata | causal.decomp | Synthetic Data for Illustration | data.frame | 1000 | 9 |
| sMIDUS | causal.decomp | Synthetic Data Generated Based on the Midlife Development in the U.S. (MIDUS) Study | data.frame | 1948 | 9 |
| ceriodaphnia.group | CountsEPPM | Ceriodaphnia data | list | | |
| herons.case | CountsEPPM | Green-backed herons as two groups | data.frame | 40 | 2 |
| herons.group | CountsEPPM | Green-backed herons as two groups | list | | |
| Luningetal.litters | CountsEPPM | Number of trials (implantations) in data of Luning, et al. (1966) | list | | |
| takeover.bids.case | CountsEPPM | Takeover bids data. | data.frame | 126 | 12 |
| Titanic.survivors.case | CountsEPPM | Titanic survivors data | data.frame | 12 | 5 |
| Williams.litters | CountsEPPM | Number of trials (implantations) of data of Williams (1996). | list | | |
| cmAll | ComradesM | Results of the Comrades Marathon race from 1921 to 2019 | spec_tbl_df | 445129 | 12 |
| cmDist | ComradesM | Distances run in the Comrades Marathon race from 1970 to 2019 | data.frame | 50 | 3 |
| cmE | ComradesM | Elevations along the Comrades Marathon route | data.frame | 109 | 3 |
| invitrodb_dd | tcpl | Short descriptions of fields for different tables are stored in a data dictionary. | data.table | 44 | 3 |
| load_data_columns | tcpl | Lists of column names returned from tcplLoadData invitrodb v4.1 (same as CCTE Bioactivity API version). | list | | |
| mc_test | tcpl | List of lists containing queries sent to tcplQuery associated with each test case. Each list also contains the associated ids with each case. Only meant to be used with automated testing with mocking for mc data. | list | | |
| mc_vignette | tcpl | List with multi-concentration data for the vignette | list | | |
| mcdat | tcpl | A subset of ToxCast data showing changes in the activity of the intracellular estrogen receptor. | data.frame | 14183 | 10 |
| mthd_list_defaults | tcpl | Lists of data frames returned from tcplMthdList invitrodb v4.2 | list | | |
| sc_test | tcpl | List of lists containing queries sent to tcplQuery associated with each test case. Each list also contains the associated ids with each case. Only meant to be used with automated testing with mocking for sc data. | list | | |
| sc_vignette | tcpl | List with single-concentration data for the vignette | list | | |
| scdat | tcpl | A subset of ToxCast data showing changes in transcription factor activity for multiple targets. | data.frame | 320 | 10 |
| test_api | tcpl | List containing ids used for different automated tests of tcpl integration with the CTX APIs, randomly selected from what is available via API. | list | | |
| errorDbase | optBiomarker | Database of leave-one-out cross validation errors for various combinations of data characteristics | array | | |
| realBiomarker | optBiomarker | A set of 54359 median gene expressions in log (base 2) scale | numeric | | |
| CHEM97Na | micemd | An incomplete two-level dataset which consists of A/AS-level examination data from England | data.frame | 1681 | 5 |
| IPDNa | micemd | A simulated Individual Patient Data (IPD) meta-analysis with missing values. | data.frame | 11685 | 10 |
| Obesity | micemd | A two-level incomplete dataset based on an online obesity survey | data.table | 2111 | 8 |
| Germany.1918 | R0 | Germany 1918 dataset | numeric | | |
| GT.chld.hsld | R0 | 2009 A/H1N1 observed generation time distribution | numeric | | |
| H1N1.serial.interval | R0 | H1N1 serial interval | numeric | | |
| dataHBMEbeta | saeHB.ME.beta | Sample Data for Small Area Estimation with Measurement Error using Hierarchical Bayesian Method under Beta Distribution | data.frame | 30 | 8 |
| dataHBMEbetaNS | saeHB.ME.beta | Sample Data for Small Area Estimation with Measurement Error using Hierarchical Bayesian Method under Beta Distribution with Non-sampled Area | data.frame | 30 | 8 |
| tsdata | SSAforecast | Time series data | ts | 159 | 1 |
| df_adverse_events | gtreg | Simulated Adverse Event Database | tbl_df | 100 | 8 |
| df_patient_characteristics | gtreg | Simulated Patient Characteristics Database | tbl_df | 100 | 7 |
| clust4 | kmed | 4-clustered data set | data.frame | 300 | 3 |
| clust5 | kmed | 5-clustered data set | data.frame | 800 | 3 |
| globalfood | kmed | Global food security index | data.frame | 113 | 4 |
| heart | kmed | Heart Disease data set | data.frame | 297 | 14 |
| breakpoints | SVAlignR | SVAlignR Sample Data | data.frame | 82 | 11 |
| longreads | SVAlignR | SVAlignR Sample Data | data.frame | 197 | 5 |
| campaign_descriptions | completejourney | Campaign metadata. | spec_tbl_df | 27 | 4 |
| campaigns | completejourney | Campaigns to household data. | spec_tbl_df | 6589 | 2 |
| coupon_redemptions | completejourney | Coupon redemption data. | spec_tbl_df | 2102 | 4 |
| coupons | completejourney | Coupon metadata. | spec_tbl_df | 116204 | 3 |
| demographics | completejourney | Household demographic metadata. | spec_tbl_df | 801 | 8 |
| products | completejourney | Product metadata. | spec_tbl_df | 92331 | 7 |
| promotions_sample | completejourney | Sampling of the full promotions data set. | spec_tbl_df | 360535 | 5 |
| transactions_sample | completejourney | Sampling of the full Complete Journey transactions. | tbl_df | 75000 | 11 |
| seoul_bike | fkcentroids | Seoul Public Bike Rental Records | list | | |
| CVF | QCA | Ethnic protest in Europe | data.frame | 29 | 6 |
| CVR | QCA | Ethnic protest in Europe | data.frame | 29 | 6 |
| d.AS | QCA | Internal Functions | data.frame | 14 | 8 |
| d.autonomy | QCA | Internal Functions | data.frame | 30 | 9 |
| d.Bas | QCA | Internal Functions | data.frame | 30 | 9 |
| d.biodiversity | QCA | Internal Functions | data.frame | 30 | 9 |
| d.BWB | QCA | Internal Functions | data.frame | 27 | 18 |
| d.CS | QCA | Internal Functions | data.frame | 15 | 10 |
| d.CZH | QCA | Internal Functions | data.frame | 17 | 5 |
| d.educate | QCA | Internal Functions | data.frame | 8 | 5 |
| d.education | QCA | Internal Functions | data.frame | 14 | 7 |
| d.Emm | QCA | Internal Functions | data.frame | 19 | 7 |
| d.graduate | QCA | Internal Functions | data.frame | 17 | 6 |
| d.health | QCA | Internal Functions | data.frame | 27 | 18 |
| d.HK | QCA | Internal Functions | data.frame | 48 | 7 |
| d.HMN | QCA | Internal Functions | data.frame | 31 | 6 |
| d.homeless | QCA | Internal Functions | data.frame | 15 | 10 |
| d.jobsecurity | QCA | Internal Functions | data.frame | 19 | 7 |
| d.Kil | QCA | Internal Functions | data.frame | 14 | 6 |
| d.Kro | QCA | Internal Functions | data.frame | 22 | 6 |
| d.napoleon | QCA | Internal Functions | data.frame | 14 | 8 |
| d.partybans | QCA | Internal Functions | data.frame | 48 | 7 |
| d.pban | QCA | Internal Functions | data.frame | 48 | 5 |
| d.represent | QCA | Internal Functions | data.frame | 22 | 6 |
| d.RS | QCA | Internal Functions | data.frame | 17 | 6 |
| d.SA | QCA | Internal Functions | data.frame | 21 | 10 |
| d.socialsecurity | QCA | Internal Functions | data.frame | 31 | 6 |
| d.SS | QCA | Internal Functions | data.frame | 14 | 7 |
| d.stakeholder | QCA | Internal Functions | data.frame | 17 | 5 |
| d.transport | QCA | Internal Functions | data.frame | 21 | 10 |
| d.urban | QCA | Internal Functions | data.frame | 14 | 6 |
| d.women | QCA | Internal Functions | data.frame | 22 | 6 |
| Emme | QCA | Internal Functions | data.frame | 19 | 7 |
| HarKem | QCA | Internal Functions | data.frame | 48 | 7 |
| HC | QCA | Time-Difference | data.frame | 15 | 5 |
| Krook | QCA | Internal Functions | data.frame | 22 | 6 |
| LC | QCA | Lipset's indicators for the survival of democracy during the inter-war period. | data.frame | 18 | 6 |
| LF | QCA | Lipset's indicators for the survival of democracy during the inter-war period. | data.frame | 18 | 6 |
| LM | QCA | Lipset's indicators for the survival of democracy during the inter-war period. | data.frame | 18 | 6 |
| LR | QCA | Lipset's indicators for the survival of democracy during the inter-war period. | data.frame | 18 | 6 |
| NF | QCA | Class voting in post-World War era | data.frame | 12 | 5 |
| Quine | QCA | Internal Functions | data.frame | 6 | 4 |
| RagStr | QCA | Internal Functions | data.frame | 17 | 6 |
| Rokkan | QCA | Internal Functions | data.frame | 16 | 5 |
| RS | QCA | University recognition of a graduate student union | data.frame | 17 | 6 |
| plasma | qrjoint | Plasma Concentration of Beta-Carotene and Retinol | data.frame | 315 | 14 |
| redmaple | qrjoint | Basal Areas of Red Maple Trees | data.frame | 608 | 8 |
| fire | spearmanCI | Danish Fire Insurance Claims Database | data.frame | 1502 | 5 |
| ftse100 | dccmidas | FTSE 100 data | xts | 5136 | 3 |
| indpro | dccmidas | Monthly U.S. Industrial Production | xts | 1224 | |
| nasdaq | dccmidas | NASDAQ data | xts | 5119 | 3 |
| sp500 | dccmidas | S&P 500 data | xts | 5122 | 3 |
| tavi | gems | tavi data set | data.frame | 194 | 7 |
| Riverside | rmpw | NEWWS Riverside data | data.frame | 694 | 16 |
| Famin81A | mpm | Famin81A Data... | data.frame | 18 | 5 |
| Golub | mpm | Golub (1999) Data... | data.frame | 5327 | 73 |
| Golub.grp | mpm | Golub (1999) Data... | integer | | |
| sim_exam | tooth | Simulated dental examination data | data.frame | 3920 | 7 |
| hvidovre | PanJen | Houseprice data | data.frame | 901 | 19 |
| gmt | scMappR | gmt_example | list | | |
| PBMC_example | scMappR | PBMC_scMappR | list | | |
| POA_example | scMappR | Preoptic_Area | list | | |
| scMappR_tissues | scMappR | scMappR_tissues | character | | |
| sm | scMappR | single_cell_process | dgCMatrix | | |
| cruncep | PEcAn.data.atmosphere | | data.table | 8736 | 10 |
| cruncep_landmask | PEcAn.data.atmosphere | | data.table | 259200 | 3 |
| ebifarm | PEcAn.data.atmosphere | | data.table | 8390 | 10 |
| FLUXNET.sitemap | PEcAn.data.atmosphere | | data.frame | 698 | 2 |
| landmask | PEcAn.data.atmosphere | | data.table | 18048 | 3 |
| Lat | PEcAn.data.atmosphere | | array | | |
| Lat | PEcAn.data.atmosphere | | array | | |
| Lon | PEcAn.data.atmosphere | | array | | |
| Lon | PEcAn.data.atmosphere | | array | | |
| narr | PEcAn.data.atmosphere | | data.table | 8760 | 10 |
| narr3h | PEcAn.data.atmosphere | | data.table | 8736 | 10 |
| beta | DMtest | Example DNA methylation data for dmvc function | matrix | 500 | 334 |
| covariate | DMtest | Example covariate data for dmvc function | data.frame | 334 | 3 |
| lalonde | randChecks | Lalonde (1986) Data | data.frame | 614 | 9 |
| lalonde.matched.card | randChecks | A Cardinality Matched Dataset for the Lalonde (1986) Data | data.frame | 240 | 10 |
| lalonde.matched.ps | randChecks | A 1:1 Propensity Score Matched Dataset for the Lalonde (1986) Data | data.frame | 370 | 10 |
| bandwidth.chrisproba | PEcAnRTM | | data.frame | 62 | 4 |
| dataSpec_prospectd | PEcAnRTM | | matrix | 2101 | 8 |
| fwhm.aviris.classic | PEcAnRTM | | data.frame | 224 | 2 |
| fwhm.aviris.ng | PEcAnRTM | | data.frame | 480 | 5 |
| fwhm.hyperion | PEcAnRTM | | list | | |
| model.list | PEcAnRTM | | data.frame | 6 | 5 |
| rsr.avhrr | PEcAnRTM | | matrix | 503 | 4 |
| rsr.landsat5 | PEcAnRTM | | matrix | 914 | 7 |
| rsr.landsat7 | PEcAnRTM | | matrix | 914 | 7 |
| rsr.landsat8 | PEcAnRTM | | matrix | 825 | 8 |
| rsr.modis | PEcAnRTM | | matrix | 1820 | 8 |
| rsr.viirs | PEcAnRTM | | matrix | 2500 | 11 |
| sensor.rsr | PEcAnRTM | | list | | |
| testspec_ACRU | PEcAnRTM | | matrix | 2101 | 78 |
| BADM | PEcAn.data.land | Biomass and soil data from FluxNet sites | data.frame | 12300 | 13 |
| bism_kc_by_crop | PEcAn.data.land | BIS crop coefficients by crop | spec_tbl_df | 66 | 16 |
| ca_n_application_rate | PEcAn.data.land | California recommended N application rates by crop | spec_tbl_df | 40 | 7 |
| ca_organic_amendment_app_rate | PEcAn.data.land | California organic amendment application rates | spec_tbl_df | 64 | 9 |
| ca_organic_amendment_properties | PEcAn.data.land | California organic amendment properties | spec_tbl_df | 32 | 9 |
| crop_whc | PEcAn.data.land | Crop-specific rooting depths and water-depletion thresholds | spec_tbl_df | 66 | 7 |
| fertilizer_composition_data | PEcAn.data.land | Fertilizer Nutrient Composition Table | tbl_df | 64 | 8 |
| iscn_soc | PEcAn.data.land | Soil organic carbon (SOC) density based on eco-region level 2 code from the ISCN database. | matrix | 200 | 43 |
| landiq_crop_mapping_codes | PEcAn.data.land | LandIQ crop mapping codes | spec_tbl_df | 203 | 4 |
| soil_class | PEcAn.data.land | Default parameters for calculating soil properties from sand & clay content | list | | |
| FIQ_CONT_ITEMS_D | SINRELEF.LD | SINRELEF-internal | numeric | | |
| FIQ_CONT_L | SINRELEF.LD | SINRELEF-internal | numeric | | |
| FIQ_CONT_VALUES_D | SINRELEF.LD | SINRELEF-internal | numeric | | |
| FIQ_CONT_VAR | SINRELEF.LD | SINRELEF-internal | numeric | | |
| FIQ400 | SINRELEF.LD | FIQ database. | data.frame | 400 | 17 |
| ensemble.output | PEcAn.uncertainty | | list | | |
| ensemble.samples | PEcAn.uncertainty | | list | | |
| sa.samples | PEcAn.uncertainty | | list | | |
| sensitivity.output | PEcAn.uncertainty | | list | | |
| settings | PEcAn.uncertainty | | list | | |
| trait.samples | PEcAn.uncertainty | | list | | |
| data1 | Dtableone | data1 | data.frame | 475 | 3 |
| data2 | Dtableone | data2 | data.frame | 475 | 3 |
| data3 | Dtableone | data3 | data.frame | 475 | 3 |
| data4 | Dtableone | data4 | data.frame | 475 | 3 |
| indpro | rumidas | Monthly U.S. Industrial Production | xts | 363 | 1 |
| rv5 | rumidas | S&P 500 realized variance at 5-minutes | xts | 5079 | 1 |
| sp500 | rumidas | S&P 500 daily log-returns | xts | 5079 | 1 |
| vix | rumidas | VIX daily data | xts | 5093 | |
| example_data_2PL | FedIRT | Binary Response Dataset for Federated 2PL Model | data.frame | 160 | 10 |
| example_data_2PL_1 | FedIRT | Binary Response Dataset for Federated 2PL Model | data.frame | 81 | 10 |
| example_data_2PL_2 | FedIRT | Binary Response Dataset for Federated 2PL Model | data.frame | 79 | 10 |
| example_data_graded | FedIRT | Graded Response Dataset for Federated Graded Model | data.frame | 100 | 10 |
| example_data_graded_and_binary | FedIRT | Graded Response Dataset for Federated Graded Model | data.frame | 81 | 8 |
| pftmapping | PEcAn.ED2 | Mapping of PEcAn PFT names to ED2 PFT numbers | data.frame | 73 | 2 |
| co2.1850.2020 | PEcAn.LPJGUESS | | data.frame | 171 | 2 |
| AmpGram_predictions | AmpGram | Prediction of antimicrobial peptides | list | | |
| example_psa | dampack | Sample PSA data for testing | list | | |
| example_psa_obj | dampack | Sample PSA data for testing | psa | | |
| hund_strat | dampack | Sample deterministic data for testing | data.frame | 100 | 3 |
| psa_cdiff | dampack | Sample PSA dataset | psa | | |
| pecan_releases | PEcAn.all | Dates, tags, and versions of all PEcAn releases | data.frame | 35 | 3 |
| pecan_version_history | PEcAn.all | Versions of all PEcAn packages in each release of PEcAn | data.frame | 53 | 36 |
| sibcasa_output_vars | PEcAn.SIBCASA | Output variables for SIBCASA | data.frame | 131 | 6 |
| DATASET | IOLS | Sample Data for Analysis | data.frame | 1000 | 3 |
| ARG_MAZ | sapfluxnetr | ARG_MAZ sapfluxnet site | sfn_data | | |
| ARG_TRE | sapfluxnetr | ARG_TRE sapfluxnet site | sfn_data | | |
| AUS_CAN_ST2_MIX | sapfluxnetr | AUS_CAN_ST2_MIX sapfluxnet site | sfn_data | | |
| sfn_metadata_ex | sapfluxnetr | sfn_metadata cache file for example data (ARG_MAZ, ARG_TRE and AUS_CAN_ST2_MIX) | list | | |
| debt | cccm | Debt Data | data.frame | 106 | 4 |
| exp1 | expirest | Stability potency data of five batches | data.frame | 53 | 3 |
| exp2 | expirest | Stability related substance data of three batches | data.frame | 24 | 3 |
| exp3 | expirest | Stability moisture data of three batches | data.frame | 33 | 3 |
| exp4 | expirest | Stability data of 4 batches | data.frame | 36 | 3 |
| ACTG175 | BART | AIDS Clinical Trials Group Study 175 | data.frame | 2139 | 27 |
| alligator | BART | American alligator Food Choice | data.frame | 80 | 5 |
| arq | BART | NHANES 2009-2010 Arthritis Questionnaire | data.frame | 4747 | 10 |
| bladder | BART | Bladder Cancer Recurrences | data.frame | 340 | 7 |
| bladder1 | BART | Bladder Cancer Recurrences | data.frame | 294 | 11 |
| bladder2 | BART | Bladder Cancer Recurrences | data.frame | 178 | 8 |
| leukemia | BART | Bone marrow transplantation for leukemia and multi-state models | data.frame | 137 | 22 |
| lung | BART | NCCTG Lung Cancer Data | data.frame | 228 | 10 |
| transplant | BART | Liver transplant waiting list | data.frame | 815 | 6 |
| xdm20.test | BART | A data set used in example of 'recur.bart'. | matrix | 79800 | 84 |
| xdm20.train | BART | A real data example for 'recur.bart'. | matrix | 39450 | 84 |
| ydm20.test | BART | A data set used in example of 'recur.bart'. | integer | | |
| ydm20.train | BART | A data set used in example of 'recur.bart'. | integer | | |
| replicates | GMSimpute | Raw mass spectrum proteomics log abundance for 4 pairs of technical replicates. | data.frame | 85 | 8 |
| tcga.bc | GMSimpute | Raw mass spectrum metabolomics data for TCGA breast cancer study. | data.frame | 150 | 30 |
| tcga.bc.full | GMSimpute | A subset of mass spectrum metabolomics data for TCGA breast cancer study without missing peaks. | matrix | 100 | 30 |
| mzipmed_data | mzipmed | Data to be used in the mzipmed package examples | data.frame | 500 | 10 |
| CITMIC_Data | CITMIC | An environment variable that includes some example data | environment | | |
| DengueSimR01 | IDSpatialStats | Simulated dataset of dengue transmission with basic reproductive number of 1 | matrix | 1100 | 5 |
| DengueSimR02 | IDSpatialStats | Simulated dataset of dengue cases with basic reproductive number of 2 | matrix | 1000 | 5 |
| DengueSimRepresentative | IDSpatialStats | Simulated dataset of dengue cases with representative underlying population | matrix | 764 | 5 |
| sim_data | intmed | A simulated dataset for demonstrating mediation analysis with a single mediator | data.frame | 1000 | 3 |
| substance | intmed | Fictitious family intervention data | data.frame | 553 | 6 |
| L4.AllDates | MAR1 | L4 plankton abundance time-series | data.frame | 1123 | 20 |
| L4.mar | MAR1 | L4 plankton abundance time-series prepared for MAR modeling | data.frame | 179 | 21 |
| run1 | MAR1 | A sample | MAR | | |
| beta_0.75_0.65 | bde | Synthetic dataset from a beta distribution | numeric | | |
| beta_1_10 | bde | Synthetic dataset from a beta distribution | numeric | | |
| beta_5_10 | bde | Synthetic dataset from a beta distribution | numeric | | |
| eruption | bde | Eruption lengths of Old Faithful geyser | numeric | | |
| suicide.r | bde | Scaled data from suicide risk data | numeric | | |
| tgaussian | bde | Synthetic dataset from a truncated Gaussian distribution | numeric | | |
| tuna.r | bde | Scaled 'tuna' data | numeric | | |
| skulls | smsets | Egyptian male skulls | data.frame | 150 | 5 |
| sparrows | smsets | Body measurements of female sparrows | data.frame | 49 | 6 |
| census_divisions_2016 | mapcan | Census divisions (2016) | data.frame | 26099 | 13 |
| census_divisions_2016_carto | mapcan | Census divisions cartogram data frame (territories included) (2016) | data.frame | 38595 | 13 |
| census_divisions_2016_noterr_carto | mapcan | Census divisions cartogram data frame (territories excluded) (2016) | data.frame | 25329 | 13 |
| census_pop2016 | mapcan | Census division population data for 2011 and 2016 | data.frame | 293 | 13 |
| federal_election_results | mapcan | Canadian federal election results data | data.frame | 2172 | 12 |
| federal_riding_bins | mapcan | Canadian federal riding bins (used for tile plots) | data.frame | 947 | 8 |
| federal_riding_hexagons | mapcan | Canadian federal riding hexagons (used for hexagonal tile plots) | data.frame | 6629 | 15 |
| federal_ridings | mapcan | Federal ridings | data.frame | 25248 | 15 |
| province_pop_annual | mapcan | Annual provincial populations data frame dating back to 1971 | data.frame | 638 | 3 |
| provinces_noterr_carto | mapcan | Provinces and territories cartogram data (territories excluded) | data.frame | 8209 | 11 |
| provinces_territories | mapcan | Provinces and territories standard geographic data | data.frame | 11430 | 10 |
| provinces_territories_carto | mapcan | Provinces and territories cartogram data (territories included) | data.frame | 18184 | 11 |
| quebec_prov_ridings2018 | mapcan | Quebec provincial ridings geographic data | data.frame | 5672 | 11 |
| quebec_provincial_results | mapcan | Quebec provincial election results data | data.frame | 125 | 6 |
| quebec_riding_bins | mapcan | Quebec provincial riding bins (used for tile plots) | data.frame | 125 | 6 |
| quebec_riding_hexagons | mapcan | Quebec provincial riding hexagons (used for hexagonal tile plots) | data.frame | 875 | 13 |
| riding_info | mapcan | Canadian federal riding population information | data.frame | 338 | 4 |
| kgss_sample | simqi | A Sample of Korean General Social Survey Data, 2023 | tbl_df | 1123 | 13 |
| som_sample | simqi | A Sample of SOM Institute Data, 2019-2020 | tbl_df | 2841 | 14 |
| ScoreInd | InformativeCensoring | Simulated time to event data with 5 time independent covariates | data.frame | 400 | 11 |
| ScoreTimeDep | InformativeCensoring | Simulated time dependent variables for time to event data | data.frame | 2191 | 5 |
| pvModels | tdr | Error Statistics | data.frame | 493 | 22 |
| pvObs | tdr | Error Statistics | numeric | | |
| APAP | pksensi | Pharmacokinetic Dataset of Acetaminophen | data.frame | 32 | 7 |
| graphexample | gephi | a super simple graph example | igraph | | |
| oldestpeople | DataViz | ~ List of the oldest people in the world data set ~ | data.frame | 64 | 4 |
| weekschedule | DataViz | ~ Example data set number 2 for force layout ~ | data.frame | 7 | 10 |
| Workweek | DataViz | ~ Example data set for force layout ~ | data.frame | 2 | 3 |
| exampleSpectrum | rtms | Simple Example Spectrum | rtmsSpectrum | | |
| api_endpoints | ftDK | The API endpoints at the Danish Parliament | tbl_df | 50 | 4 |
| ndvi_AK10000 | remotePARTS | NDVI remote sensing data for 10,000 random pixels from Alaska, with rare land classes removed. | data.frame | 10000 | 37 |
| partGLS_ndviAK | remotePARTS | partitioned GLS results | partGLS | | |
| nor_covid19_cases_by_time_location | csdb | Covid-19 data for PCR-confirmed cases in Norway (nation and county) | data.table | 11028 | 18 |
| rabimo_inputs_2020 | kwb.rabimo | R-Abimo Inputs (Data and Config) for Berlin, 2020 | list | | |
| rabimo_inputs_2025 | kwb.rabimo | R-Abimo Inputs (Data and Config) for Berlin, 2025. | list | | |
| ACS | DTDA | Acute Coronary Syndrome data | data.frame | 939 | 5 |
| ACSred | DTDA | Acute Coronary Syndrome reduced data | data.frame | 917 | 5 |
| AIDS | DTDA | AIDS Blood Transfusion Data, right truncated | data.frame | 258 | 3 |
| AIDS.DT | DTDA | AIDS Blood Transfusion Data, doubly truncated | data.frame | 295 | 4 |
| ChildCancer | DTDA | Childhood Cancer Data | data.frame | 406 | 8 |
| EqSRounded | DTDA | Equipment-S Rounded Failure Time Data | data.frame | 174 | 3 |
| PDearly | DTDA | Parkinson's Disease Data: early onset | data.frame | 99 | 5 |
| PDlate | DTDA | Parkinson's Disease Data: late onset | data.frame | 100 | 5 |
| Quasars | DTDA | Quasars Data | data.frame | 210 | 3 |
| standard_vars | PEcAn.utils | Standardized variable names and units for PEcAn | data.frame | 121 | 11 |
| trait.dictionary | PEcAn.utils | | data.frame | 113 | 4 |
| sim_all | MIRES | Simulated data: All parameters vary (Full non-invariance) | data.frame | 250 | 9 |
| sim_intercepts | MIRES | Simulated data: Intercepts vary | data.frame | 250 | 9 |
| sim_items | MIRES | Simulated data: Half the items are non-invariant. | data.frame | 250 | 9 |
| sim_loadings | MIRES | Simulated data: Loadings vary | data.frame | 250 | 9 |
| sim_none | MIRES | Simulated data: No variance (Full invariance) | data.frame | 250 | 9 |
| sim_resid | MIRES | Simulated data: Residual variances vary | data.frame | 250 | 9 |
| datCancer | survPen | Patients diagnosed with cervical cancer | data.frame | 2000 | 6 |
| expected.table | survPen | French women mortality table | data.frame | 22866 | 3 |
| HeartFailure | survPen | Patients with heart failure at risk of recurrent hospitalization events | data.frame | 3068 | 6 |
| list.wicss | survPen | List of ICSS standards for age-standardization of cancer (net) survival | list | | |
| bullets | cmpsR | Information of two example bullets | tbl_df | 12 | 3 |
| iran.quakes | ETAS | An Iranian Earthquake Catalog | data.frame | 5970 | 5 |
| italy.quakes | ETAS | An Italian Earthquake Catalog | data.frame | 2158 | 6 |
| japan.quakes | ETAS | A Japanese Earthquake Catalog | data.frame | 13724 | 6 |
| valueType_list | mlstrOpalr | Built-in tibble of allowed valueType values | tbl_df | 12 | 7 |
| denv_data | topolow | Dengue Virus (DENV) Titer Data | data.frame | 1838 | 7 |
| example_positions | topolow | Example Antigenic Mapping Data | data.frame | 272 | 11 |
| h3n2_data | topolow | H3N2 Influenza HI Assay Data from Smith et al. 2004 | data.frame | 3542 | 7 |
| hiv_titers | topolow | HIV Neutralization Assay Data | data.frame | 178912 | 3 |
| hiv_viruses | topolow | HIV Virus Metadata | data.frame | 2638 | 4 |
| delivery_data | spopt | Delivery routing data for Fort Worth, TX | list | | |
| tarrant_travel_times | spopt | Tarrant County Travel Time Matrix Example Data | list | | |
| emale_ava | gggenomes | All-versus-all whole genome alignments of 6 EMALE genomes | tbl_df | 125 | 23 |
| emale_cogs | gggenomes | Clusters of orthologs of 6 EMALE proteomes | tbl_df | 48 | 3 |
| emale_gc | gggenomes | Relative GC-content along 6 EMALE genomes | spec_tbl_df | 2856 | 6 |
| emale_genes | gggenomes | Gene annotations if 6 EMALE genomes (endogenous virophages) | tbl_df | 143 | 17 |
| emale_ngaros | gggenomes | Integrated Ngaro retrotransposons of 6 EMALE genomes | tbl_df | 3 | 14 |
| emale_prot_ava | gggenomes | All-versus-all alignments 6 EMALE proteomes | spec_tbl_df | 827 | 13 |
| emale_seqs | gggenomes | Sequence index of 6 EMALE genomes (endogenous virophages) | spec_tbl_df | 6 | 4 |
| emale_tirs | gggenomes | Terminal inverted repeats of 6 EMALE genomes | tbl_df | 12 | 15 |
| crypto_prices | tsviz | Prices of 3 crypto currencies | data.frame | 1174 | 4 |
| esri_version_ref | esri2sf | ArcGIS REST API Version Reference | spec_tbl_df | 19 | 2 |
| scenario_regions | pacta.scenario.data.preparation | A dataset of countries contained in different scenario regions. | spec_tbl_df | 1492 | 4 |
| scenario_source_pacta_geography_bridge | pacta.scenario.data.preparation | A dataset that maps scenario regions as defined by their source, to a list of PACTA compatible scenario regions. | spec_tbl_df | 15 | 3 |
| ihdengland | disbayes | Ischemic heart disease in England | tbl_df | 3400 | 9 |
| ihdtrends | disbayes | Trends in ischemic heart disease in England | tbl_df | 40000 | 5 |
| Arthritis1 | MBRM | Arthritis1 Dataset | data.frame | 190 | 6 |
| toenail | MBRM | toenail Dataset | data.frame | 1908 | 5 |
| BVDW | gogarch | Dow Jones Industrial Average and Nasdaq stock indices | data.frame | 2610 | 3 |
| BVDWAIR | gogarch | Stock prices transportation sector, oil and kerosene prices | data.frame | 3791 | 7 |
| BVDWSTOXX | gogarch | Sector indices of the EURO STOXX 600 | data.frame | 5652 | 16 |
| VDW | gogarch | Dow Jones Industrial Average and Nasdaq stock indices | data.frame | 3082 | 2 |
| internet_availability | RMaCzek | Data of internet_availability | list | | |
| seals_similarities | RMaCzek | Data of seals_similarities | matrix | 37 | 37 |
| skulls_distances | RMaCzek | Data of skulls_distances | matrix | 13 | 13 |
| urns | RMaCzek | Data of urns | matrix | 15 | 9 |
| examp_DD | discAUC | Delay discounting data | tbl_df | 360 | 4 |
| examp_PD | discAUC | Probability discounting data | tbl_df | 360 | 4 |
| Bublava_SO_cz | lpanda | Municipal Election Data: Bublava (SO, CZ) | data.frame | 193 | 14 |
| Cernosice_PZ_cz | lpanda | Municipal Election Data: Cernosice (PZ, CZ) | data.frame | 971 | 14 |
| Dasnice_SO_cz | lpanda | Municipal Election Data: Dasnice (SO, CZ) | data.frame | 81 | 14 |
| Doubice_DC_cz | lpanda | Municipal Election Data: Doubice (DC, CZ) | data.frame | 151 | 14 |
| Horomerice_PZ_cz | lpanda | Municipal Election Data: Horomerice (PZ, CZ) | data.frame | 438 | 14 |
| Hradce_CB_cz | lpanda | Municipal Election Data: Hradce (CB, CZ) | data.frame | 103 | 14 |
| Jilove_DC_cz | lpanda | Municipal Election Data: Jilove (DC, CZ) | data.frame | 745 | 14 |
| Kamenna_CB_cz | lpanda | Municipal Election Data: Kamenna (CB, CZ) | data.frame | 178 | 14 |
| Nebanice_CH_cz | lpanda | Municipal Election Data: Nebanice (CH, CZ) | data.frame | 136 | 14 |
| Potucky_KV_cz | lpanda | Municipal Election Data: Potucky (KV, CZ) | data.frame | 130 | 14 |
| Prameny_CH_cz | lpanda | Municipal Election Data: Prameny (CH, CZ) | data.frame | 117 | 14 |
| Roztoky_PZ_cz | lpanda | Municipal Election Data: Roztoky (PZ, CZ) | data.frame | 1079 | 14 |
| sample_binary_values | lpanda | Sample Dataset with Binary Values | tbl_df | 18 | 5 |
| sample_data | lpanda | Simple Sample Dataset | tbl_df | 18 | 5 |
| sample_different_varnames | lpanda | Sample Dataset with Some Different Variable Names | tbl_df | 18 | 5 |
| sample_no_continuity | lpanda | Sample Dataset Without Continuity Between Elections | tbl_df | 15 | 5 |
| sample_no_pluralism | lpanda | Sample Dataset Without Party Pluralism (only one candidate list per election) | tbl_df | 9 | 5 |
| sample_no_switching | lpanda | Sample Dataset Without Candidate Switching | tbl_df | 15 | 5 |
| Ustek_LT_cz | lpanda | Municipal Election Data: Ustek (LT, CZ) | data.frame | 742 | 14 |
| covidregionaldataUK | incidence2 | Regional data for COVID-19 cases in the UK | data.frame | 6370 | 13 |
| hSL | segmentSeq | Preprocessed 'lociData' object containing likelihoods of methylation at each locus. | lociData | | |
| battery | adas.utils | Battery experiment data | data.frame | 36 | 6 |
| ccd_experiment_yield | adas.utils | Central Composite Design Experiment Yields | list | | |
| cotton | adas.utils | Cotton yarn experiment data | data.frame | 25 | 3 |
| filtration | adas.utils | Filtration data | data.frame | 16 | 5 |
| tds | topdownr | TopDownSet Example Data | TopDownSet | | |
| gesthtn | medrobust | Gestational Hypertension as a Differentially Misclassified Binary Mediator | data.frame | 5000 | 4 |
| heals_data | medrobust | Synthetic HEALS Data with Differential Measurement Error | data.frame | 450 | 9 |
| nhanes_pa | medrobust | Physical Inactivity as a Differentially Misclassified Binary Exposure | data.frame | 9906 | 6 |
| DT | hwig | Example data for input to 'hwig' | data.table | 1857 | 3 |
| aecdop_2022 | prefviz | Distribution of preferences by candidate by division in the Australian Federal Election (2022 and 2025) | spec_tbl_df | 35096 | 14 |
| aecdop_2025 | prefviz | Distribution of preferences by candidate by division in the Australian Federal Election (2022 and 2025) | spec_tbl_df | 30888 | 14 |
| aecdop22_transformed | prefviz | Distribution of preferences in wide form for selected parties (2022 and 2025) | tbl_df | 1052 | 6 |
| aecdop25_transformed | prefviz | Distribution of preferences in wide form for selected parties (2022 and 2025) | tbl_df | 976 | 8 |
| elb_centroid | prefviz | Centroids of electoral divisions in the 2025 Australian Federal Election | data.frame | 150 | 6 |
| elb_map | prefviz | Electoral boundaries map for the 2025 Australian Federal Election | tbl_df | 125263 | 8 |
| pancreas | veloviz | Pancreas scRNA-seq data | list | | |
| vel | veloviz | MERFISH velocity subset | list | | |
| PK | guiplot | somedata | data.frame | 26 | 4 |
| Dm.tnumber | DTA | The amount of thymines in the cDNA of each transcript of Drosophila Melanogaster. | integer | | |
| Hs.datamat | DTA | Gene expression profiles of the Homo Sapiens DTA experiment from Doelken et al. | matrix | 19791 | 9 |
| Hs.enst2ensg | DTA | Mapping of Homo Sapiens gene and transcript identifiers. | character | | |
| Hs.phenomat | DTA | Design of the Homo Sapiens DTA experiment from Doelken et al. | matrix | 9 | 5 |
| Hs.reliable | DTA | Gene identifiers valid for parameter estimation from the Homo Sapiens Doelken et al. DTA experiment. | character | | |
| Hs.tnumber | DTA | The amount of thymines in the cDNA of each transcript of Homo Sapiens. | integer | | |
| Hs.tnumber | DTA | The amount of thymines in the cDNA of each transcript of Homo Sapiens. | integer | | |
| Mm.datamat | DTA | Gene expression profiles of the Mus Musculus DTA experiment from Doelken et al. | matrix | 16747 | 9 |
| Mm.enst2ensg | DTA | Mapping of Mus Musculus gene and transcript identifiers. | character | | |
| Mm.phenomat | DTA | Design of the Mus Musculus DTA experiment from Doelken et al. | matrix | 9 | 5 |
| Mm.reliable | DTA | Gene identifiers valid for parameter estimation from the Mus Musculus Doelken et al. DTA experiment. | character | | |
| Mm.tnumber | DTA | The amount of thymines in the cDNA of each transcript of Mus Musculus. | integer | | |
| Mm.tnumber | DTA | The amount of thymines in the cDNA of each transcript of Mus Musculus. | integer | | |
| Pol.phenomat | DTA | Design of the Saccharomyces Cerevisiae rpb1-N488D (Slow Polymerase) cDTA experiment from Sun et al. | matrix | 4 | 14 |
| Raw.datamat | DTA | Gene expression profiles of the Saccharomyces Cerevisiae rpb1-N488D (Slow Polymerase) and wild-type cDTA experiment from Sun et al. | matrix | 10849 | 8 |
| Sc.affy2ensg | DTA | Mapping of SaccharomycesCerevisiae Affymetrix Yeast 2.0 and gene identifiers. | character | | |
| Sc.datamat | DTA | Gene expression profiles of the Saccharomyces Cerevisiae wild-type DTA experiment from Miller et al. | matrix | 5976 | 12 |
| Sc.datamat.dynamic | DTA | Gene expression profiles of the Saccharomyces Cerevisiae salt stress DTA experiment from Miller et al. | matrix | 5976 | 24 |
| Sc.ensg.reliable | DTA | Gene identifiers valid for parameter estimation from the Saccharomyces Cerevisiae Sun et al. cDTA experiment. | character | | |
| Sc.phenomat | DTA | Design of the Saccharomyces Cerevisiae wild-type DTA experiment from Miller et al. | matrix | 12 | 4 |
| Sc.phenomat.dynamic | DTA | Design of the Saccharomyces Cerevisiae salt stress DTA experiment from Miller et al. | matrix | 24 | 11 |
| Sc.reliable | DTA | Gene identifiers valid for parameter estimation from the Saccharomyces Cerevisiae Miller et al. wild-type DTA experiment. | character | | |
| Sc.reliable.dynamic | DTA | Gene identifiers valid for parameter estimation from the Saccharomyces Cerevisiae Miller et al. salt stress DTA experiment. | character | | |
| Sc.ribig.ensg | DTA | Ribosome biogenesis genes. | character | | |
| Sc.rpg.ensg | DTA | Ribosomal protein genes. | character | | |
| Sc.stress.ensg | DTA | ISA stress module. | character | | |
| Sc.tf.ensg | DTA | Transcription factors. | character | | |
| Sc.tnumber | DTA | The amount of thymines in the cDNA of each transcript of Saccharomyces Cerevisiae. | integer | | |
| Sc.tnumber | DTA | The amount of thymines in the cDNA of each transcript of Saccharomyces Cerevisiae. | integer | | |
| Sc.tnumber | DTA | The amount of thymines in the cDNA of each transcript of Saccharomyces Cerevisiae. | integer | | |
| Sc.tnumber | DTA | The amount of thymines in the cDNA of each transcript of Saccharomyces Cerevisiae. | integer | | |
| Sp.affy.reliable | DTA | Gene identifiers valid for cDTA normalization from the Saccharomyces Cerevisiae Sun et al. cDTA experiment. | character | | |
| Sp.tnumber | DTA | The amount of thymines in the cDNA of each transcript of Schizosaccharomyces Pombe. | integer | | |
| Wt.phenomat | DTA | Design of the Saccharomyces Cerevisiae wild-type cDTA experiment from Sun et al. | matrix | 4 | 14 |
| liver | scHOT | Liver trajectory example data | list | | |
| MOB_subset | scHOT | MOB_subset spatial example data | list | | |
| fhs | bclogit | Framingham Heart Study Dataset | data.frame | 5942 | 39 |
| lungData | metagenomeSeq | OTU abundance matrix of samples from a smoker/non-smoker study | MRexperiment | | |
| mouseData | metagenomeSeq | OTU abundance matrix of mice samples from a diet longitudinal study | MRexperiment | | |
| slp_electricity_profiles | standardlastprofile | Standard Load Profile Data for Electricity from BDEW | data.frame | 26784 | 5 |
| example_many_sequences | ggDNAvis | Example multiple sequences data | data.frame | 23 | 10 |
| fastq_quality_scores | ggDNAvis | Vector of the quality scores used by the FASTQ format | character | | |
| sequence_col_palettes | ggDNAvis | Colour palettes for sequence visualisations | list | | |
| sequence_color_palettes | ggDNAvis | Colour palettes for sequence visualisations | list | | |
| sequence_colour_palettes | ggDNAvis | Colour palettes for sequence visualisations | list | | |
| ozturk | Davies | Parameters used in a paper by Ozturk | numeric | | |
| x00m700p4 | Davies | Peak concentration for 100 instantaneous releases | numeric | | |
| gsz | ivDiag | Data from GSZ (2016) | data.frame | 5357 | 11 |
| gsz_south | ivDiag | Data from GSZ (2016): Subsample | data.frame | 2175 | 11 |
| rueda | ivDiag | Data from Rueda (2017) | data.frame | 4352 | 6 |
| GO_gene_sets | gsEasy | GO term gene sets | list | | |
| compSPOT_example_mutations | compSPOT | Single Nucleotide Variants and Patient Features in Lung Cancer Patients | data.frame | 21225 | 11 |
| compSPOT_example_regions | compSPOT | Genomic Coordinates of Regions of Interest | data.frame | 200 | 4 |
| CODrecovery | EWSmethods | Three Recovering Cod Populations | list | | |
| simTransComms | EWSmethods | Three Simulated Transitioning Communities. | list | | |
| movies | ggplot2movies | Movie information and user ratings from IMDB.com. | tbl_df | 58788 | 24 |
| data_panel | EWS | Historical data for 13 OECD countries | data.frame | 6903 | 4 |
| data_USA | EWS | Historical data for the United States | data.frame | 268 | 5 |
| data.mccall93 | puniform | Data from a meta-analysis infants' habituation to a give stimulus and their later cognitive ability (IQ) | data.frame | 12 | 2 |
| melc_data | VoltRon | Example MELC Data | VoltRon | | |
| merged_object | VoltRon | Example Transfer Data | VoltRon | | |
| visium_data | VoltRon | Example Visium Data | VoltRon | | |
| xenium_data | VoltRon | Example Xenium Data | VoltRon | | |
| dfPrix_SP95_2016 | btb | Unleaded 95 price in France in 2016 | data.frame | 5573 | 3 |
| dfRestaurantParis | btb | Parisian restaurants | data.frame | 13823 | 17 |
| pixel_france | btb | France grid with 1km square tiles | data.frame | 548495 | 2 |
| reunion | btb | Households of Reunion | data.frame | 14076 | 4 |
| exprs | les | Spike-in ChIP-chip data set | matrix | 452 | 6 |
| pos | les | Spike-in ChIP-chip data set | integer | | |
| pval | les | Spike-in ChIP-chip data set | numeric | | |
| reference | les | Spike-in ChIP-chip data set | data.frame | 1 | 6 |
| PFF | MultiTraits | Plant Functional Traits Dataset from Ponderosa Pine Forests Flora (PFF) | data.frame | 133 | 21 |
| PFF_tree | MultiTraits | Phylogenetic Tree of 133 Plant Species from Pine Forest Flora | phylo | | |
| sampledata | epmfd | Example Polytomous Response Data | tbl_df | 660 | 7 |
| dfhera | africovid | subnational covid data from HERA | tbl_df | 64292 | 17 |
| elements | Rpdb | Periodic Table of the Elements | data.frame | 117 | 15 |
| universalConstants | Rpdb | Universal Constants | data.frame | 23 | 3 |
| york | maxcovr | York Listed Buildings. | tbl_df | 2944 | 7 |
| york_crime | maxcovr | York Crime Locations. | tbl_df | 1814 | 12 |
| Zaab | RHMS | datasets for Zaab subbasin, a subbasin in Kurdistan, Iran. | list | | |
| data_d_bb_multi | psychmeta | Hypothetical _d_ value dataset simulated with sampling error only | data.frame | 100 | 6 |
| data_d_meas_multi | psychmeta | Hypothetical _d_ value dataset simulated to satisfy the assumptions of the correction for measurement error only in multiple constructs | data.frame | 100 | 6 |
| data_r_bvdrr | psychmeta | Hypothetical dataset simulated to satisfy the assumptions of the bivariate correction for direct range restriction | data.frame | 100 | 8 |
| data_r_bvirr | psychmeta | Hypothetical dataset simulated to satisfy the assumptions of the bivariate correction for indirect range restriction | data.frame | 50 | 8 |
| data_r_gonzalezmule_2014 | psychmeta | Meta-analysis of OCB correlations with other constructs | data.frame | 43 | 10 |
| data_r_mcdaniel_1994 | psychmeta | Artifact-distribution meta-analysis of the validity of interviews | data.frame | 160 | 11 |
| data_r_mcleod_2007 | psychmeta | Bare-bones meta-analysis of parenting and childhood depression | data.frame | 45 | 12 |
| data_r_meas | psychmeta | Hypothetical dataset simulated to satisfy the assumptions of the correction for measurement error only | data.frame | 100 | 4 |
| data_r_meas_multi | psychmeta | Hypothetical correlation dataset simulated to satisfy the assumptions of the correction for measurement error only in multiple constructs | data.frame | 120 | 9 |
| data_r_oh_2009 | psychmeta | Second order meta-analysis of operational validities of big five personality measures across East Asian countries | data.frame | 20 | 6 |
| data_r_roth_2015 | psychmeta | Artifact-distribution meta-analysis of the correlation between school grades and cognitive ability | data.frame | 240 | 13 |
| data_r_uvdrr | psychmeta | Hypothetical dataset simulated to satisfy the assumptions of the univariate correction for direct range restriction | data.frame | 50 | 7 |
| data_r_uvirr | psychmeta | Hypothetical dataset simulated to satisfy the assumptions of the univariate correction for indirect range restriction | data.frame | 20 | 7 |
| breast | success | Survival after breast cancer surgery | data.frame | 2663 | 11 |
| surgerydat | success | Simulated data set with data of surgery procedures performed at multiple hospitals. | data.frame | 32529 | 9 |
| eusilcA_pop | saeTrafo | Simulated eusilc data - population data | data.frame | 25000 | 17 |
| eusilcA_smp | saeTrafo | Simulated eusilc data - sample data | data.frame | 1945 | 18 |
| pop_area_size | saeTrafo | Aggregates from simulated eusilc population data: domain sizes | numeric | | |
| pop_cov | saeTrafo | Aggregates from simulated eusilc population data: domain-specific covariances | list | | |
| pop_mean | saeTrafo | Aggregates from simulated eusilc population data: domain-specific means | list | | |
| graph20 | NetworkDistance | 20 adjacency matrices from Erdős–Rényi models | list | | |
| age_freq | foodquotient | Frequency Factors for American Children with Age of Participant | data.frame | 32 | 86 |
| freq | foodquotient | Frequency Factors for American Children | data.frame | 32 | 85 |
| hsffq | foodquotient | Harvard Foood Frequency Questionnaire Nutrition Information | spec_tbl_df | 85 | 15 |
| biomass | VisCollin | Biomass Production in the Cape Fear Estuary | data.frame | 45 | 17 |
| cars | VisCollin | Cars Data | data.frame | 406 | 10 |
| consumption | VisCollin | Consumption Function Dataset | data.frame | 28 | 5 |
| sample_income_data | GiniDecompLY | Sample income data | spec_tbl_df | 200 | 6 |
| alces | carbondate | Example real-life data - Alces in Yukon and Alaska | data.frame | 58 | 7 |
| armit | carbondate | Example real-life data - Population Decline in Iron Age Ireland | data.frame | 2021 | 4 |
| bison | carbondate | Example real-life data - Bison in Yukon and Alaska | data.frame | 64 | 7 |
| buchanan | carbondate | Example real-life data - Palaeo-Indian demography | data.frame | 628 | 4 |
| cervus | carbondate | Example real-life data - Cervus in Yukon and Alaska | data.frame | 63 | 7 |
| equus | carbondate | Example real-life data - Equus in Yukon and Alaska | data.frame | 84 | 7 |
| human | carbondate | Example real-life data - Humans in Yukon and Alaska | data.frame | 46 | 7 |
| intcal04 | carbondate | IntCal04 calibration curve | data.frame | 3301 | 5 |
| intcal09 | carbondate | IntCal09 calibration curve | data.frame | 3521 | 5 |
| intcal13 | carbondate | IntCal13 calibration curve | data.frame | 5141 | 5 |
| intcal20 | carbondate | IntCal20 calibration curve | data.frame | 9501 | 5 |
| intcal98 | carbondate | IntCal98 calibration curve | data.frame | 1538 | 5 |
| kerr | carbondate | Example real-life data - Irish Rath | data.frame | 255 | 4 |
| mammuthus | carbondate | Example real-life data - Mammuthus in Yukon and Alaska | data.frame | 117 | 7 |
| marine04 | carbondate | Marine04 calibration curve | data.frame | 3301 | 5 |
| marine09 | carbondate | Marine09 calibration curve | data.frame | 3651 | 5 |
| marine13 | carbondate | Marine13 calibration curve | data.frame | 4801 | 5 |
| marine20 | carbondate | Marine20 calibration curve | data.frame | 5501 | 5 |
| pp_uniform_phase | carbondate | Example artificial data - Uniform Phase | data.frame | 40 | 4 |
| pp_uniform_phase_marine | carbondate | Example artificial marine data - Uniform Phase | data.frame | 40 | 7 |
| pp_uniform_phase_mixed | carbondate | Example artificial data requiring multiple calibration curves - Uniform Phase | data.frame | 40 | 7 |
| shcal04 | carbondate | SHCal04 calibration curve | data.frame | 2202 | 5 |
| shcal13 | carbondate | SHCal13 calibration curve | data.frame | 5141 | 5 |
| shcal20 | carbondate | SHCal20 calibration curve | data.frame | 9501 | 5 |
| two_normals | carbondate | Example artificial data - Mixture of Normal Phases | data.frame | 50 | 4 |
| two_normals_marine | carbondate | Example artificial data - Mixture of Normal Phases Using Marine20 calibration curve | data.frame | 50 | 7 |
| arabiya_text | quiltr | Arabiya politics text from SANAD dataset | data.frame | 100 | 1 |
| lipsum_text | quiltr | Lipsum text from 'stringi' | data.frame | 100 | 1 |
| qdat | quiltr | Dataset generated with 'quilt_form_data' | data.frame | 100 | 3 |
| r2q_pal | r2q | Color palette for R2Q Plots | data.frame | 6 | 3 |
| Data2010 | SCDA | Spatial dataset to replicate the results for 2010 from Cerqueti, R., Maranzano, P. & Mattera, R. "Spatially-clustered spatial autoregressive models with application to agricultural market concentration in Europe". arXiv preprints (<https://doi.org/10.48550/arXiv.2407.15874> | sf | 222 | 14 |
| Data2020 | SCDA | Spatial dataset to replicate the results for 2020 from Cerqueti, R., Maranzano, P. & Mattera, R. "Spatially-clustered spatial autoregressive models with application to agricultural market concentration in Europe". arXiv preprints (<https://doi.org/10.48550/arXiv.2407.15874>) | sf | 222 | 14 |
| listW | SCDA | List of 222 spatial weights (style = "W", zero.policy=TRUE) used in Cerqueti, R., Maranzano, P. & Mattera, R. "Spatially-clustered spatial autoregressive models with application to agricultural market concentration in Europe". arXiv preprints (<https://doi.org/10.48550/arXiv.2407.15874>) | listw | | |
| running_example_ggmap | activatr | Precomputed example ggmap | ggmap | 1280 | |
| caradat | lomb | Locomotor activiy of a Carabid beetle. | data.frame | 2014 | 2 |
| deerdat | lomb | Locomotor activiy of a deer. | data.frame | 293826 | 9 |
| ibex | lomb | Rumen Temperature In An Alpine Ibex | data.frame | 1201 | 3 |
| layla | lomb | Activiy of a dog. | data.frame | 10120 | 4 |
| coordinate_example | inborutils | Example 'data.frame' with coordinates | data.frame | 52 | 3 |
| rain_knmi_2012 | inborutils | Example 'data.frame' with 'KNMI' downloaded data | data.frame | 1536 | 9 |
| species_example | inborutils | Example 'data.frame' with species name column | data.frame | 3 | 3 |
| waterlevels | Tides | Observed water levels of the tides in the Lippenbroek Flood Control Area with controled reduced tide (FCA-CRT) | data.frame | 8915 | 3 |
| ozone20km | pargasite | Ozone concentration data | stars | | |
| dat | edl | Simulated learning data. | data.frame | 36 | 5 |
| Bank_Branch_Operating_Efficiency | MultiplierDEA | Data: Bank Branch Operating Efficiency data | tbl_df | 17 | 8 |
| BenchMark_Tests_And_Microcomputer | MultiplierDEA | Data: Relationship between benchmark tests and Microcomputer price data | tbl_df | 22 | 9 |
| Data_City | MultiplierDEA | Data: City data | tbl_df | 15 | 8 |
| Departments_Of_Accounting | MultiplierDEA | Data: UK University Departments Of Accounting Efficiency data. | tbl_df | 20 | 11 |
| Evaluation_Educational_Program | MultiplierDEA | Data: Educational program data | tbl_df | 22 | 9 |
| Evaluations_Of_NonProfitOrganizations | MultiplierDEA | Data: Evaluation of Non-Profit organizations data | tbl_df | 16 | 7 |
| Japanese_Companies | MultiplierDEA | Data: Japanese Companies data. | tbl_df | 20 | 6 |
| Metropolitan_And_London_Rates_Departments | MultiplierDEA | Data: Metropolitan and London rates departments data | tbl_df | 62 | 6 |
| ex.data.3 | longRPart2 | A dataset used as an example for longRPart2 | data.frame | 600 | 4 |
| lcart.mod1 | longRPart2 | Longitudinal data with groups | lrp | | |
| allo | corona | Allometric scaling data. | data.frame | 455 | 7 |
| citymap | corona | Citymapper data. | data.frame | 108 | 22 |
| cntry | corona | Country data from Our World In Data. | data.frame | 213 | 20 |
| djia | corona | Historical Dow Jones Industrial Average prices. | data.frame | 5156 | 5 |
| gt | corona | Google trends search for 'coronavirus'. | data.frame | 155 | 3 |
| life | corona | The game of life. | data.frame | 213 | 3 |
| lock | corona | Approximate dates of full lockdown in various countries. | data.frame | 110 | 3 |
| owid | corona | Wide-ranging data from Our World In Data. I only use a tiny part. | data.frame | 27193 | 10 |
| stmf | corona | Deaths, by week, for various countries. | data.frame | 22676 | 5 |
| vienna | corona | Semmelweis' data on Deaths of parturients in Vienna | data.frame | 98 | 3 |
| agreem_binary | kappaGold | Three reliability studies for some binary rating | list | | |
| depression | kappaGold | Depression screening | matrix | 50 | 3 |
| diagnoses | kappaGold | Psychiatric diagnoses | matrix | 30 | 6 |
| SC_test | kappaGold | Script concordance test (SCT). | matrix | 34 | 50 |
| stagingData | kappaGold | Staging of colorectal carcinoma | data.frame | 21 | 6 |
| electricity | nixtlar | Electricity dataset | data.frame | 8400 | 3 |
| electricity_exo_vars | nixtlar | Electricity dataset with exogenous variables | data.frame | 8400 | 12 |
| electricity_future_exo_vars | nixtlar | Future values for the electricity dataset with exogenous variables | data.frame | 120 | 11 |
| kyoto_districts | kyotocities | Kyoto prefecture administrative district data | sf | 36 | 5 |
| kyoto_fire_stations | kyotocities | Kyoto Fire Station Data | sf | 122 | 2 |
| dhb_cars | DHBins | Cars per household in New Zealand | data.frame | 20 | 5 |
| immune | DHBins | Immunisation coverage | data.frame | 20 | 19 |
| immune_long | DHBins | Immunisation coverage | data.frame | 120 | 3 |
| region_ethnicity | DHBins | Ethnic makeup of New Zealand, by region | data.frame | 16 | 6 |
| gene_edges | node2vec | 6 edges information between two genes of human | data.frame | 6 | 2 |
| argo_floats_atlantic_2003 | bmstdr | Temperature and salinity data from Argo floats in the North Atlantic Ocean at three layers of depth: surface (less than 50 meters), mid-layer (between 475-525 meters) and deep (975 to 1025 meters) during 2003. | data.frame | 6978 | 11 |
| colpalette | bmstdr | The color palette used to draw maps to illustrate the package bmstdr, see Sahu (2022) It has the values in order: dodgerblue4, dodgerblue2, firebrick2, firebrick4 and purple. | character | | |
| engdeaths | bmstdr | Number of weekly Covid-19 deaths and cases in the 313 local Local Authority Districts, Counties and Unitary Authorities (LADCUA) in England during the 20 peaks in the first peak from March 13 to July 31, 2020. | data.frame | 6260 | 24 |
| engtotals | bmstdr | Total number of weekly Covid-19 deaths and cases in the 313 local Local Authority Districts, Counties and Unitary Authorities (LADCUA) in England during the first peak from March 13 to July 31, 2020. | data.frame | 313 | 19 |
| gridnyspatial | bmstdr | Values of three covariates for 100 grid locations in New York averaged over the 62 days during the months of July and August, 2006. | data.frame | 100 | 8 |
| gridnysptime | bmstdr | Values of three covariates for 100 grid locations in New York for the 62 days during the months of July and August, 2006. | data.frame | 6200 | 11 |
| nyspatial | bmstdr | Average ozone concentration values and three covariates from 28 sites in New York. | data.frame | 28 | 9 |
| nysptime | bmstdr | Daily 8-hour maximum ozone concentration values and three covariates from 28 sites in New York for the 62 days during the months of July and August, 2006. | data.frame | 1736 | 12 |
| Weng | bmstdr | A 313 by 313 proximity matrix for the 313 LADCUAS in England. Each entry is either 0 or 1 and is 1 if the corresponding row and column LADCUAs share a common boundary. | matrix | 313 | |
| ydata | bmstdr | Average air pollution values from 28 sites in New York. | numeric | | |
| leuk | bpcp | Acute Leukemia data (treatment only) from Freireich et al (1963). | data.frame | 21 | 2 |
| leuk2 | bpcp | Acute Leukemia data from Freireich et al (1963). | data.frame | 42 | 4 |
| sclerosis | bpcp | Pilot study of treatment of severe systemic sclerosis (Nash, et al, 2007). | data.frame | 34 | 3 |
| dataset_size | onsr | The Dataset with a size column | data.frame | 41 | 3 |
| cat.params | SimTimeVar | An example dataframe for categorical variable parameters | data.frame | 5 | 3 |
| params | SimTimeVar | An example parameters dataframe | data.frame | 12 | 8 |
| pcor | SimTimeVar | An example across-cluster correlation dataframe | data.frame | 9 | 9 |
| wcor | SimTimeVar | An example within-cluster correlation dataframe | data.frame | 6 | 6 |
| ama1c1cpg | asht | Three arm phase 1 malaria vaccine trial | data.frame | 58 | 2 |
| colon | biglasso | Gene expression data from colon-cancer patients | list | | |
| ASVAB | mirt | Description of ASVAB data | data.frame | 16 | 8 |
| Attitude | mirt | Description of Attitude data | data.frame | 22 | 9 |
| Bock1997 | mirt | Description of Bock 1997 data | data.frame | 64 | 4 |
| deAyala | mirt | Description of deAyala data | matrix | 32 | 6 |
| LSAT6 | mirt | Description of LSAT6 data | data.frame | 30 | 6 |
| LSAT7 | mirt | Description of LSAT7 data | data.frame | 32 | 6 |
| pirt_DIF | mirt | Simulated datasets for PIRT-DIF | list | | |
| SAT12 | mirt | Description of SAT12 data | data.frame | 600 | 32 |
| Science | mirt | Description of Science data | data.frame | 392 | 4 |
| SLF | mirt | Social Life Feelings Data | data.frame | 32 | 6 |
| aids | JMbayes | Didanosine versus Zalcitabine in HIV Patients | data.frame | 1405 | 12 |
| aids.id | JMbayes | Didanosine versus Zalcitabine in HIV Patients | data.frame | 467 | 12 |
| pbc2 | JMbayes | Mayo Clinic Primary Biliary Cirrhosis Data | data.frame | 1945 | 20 |
| pbc2.id | JMbayes | Mayo Clinic Primary Biliary Cirrhosis Data | data.frame | 312 | 20 |
| prothro | JMbayes | Prednisone versus Placebo in Liver Cirrhosis Patients | data.frame | 2968 | 9 |
| prothros | JMbayes | Prednisone versus Placebo in Liver Cirrhosis Patients | data.frame | 488 | 4 |
| FACSdata | EDOtrans | Example data of hematologic marker expression. | data.frame | 3000 | 5 |
| GMMartificialData | EDOtrans | Example data an artificial Gaussioan mixture. | data.frame | 1000 | 5 |
| Anxiety | lordif | A Measure of Anxiety | data.frame | 766 | 32 |
| data_Field | EFA.dimensions | data_Field | data.frame | 2571 | 23 |
| data_Harman | EFA.dimensions | Correlation matrix from Harman (1967, p. 80). | matrix | 8 | 8 |
| data_NEOPIR | EFA.dimensions | data_NEOPIR | data.frame | 1000 | 30 |
| data_RSE | EFA.dimensions | Item-level dataset for the Rosenberg Self-Esteem scale | data.frame | 800 | 10 |
| data_RSE_not_recoded | EFA.dimensions | Item-level dataset for the Rosenberg Self-Esteem scale | data.frame | 300 | 10 |
| data_RSE_sex | EFA.dimensions | Item-level dataset for the Rosenberg Self-Esteem scale plus gender | data.frame | 800 | 11 |
| data_TabFid | EFA.dimensions | data_TabFid | data.frame | 340 | 44 |
| IR_diabetes | icsp2 | Interval censored time from diabetes onset to diabetic nephronpathy | data.frame | 731 | 3 |
| miceData | icsp2 | Lung Tumor Interval Censored Data from Hoel and Walburg 1972 | data.frame | 144 | 3 |
| xt_example | rtemis | Example longitudinal dataset | data.frame | 30 | 4 |
| bballHR | assessor | MLB Players' Home Run and Batted Ball Statistics with Red Zone Metrics (2017-2019) | data.frame | 400 | 17 |
| LGPIF | assessor | LGPIF Data | data.frame | 5677 | 41 |
| MEPS | assessor | Healthcare expenditure data | data.frame | 29784 | 29 |
| mimeTypeExtensions | RCurl | Mapping from extension to MIME type | character | | |
| example_causality | grangersearch | Example Time Series Data with Known Causal Relationship | data.frame | 200 | 3 |
| bioclim_example | corrselect | Example Bioclimatic Data for Ecological Modeling | data.frame | 100 | 20 |
| cor_example | corrselect | Example Correlation Matrix with Block Structure | matrix | 20 | 20 |
| genes_example | corrselect | Example Gene Expression Data for Bioinformatics | data.frame | 100 | 202 |
| longitudinal_example | corrselect | Example Longitudinal Data for Clinical Research | data.frame | 500 | 25 |
| survey_example | corrselect | Example Survey Data for Social Science Research | data.frame | 200 | 35 |
| left_hippocampus_mask | ravetools | Left 'Hippocampus' of 'N27-Collin' brain | array | | |
| stimulation_signal | ravetools | Sample stimulation recording | list | | |
| WoSdata | xxdi | WoSdata | tbl_df | 2355 | 4 |
| Big5 | jmv | | data.frame | 500 | 5 |
| bugs | jmv | | data.frame | 93 | 8 |
| iris | jmv | | data.frame | 150 | 5 |
| ToothGrowth | jmv | | data.frame | 60 | 3 |
| allom.components | PEcAn.allometry | | data.frame | 41 | 3 |
| Jenkins2004_Table9 | PEcAn.allometry | | data.frame | 174 | 1 |
| Table3_GTR-NE-319.v2 | PEcAn.allometry | | data.frame | 2642 | 1 |
| example_data | ICEHmeasures | Example dataset | tbl_df | 500 | 5 |
| example_data2 | ICEHmeasures | Example dataset 2 | tbl_df | 38 | 3 |
| ecoli | MoTBFs | Data set Ecoli: Protein Localization Sites | data.frame | 336 | 9 |
| thyroid | MoTBFs | Data set Thyroid Disease (thyroid0387) | data.frame | 7200 | 22 |
| hexify_world | hexify | Simplified World Map | sf | 242 | 16 |
| cube34landm | Anthropometry | Cube of 34 landmarks | array | | |
| cube8landm | Anthropometry | Cube of 8 landmarks | array | | |
| descrDissTrunks | Anthropometry | Description of the dissimilarities between women's trunks | matrix | 470 | 4 |
| landmarksSampleSpaSurv | Anthropometry | Landmarks of the sampled women of the Spanish Survey | data.frame | 600 | 198 |
| parallelep34landm | Anthropometry | Parallelepiped of 34 landmarks | array | | |
| parallelep8landm | Anthropometry | Parallelepiped of 8 landmarks | array | | |
| sampleSpanishSurvey | Anthropometry | Sample database of the Spanish anthropometric survey | data.frame | 600 | 5 |
| USAFSurvey | Anthropometry | USAF 1967 survey | data.frame | 2420 | 202 |
| nyc_ct | pycnogrid | New York City census tract example data | sf | 2324 | 4 |
| nyc_ct_small | pycnogrid | Small New York City census tract example data | sf | 26 | 4 |
| Baystudy | LTMRdata | Bay study dataset | tbl_df | 805858 | 23 |
| Baystudy_measured_lengths | LTMRdata | Baystudy measured lengths | data.frame | 802128 | 5 |
| DJFMP | LTMRdata | DJFMP dataset | tbl_df | 2248199 | 19 |
| EDSM | LTMRdata | EDSM dataset | tbl_df | 190055 | 20 |
| FMWT | LTMRdata | FMWT dataset | tbl_df | 266694 | 23 |
| FMWT_measured_lengths | LTMRdata | FMWT measured lengths | tbl_df | 233902 | 4 |
| Length_conversions | LTMRdata | Length conversions | tbl_df | 20 | 3 |
| Salvage | LTMRdata | Salvage dataset | tbl_df | 2734547 | 15 |
| Salvage_measured_lengths | LTMRdata | Salvage measured lengths | tbl_df | 1904345 | 4 |
| SKT | LTMRdata | SKT dataset | data.frame | 42909 | 22 |
| SKT_measured_lengths | LTMRdata | SKT measured lengths | tbl_df | 41295 | 4 |
| SLS | LTMRdata | SLS dataset | data.frame | 27851 | 26 |
| Species | LTMRdata | Species codes | tbl_df | 289 | 13 |
| STN | LTMRdata | STN dataset | tbl_df | 191547 | 22 |
| STN_measured_lengths | LTMRdata | STN measured lengths | tbl_df | 173981 | 4 |
| Suisun | LTMRdata | Suisun Marsh dataset | tbl_df | 202676 | 21 |
| Suisun_measured_lengths | LTMRdata | Suisun measured lengths | data.frame | 173209 | 5 |
| TMM | LTMRdata | 20mm dataset | data.frame | 355228 | 23 |
| TMM_measured_lengths | LTMRdata | 20mm measured lengths | data.frame | 351699 | 4 |
| dat | adlaplace | Example GAMM simulation data | data.frame | 500 | 13 |
| germany | adlaplace | Germany oral cavity cancer (Besag-York-Mollie example) | list | | |
| london | adlaplace | London daily mortality and PM10 (case-crossover example) | data.frame | 1826 | 10 |
| historico_pesquisas_poder360 | agregR | Historical Polls by Poder360 | spec_tbl_df | 166292 | 24 |
| jagst | webchem | Organic plant protection products in the river Jagst / Germany in 2013 | data.frame | 442 | 4 |
| lc50 | webchem | Acute toxicity data from U.S. EPA ECOTOX | data.frame | 124 | 2 |
| DAX | PMwR | Deutscher Aktienindex (DAX) | data.frame | 505 | 1 |
| REXP | PMwR | REXP | data.frame | 502 | 1 |
| data_small | susieR | Simulated Small-sample eQTL Data. | list | | |
| FinemappingConvergence | susieR | Simulated Fine-mapping Data with Convergence Problem. | list | | |
| N2finemapping | susieR | Simulated Fine-mapping Data with Two Effect Variables | list | | |
| N3finemapping | susieR | Simulated Fine-mapping Data with Three Effect Variables. | list | | |
| rss_mismatch_example | susieR | Real-data SuSiE-RSS example with R-reference mismatch. | list | | |
| SummaryConsistency | susieR | Simulated Fine-mapping Data with LD matrix From Reference Panel. | list | | |
| unmappable_data | susieR | Simulated Fine-mapping Data with Sparse, Oligogenic and Polygenic Effects. | list | | |
| metals | qgcomp | Well water data | data.frame | 452 | 26 |
| tximeta_summarizeToGene_object | tidybulk | Needed for tests tximeta_summarizeToGene_object, It is SummarizedExperiment from tximeta | RangedSummarizedExperiment | | |
| vignette_manuscript_signature_boxplot | tidybulk | Needed for vignette vignette_manuscript_signature_boxplot | tbl_df | 899 | 12 |
| vignette_manuscript_signature_tsne | tidybulk | Needed for vignette vignette_manuscript_signature_tsne | spec_tbl_df | 283 | 10 |
| vignette_manuscript_signature_tsne2 | tidybulk | Needed for vignette vignette_manuscript_signature_tsne2 | tbl_df | 283 | 9 |
| X_cibersort | tidybulk | Cibersort reference | data.frame | 547 | 22 |
| game_revenue | pointblank | A table with game revenue data | tbl_df | 2000 | 11 |
| game_revenue_info | pointblank | A table with metadata for the 'game_revenue' dataset | tbl_df | 11 | 2 |
| small_table | pointblank | A small table that is useful for testing | spec_tbl_df | 13 | 8 |
| specifications | pointblank | A table containing data pertaining to various specifications | tbl_df | 8 | 12 |
| shen_orr_ex | dtangle | Example Subset of Shen-Orr deconvolution data set. | list | | |
| dt_potato | flexFitR | Drone-derived data from a potato breeding trial (2020) | tbl_df | 1568 | 8 |
| dt_potato_22 | flexFitR | Drone-derived data from a potato breeding trial (2022) | tbl_df | 1764 | 9 |
| dt_soybean_22 | flexFitR | Soybean canopy cover from HTP platform (Keller et all., 2026) | spec_tbl_df | 2793 | 7 |
| dat.sim | miapack | Simulated data set | data.frame | 9297 | 4 |
| BAF | copynumber | Artificial SNP array data | data.frame | 10000 | 4 |
| logR | copynumber | Artificial SNP array data | data.frame | 10000 | 4 |
| lymphoma | copynumber | 3K aCGH data | data.frame | 3091 | 23 |
| micma | copynumber | Subset of 244K aCGH data | data.frame | 7658 | 8 |
| IvesGodfray | picante | Host-parasitoid food web data | list | | |
| phylocom | picante | Phylocom default data | list | | |
| degpatterns_dex | carnation | A 'degPatterns' object for differentially expressed genes in the dexamethasone treatment comparison. | list | | |
| eres_cell | carnation | An 'enrichResult' object for differentially expressed genes in the cell line comparison. | enrichResult | | |
| eres_dex | carnation | An 'enrichResult' object for differentially expressed genes in the dexamethasone treatment comparison. | enrichResult | | |
| res_cell | carnation | A 'DESeqResults' object testing the difference between two cell lines of smooth muscle cells | DESeqResults | | |
| res_dex | carnation | A 'DESeqResults' object testing the effect of dexamethasone on smooth muscle cells | DESeqResults | | |
| Arabidopsis | lme4 | Arabidopsis clipping/fertilization data | data.frame | 625 | 8 |
| cake | lme4 | Breakage Angle of Chocolate Cakes | data.frame | 270 | 5 |
| cbpp | lme4 | Contagious bovine pleuropneumonia | data.frame | 56 | 4 |
| cbpp2 | lme4 | Contagious bovine pleuropneumonia | data.frame | 56 | 6 |
| culcitalogreg | lme4 | Coral-eating seastar Culcita novaeguineae data (binary predation version) | data.frame | 80 | 9 |
| culcitalvolume | lme4 | Coral-eating seastar Culcita novaeguineae data (volume loss version) | data.frame | 50 | 7 |
| Dyestuff | lme4 | Yield of dyestuff by batch | data.frame | 30 | 2 |
| Dyestuff2 | lme4 | Yield of dyestuff by batch | data.frame | 30 | 2 |
| gopherdat2 | lme4 | Gopher tortoises shell remains | data.frame | 30 | 6 |
| grouseticks | lme4 | Data on red grouse ticks from Elston et al. 2001 | data.frame | 403 | 7 |
| grouseticks_agg | lme4 | Data on red grouse ticks from Elston et al. 2001 | data.frame | 118 | 7 |
| InstEval | lme4 | University Lecture/Instructor Evaluations by Students at ETH | data.frame | 73421 | 7 |
| Pastes | lme4 | Paste strength by batch and cask | data.frame | 60 | 4 |
| Penicillin | lme4 | Variation in penicillin testing | data.frame | 144 | 3 |
| salamander | lme4 | Mountain dusky salamander mating | data.frame | 360 | 8 |
| schizophrenia | lme4 | National Institute of Mental Health Schizophrenia Collaborative Study | data.frame | 1603 | 9 |
| sleepstudy | lme4 | Reaction times in a sleep deprivation study | data.frame | 180 | 3 |
| toenail | lme4 | Toenail onychomycosis data from dermatophyte infections | data.frame | 1908 | 5 |
| VerbAgg | lme4 | Verbal Aggression item responses | data.frame | 7584 | 9 |
| bearingcage | fwb | Bearing Cage field failure data | data.frame | 1703 | 2 |
| lalonde | MatchIt | Data from National Supported Work Demonstration and PSID, as analyzed by Dehejia and Wahba (1999). | data.frame | 614 | 9 |
| spdx_licenses | xmpdf | SPDX License List data | data.frame | 478 | 8 |
| data_birthwt | gtregression | Birth Weight Data | data.frame | 189 | 10 |
| data_diabetes_mediation | gtregression | Diabetes Mediation Teaching Dataset | data.frame | 727 | 8 |
| data_endometrial | gtregression | Endometrial Cancer Histology Grade Data | data.frame | 79 | 4 |
| data_epilepsy | gtregression | Epilepsy Treatment and Seizure Counts | data.frame | 236 | 9 |
| data_gt_quin | gtregression | Student Absenteeism in Rural Schools | data.frame | 146 | 5 |
| data_infertility | gtregression | Infertility Matched Case-Control Study | data.frame | 248 | 8 |
| data_lungcancer | gtregression | Lung Cancer Trial Data | data.frame | 137 | 8 |
| data_SynthDiabetes | gtregression | Synthetic Diabetes Dataset | data.frame | 768 | 9 |
| ensembl_versions | biomaRt | Mapping of Ensembl versions to their release dates | data.frame | 35 | 5 |
| time_to_million | censored | Number of days before a movie grosses $1M USD | tbl_df | 551 | 49 |
| CLUSexample | roads | Data from the CLUS example | list | | |
| dem_example | roads | Grade penalty example data | list | | |
| demoScen | roads | Demonstration set of 10 input scenarios | list | | |
| mpdta | did | County Teen Employment Dataset | data.frame | 2500 | 6 |
| delaware | idealstan | Rollcall vote data for Delaware State Legislature | tbl_df | 186183 | 5 |
| senate114 | idealstan | Rollcall vote data for 114th Senate | tbl_df | 24000 | 39 |
| sepsis | phoenix | sepsis | data.frame | 20 | 27 |
| lalonde.exp | qte | Lalonde's Experimental Dataset | data.frame | 445 | 13 |
| lalonde.exp.panel | qte | Lalonde's Panel Experimental Dataset | data.frame | 1335 | 13 |
| lalonde.psid | qte | Lalonde's Observational Dataset | data.frame | 2675 | 13 |
| lalonde.psid.panel | qte | Lalonde's Panel Observational Dataset | data.frame | 8025 | 13 |
| covid_data | ptetools | State-level Covid-19 Data | grouped_df | 1656 | 9 |
| hmp.u01.gc.coarse | grip | HMP/U01 Coarsened Giant-Component Graph | list | | |
| brazil_agriculture | ekioplot | Brazilian Municipal Agricultural Production (2022) | tbl_df | 16689 | 12 |
| brazil_agriculture_states | ekioplot | Brazilian State-Level Agricultural Production Time Series (1974-2023) | tbl_df | 9450 | 8 |
| brazil_gdp | ekioplot | Brazilian Municipal GDP and Economic Structure (2021) | tbl_df | 5570 | 7 |
| brazil_population | ekioplot | Brazilian Municipal Population Data (2025) | tbl_df | 338 | 5 |
| fuels | ekioplot | Global Fuel Consumption by Source | tbl_df | 228 | 4 |
| ips_brasil | ekioplot | IPS Brasil 2025 - Social Progress Index for Brazilian Municipalities | tbl_df | 200 | 9 |
| raman_hdpe | OpenSpecy | Sample Raman spectrum | OpenSpecy | | |
| test_lib | OpenSpecy | Test reference library | OpenSpecy | | |
| matStig | expm | Stig's "infamous" Example Matrix | matrix | 8 | |
| gpcr | bios2mds | Pre-analyzed G-Protein-Coupled Receptor (GPCR) data set | list | | |
| sub.mat | bios2mds | Amino acid substitution matrices | list | | |
| nhanes_measure_data | mapnhanespa | NHANES PA data | tbl_df | 112653 | 10 |
| ebirdst_predictor_descriptions | ebirdst | eBird Status and Trends predictors descriptions | tbl_df | 42 | 4 |
| ebirdst_predictors | ebirdst | eBird Status and Trends predictor variables | tbl_df | 153 | 4 |
| ebirdst_runs | ebirdst | Data frame of species with eBird Status and Trends Data Products | tbl_df | 2981 | 30 |
| ashua | GLBFP | River Ashuapmushuan daily flow and level data | data.frame | 4389 | 3 |
| bear_lake | awdb | Datasets | sfc_POLYGON | | |
| cascades | awdb | Datasets | sfc_POLYGON | | |
| metalite_ae_adex | metalite.ae | ADEX dataset | data.frame | 591 | 41 |
| metalite_ae_adexsum | metalite.ae | ADEXSUM dataset | tbl_df | 508 | 50 |
| attrition_experiment | vayr | Simulated experiment encountering attrition | tbl_df | 200 | 10 |
| blocked_experiment | vayr | Simulated block-randomized experiment | tbl_df | 150 | 9 |
| clustered_experiment | vayr | Simulated cluster-randomized experiment | tbl_df | 441 | 11 |
| continuous_interaction | vayr | Simulated experiment whose effect varies with a continuous covariate | tbl_df | 1189 | 10 |
| covariate_adjustment | vayr | Simulated experiment with a pretreatment covariate | tbl_df | 100 | 9 |
| noncompliance_experiment | vayr | Simulated experiment with two-sided noncompliance | tbl_df | 600 | 3 |
| patriot_act | vayr | Original and Replication data for the Patriot Act experiment described in Persuasion in Parallel | tbl_df | 2062 | 4 |
| two_arm_trial | vayr | Simulated two-arm trial | tbl_df | 500 | 8 |
| dyads_cross | dyadMLM | Example Gaussian cross-sectional dyadic data | tbl_df | 720 | 5 |
| dyads_ild | dyadMLM | Example Gaussian intensive longitudinal dyadic data | tbl_df | 10080 | 6 |
| dyads_nbinom_cross | dyadMLM | Example negative-binomial cross-sectional dyadic data | tbl_df | 720 | 5 |
| dyads_nbinom_ild | dyadMLM | Example negative-binomial intensive longitudinal dyadic data | tbl_df | 10080 | 6 |
| lutkepohl_e1 | combcoint | Economic data from West Germany | spec_tbl_df | 92 | 10 |
| media_diary | mlstats | Simulated daily diary study: entertainment media use and wellbeing | tbl_df | 1184 | 6 |
| Industry_10 | RiskPortfolios | Industry Portfolios | matrix | 252 | 10 |
| translategemma_languages | quackingllama | | tbl_df | 581 | 2 |
| locations_example | geoprofiler | Example 'sf' data set | sf | 495 | 2 |
| raster_example | geoprofiler | Example raster data set | matrix | 126 | 3 |
| rain | isodistrreg | Frankfurt airport precipitation data | data.frame | 3617 | 54 |
| COVID19 | Dyn4cast | Dynamic Forecast of Five Models and their Ensembles | spec_tbl_df | 348 | 2 |
| Data | Dyn4cast | Collection of Machine Learning Model Metrics for Easy Reference | spec_tbl_df | 200 | 3 |
| garrett_data | Dyn4cast | Garrett Ranking of Categorical Data | spec_tbl_df | 29 | 15 |
| garrett_table | Dyn4cast | Garrett Ranking of Categorical Data | spec_tbl_df | 100 | 3 |
| linearsystems | Dyn4cast | Linear Model and various Transformations for Efficiency | spec_tbl_df | 100 | 9 |
| mdpi1 | Dyn4cast | Sequential Computation of Dynamic Multidimensional Indices (MDI) | spec_tbl_df | 30 | 12 |
| mdpi2 | Dyn4cast | Sequential Computation of Dynamic Multidimensional Indices (MDI) | spec_tbl_df | 14903 | 17 |
| Quicksummary | Dyn4cast | Quick Formatted Summary of Machine Learning Data | spec_tbl_df | 103 | 29 |
| sampling | Dyn4cast | Linear Model and various Transformations for Efficiency | spec_tbl_df | 200 | 5 |
| Transform | Dyn4cast | Standardize 'data.frame' for comparable *Machine Learning* prediction and visualization | spec_tbl_df | 25 | 7 |
| treatments | Dyn4cast | Enhanced Estimation of Treatment Effects of Binary Data from Randomized Experiments | spec_tbl_df | 500 | 3 |
| mutate_example | MuTATE | Synthetic example dataset for MuTATE | data.frame | 200 | 9 |
| cpm_models | tectonicr | Global model of current plate motions | vctrs_list_of | | |
| iceland | tectonicr | Example crustal stress dataset | sf | 490 | 10 |
| nuvel1 | tectonicr | NUVEL-1 Global model of current plate motions | data.frame | 14 | 7 |
| nuvel1_plates | tectonicr | Plate Boundaries on the Earth | sf | 36 | 4 |
| pb2002 | tectonicr | Global model of current plate motions | data.frame | 52 | 7 |
| plates | tectonicr | Plate Boundaries on the Earth | sf | 157 | 9 |
| san_andreas | tectonicr | Example crustal stress dataset | sf | 1126 | 10 |
| tibet | tectonicr | Example crustal stress dataset | sf | 1165 | 10 |
| dime_data | zoomerjoin | Donors from DIME Database | tbl_df | 1000 | 2 |
| actg | mlr3proba | ACTG 320 Clinical Trial Dataset | data.frame | 1151 | 16 |
| gbcs | mlr3proba | German Breast Cancer Study (GBCS) Dataset | data.frame | 686 | 16 |
| grace | mlr3proba | GRACE 1000 Dataset | data.frame | 1000 | 9 |
| whas | mlr3proba | Worcester Heart Attack Study (WHAS) Dataset | data.frame | 481 | 14 |
| cookfarm_mlr3 | mlr3spatiotempcv | Cookfarm Profiles Regression Task | data.frame | 178840 | 25 |
| diplodia | mlr3spatiotempcv | Diplodia Classification Task | data.frame | 922 | 13 |
| ecuador | mlr3spatiotempcv | Ecuador Classification Task | data.frame | 751 | 13 |
| leipzig | mlr3spatial | Leipzig Land Cover Task | sf | 97 | 10 |
| dti | mlr3fda | The dti dataset | data.frame | 382 | 5 |
| fuel | mlr3fda | The fuel dataset | data.frame | 129 | 4 |
| phoneme | mlr3fda | The phoneme dataset | data.frame | 250 | 151 |
| isotopes_ds | eprscope | Nuclear Isotope Data Frame (Dataset) with ENDOR Frequencies | data.table | 351 | 9 |
| solvents_ds | eprscope | Solvent Properties Data Frame (Dataset) for EPR/ENDOR | tbl_df | 46 | 10 |
| forplotdata | forplot | Demonstration data set with continous outcomes | data.frame | 10 | 10 |
| forplotdata_bp | forplot | Raw data for continous outcomes | data.frame | 2000 | 3 |
| forplotdata_prop | forplot | Demonstration data set with binary outcomes | data.frame | 10 | 12 |
| forplotdata | forplot | Demonstration data set with continous outcomes | data.frame | 10 | 10 |
| forplotdata_bp | forplot | Raw data for continous outcomes | data.frame | 2000 | 3 |
| forplotdata_prop | forplot | Demonstration data set with binary outcomes | data.frame | 10 | 12 |
| BaseDataSet.ConversionFactors | Luminescence | Base datasets | list | | |
| BaseDataSet.FractionalGammaDose | Luminescence | Base datasets | list | | |
| BaseDataSet.GrainSizeAttenuation | Luminescence | Base datasets | data.frame | 16 | 7 |
| CW_Curve.BosWallinga2012 | Luminescence | Example CW-OSL curve data for the package Luminescence | data.frame | 2000 | 2 |
| CWOSL.SAR.Data | Luminescence | Example data from a SAR OSL and SAR TL measurement for the package Luminescence | Risoe.BINfileData | | |
| data_CrossTalk | Luminescence | Example Al2O3:C Measurement Data | list | | |
| data_ITC | Luminescence | Example Al2O3:C Measurement Data | RLum.Analysis | | |
| ExampleData.CobbleData | Luminescence | Example data for calc_CobbleDoseRate() | data.frame | 14 | 24 |
| ExampleData.CW_OSL_Curve | Luminescence | Example CW-OSL curve data for the package Luminescence | data.frame | 1000 | 2 |
| ExampleData.DeValues | Luminescence | Example De data sets for the package Luminescence | list | | |
| ExampleData.Fading | Luminescence | Example data for feldspar fading measurements | list | | |
| ExampleData.portableOSL | Luminescence | Example portable OSL curve data for the package Luminescence | list | | |
| ExampleData.RLum.Data.Image | Luminescence | Example data as RLum.Data.Image objects | RLum.Data.Image | | |
| ExampleData.ScaleGammaDose | Luminescence | Example data for scale_GammaDose() | data.frame | 9 | 12 |
| ExampleData.SurfaceExposure | Luminescence | Example OSL surface exposure dating data | list | | |
| ExampleData.TR_OSL | Luminescence | Example TR-OSL data | RLum.Data.Curve | | |
| IRSAR.RF.Data | Luminescence | Example data as RLum.Analysis objects | RLum.Analysis | | |
| Lx.data | Luminescence | Example Lx and Tx curve data from an artificial OSL measurement | data.frame | 100 | 2 |
| LxTxData | Luminescence | Example Lx/Tx data from CW-OSL SAR measurement | data.frame | 7 | 4 |
| MortarData | Luminescence | Example equivalent dose data from mortar samples | data.frame | 40 | 2 |
| OSL.SARMeasurement | Luminescence | Example data for a SAR OSL measurement and a TL spectrum using a lexsyg reader | list | | |
| RF70Curves | Luminescence | RF70 Example data as RLum.Analysis objects | list | | |
| TL.SAR.Data | Luminescence | Example data from a SAR OSL and SAR TL measurement for the package Luminescence | Risoe.BINfileData | | |
| TL.Spectrum | Luminescence | Example data for a SAR OSL measurement and a TL spectrum using a lexsyg reader | RLum.Data.Spectrum | | |
| Tx.data | Luminescence | Example Lx and Tx curve data from an artificial OSL measurement | data.frame | 100 | 2 |
| values.cosmic.Softcomp | Luminescence | Base data set for cosmic dose rate calculation | data.frame | 33 | 2 |
| values.curve | Luminescence | Example data for fit_LMCurve() in the package Luminescence | data.frame | 4000 | 2 |
| values.curveBG | Luminescence | Example data for fit_LMCurve() in the package Luminescence | data.frame | 4000 | 2 |
| values.factor.Altitude | Luminescence | Base data set for cosmic dose rate calculation | data.frame | 7 | 2 |
| values.par.FJH | Luminescence | Base data set for cosmic dose rate calculation | data.frame | 12 | 4 |
| adduct_file | SpaMTP | Common adduct constants | data.frame | 47 | 6 |
| reaction_type | SpaMTP | Pathway-network reaction styles | data.frame | 18 | 5 |
| avc | TemporalHazard | AVC: Atrioventricular Canal Repair | data.frame | 310 | 11 |
| cabgkul | TemporalHazard | CABGKUL: Primary Isolated Coronary Artery Bypass Grafting (KU Leuven) | data.frame | 5880 | 2 |
| omc | TemporalHazard | OMC: Open Mitral Commissurotomy | data.frame | 339 | 7 |
| tga | TemporalHazard | TGA: Transposition of the Great Arteries | data.frame | 470 | 14 |
| valves | TemporalHazard | Valves: Primary Heart Valve Replacement | data.frame | 1533 | 19 |
| trial | gtsummary | Results from a simulated study of two chemotherapy agents | tbl_df | 200 | 8 |
| agp_gut | linf | American Gut Project gut microbiome dataset | list | | |
| valencia_linf_hypercube_1k | linf | Valencia 1k four-component hypercube embedding example | list | | |
| valencia13k_dcst_depth2_merged | linf | Valencia 13k merged depth-2 dCST assignments | list | | |
| valencia13k_dcst_depth3_merged | linf | Valencia 13k merged depth-3 dCST assignments | list | | |
| valencia2k | linf | Valencia 2k vaginal microbiome dataset | list | | |
| lob.data | obAnalytics | Example limit order book data. | list | | |
| admix | plmmr | Admix: Semi-simulated SNP data | list | | |
| chp10GNP | MSTest | Carrasco, Hu, & Ploberger 2010 GNP data | data.frame | 239 | 3 |
| hamilton84GNP | MSTest | Hamilton 1984 & Hansen 1992 GNP data | data.frame | 135 | 3 |
| USGNP | MSTest | US GNP data 1947Q2 - 2024Q2 | data.frame | 309 | 3 |
| USRGDP | MSTest | US Real GDP data 1947Q2 - 2024Q2 | data.frame | 309 | 3 |
| mix2gauss | dsdp | Datasets of Mixture of 2 Gaussian Distributions | list | | |
| mix2gaussHist | dsdp | Dataset of Mixture of 2 Gaussian Distributions: Histogram version | list | | |
| mix3gauss | dsdp | Datasets of Mixture of 3 Gaussian Distributions | list | | |
| mixexpgamma | dsdp | Dataset of Mixture of Exponential Distribution and Gamma Distribution | list | | |
| mixExpGammaHist | dsdp | Dataset of Mixture of Exponential Distribution and Gamma Distribution: Histogram Version | list | | |
| example_metadata | MiCARA | Synthetic Example Sample Metadata | data.frame | 30 | 6 |
| example_pathways | MiCARA | Synthetic Example Functional Pathways Matrix | data.frame | 10 | 30 |
| example_taxa | MiCARA | Synthetic Example Taxa Abundance Matrix | data.frame | 20 | 30 |
| atlantic_amphibians | RuHere | Amphibian communities from the Atlantic Forest | tbl_df | 8254 | 3 |
| country_dictionary | RuHere | Country dictionary for standardizing country names and codes | list | | |
| cultivated | RuHere | Dictionary of terms used to flag cultivated individuals | list | | |
| fake_data | RuHere | Fake occurrence data for testing coordinate validation functions | data.frame | 86 | 24 |
| flag_colors | RuHere | Color palette for flagged records | character | | |
| flag_names | RuHere | Flag name dictionary | character | | |
| occ_bien | RuHere | Occurrence records of Yellow Trumpet Tree from BIEN | data.frame | 1791 | 32 |
| occ_flagged | RuHere | Flagged occurrence records of _Araucaria angustifolia_ | tbl_df | 2656 | 28 |
| occ_gbif | RuHere | Occurrence records of _Araucaria angustifolia_ from GBIF | tbl_df | 2989 | 21 |
| occ_idig | RuHere | Occurrence records of azure jay from iDigBio | data.frame | 33 | 86 |
| occ_splink | RuHere | Occurrence records of azure jay from SpeciesLink | data.frame | 361 | 15 |
| occurrences | RuHere | Integrated occurrence dataset for three example species | data.frame | 4080 | 14 |
| prepared_metadata | RuHere | Metadata templates used internally by 'format_columns()' | list | | |
| puma_atlanticr | RuHere | Occurrence records of _Puma concolor_ from AtlanticR | spec_tbl_df | 139 | 40 |
| states | RuHere | Administrative Units (States, Provinces, and Regions) | PackedSpatVector | | |
| states_dictionary | RuHere | States dictionary for standardizing state and province names and codes | list | | |
| world | RuHere | World Countries | PackedSpatVector | | |
| worldclim | RuHere | Bioclimatic Variables from WorldClim (bio_1, bio_7, bio_12) | PackedSpatRaster | | |
| bruno_example | bruno | Constructed Example Data for bruno | data.frame | 12 | 4 |
| ADAE | cards | Example ADaM Data | tbl_df | 1191 | 56 |
| ADLB | cards | Example ADaM Data | tbl_df | 5784 | 46 |
| ADSL | cards | Example ADaM Data | tbl_df | 254 | 49 |
| ADTTE | cards | Example ADaM Data | tbl_df | 254 | 26 |
| acs_poverty | socviz | US County Poverty Rates by Age Group | spec_tbl_df | 9666 | 4 |
| acs_poverty_lon | socviz | US County Poverty Rates by Age Group, Longer Version | spec_tbl_df | 12888 | 3 |
| asasec | socviz | American Sociological Association Section Membership | spec_tbl_df | 52 | 9 |
| color_table | socviz | A table of hex color values related to types of color blindness | tbl_df | 5 | 4 |
| counties_sf | socviz | US County geometries and demographic data | sf | 3144 | 18 |
| county_comp | socviz | US County Components of Population Change | spec_tbl_df | 3144 | 9 |
| county_data | socviz | Census Data on US Counties | tbl_df | 3195 | 8 |
| county_map | socviz | US County map file | data.frame | 191382 | 7 |
| edu | socviz | Years of school completed by people 25 years and over in the US. | tbl_df | 366 | 11 |
| election | socviz | US Presidential Election 2016, State-level results | tbl_df | 51 | 22 |
| election24 | socviz | US Presidential Election 2024, State-level results | tbl_df | 51 | 20 |
| election24_county_df | socviz | US County-level Presidential Election data, 2024 | tbl_df | 3153 | 7 |
| elections_historic | socviz | US Presidential Election vote shares | tbl_df | 51 | 19 |
| farsinvolved | socviz | Child Pedestrians involved in Fatal Motor Vehicle Crashes, 2009-2023 | tbl_df | 5490 | 4 |
| fredts | socviz | Monetary Base and S&P 500 series | data.frame | 357 | 5 |
| gss_lon | socviz | General Social Survey data, 1972-2024 | tbl_df | 75699 | 25 |
| gss_sm | socviz | General Social Survey data, 2016 | tbl_df | 2867 | 32 |
| lawschools | socviz | US Law School Enrollments 1963-2015 | tbl_df | 53 | 11 |
| maunaloa | socviz | Mauna Loa Atmospheric CO2 Concentration | data.frame | 168 | 4 |
| oecd_le | socviz | Life Expectancy in the OECD, 1960-2023. | spec_tbl_df | 2203 | 4 |
| oecd_sum | socviz | Life Expectancy in the OECD, 1960-2023 | spec_tbl_df | 64 | 5 |
| okboomer | socviz | Monthly Births in the U.S., 1933-2015 | tbl_df | 996 | 11 |
| opiates | socviz | Opiate-Related Deaths in the United States, 1999-2020 | tbl_df | 1122 | 8 |
| organdata | socviz | Organ donation in the OECD | tbl_df | 238 | 21 |
| states_sf | socviz | US State geometries | sf | 51 | 5 |
| studebt | socviz | Student debt data | tbl_df | 16 | 4 |
| titanic | socviz | A table of survival rates from the Titanic | data.frame | 4 | 4 |
| yahoo | socviz | Yahoo Revenue and Employees | spec_tbl_df | 12 | 4 |
| depression | RESI | Depression Treatment Data | data.frame | 1020 | 5 |
| insurance | RESI | US Health Insurance Data | data.frame | 1338 | 7 |
| country_codes | faunabr | Country Codes and Names | data.frame | 244 | 2 |
| fauna_data | faunabr | Catálogo Taxonômico da Fauna do Brasil database - Version 1.17 | data.frame | 9607 | 21 |
| map_translation | faunabr | Helpers for translating data | list | | |
| occurrences | faunabr | Records of animal species | data.frame | 2809 | 4 |
| states | faunabr | SpatVector of the federal states of Brazil | PackedSpatVector | | |
| world_fauna | faunabr | SpatVector of the world countries | PackedSpatVector | | |
| etitanic | earth | Titanic data with incomplete cases removed | data.frame | 1046 | 6 |
| ozone1 | earth | Ozone readings in Los Angeles with incomplete cases removed | data.frame | 330 | 10 |
| dem2gbp | bayesGARCH | DEM/GBP exchange rate log-returns | numeric | | |
| sim_sce_test | smartid | scRNA-seq test data of 4 groups simulated by 'splatter'. | SingleCellExperiment | | |
| ai_long | Nestimate | Human-AI Vibe Coding Interaction Data (Long Format) | data.frame | 8551 | 9 |
| chatgpt_srl | Nestimate | ChatGPT Self-Regulated Learning Scale Scores | data.frame | 1000 | 5 |
| group_regulation_long | Nestimate | Group Regulation in Collaborative Learning (Long Format) | data.frame | 27533 | 6 |
| human_long | Nestimate | Human-AI Vibe Coding Interaction Data (Long Format) | data.frame | 10796 | 9 |
| learning_activities | Nestimate | Online Learning Activity Indicators | data.frame | 6000 | 7 |
| srl_strategies | Nestimate | Self-Regulated Learning Strategy Frequencies | data.frame | 250 | 9 |
| trajectories | Nestimate | Student Engagement Trajectories | matrix | 138 | 15 |
| ptitanic | rpart.plot | Titanic data with passenger names and other details removed. | data.frame | 1309 | 6 |
| demo_events_gpt | ggtilecal | 5 Sample Events generated by ChatGPT 3.5 | tbl_df | 5 | 8 |
| demo_events_overlap | ggtilecal | 10 Overlapping Events | rowwise_df | 10 | 7 |
| owtemps | chandwich | Oxford and Worthing annual maximum temperatures | data.frame | 80 | 2 |
| rats | chandwich | Rat tumor data | matrix | 71 | 2 |
| stunitco | stLMM | FIA state, unit, and county polygons | sf | 3108 | 8 |
| exdat_cantons | healthiar | PM2.5 exposure and COPD incidence in Switzerland | data.frame | 26 | 15 |
| exdat_lifetable | healthiar | Population data per age and sex in Switzerland | tbl_df | 200 | 4 |
| exdat_noise | healthiar | Noise exposure in urban and rural regions in Norway | tbl_df | 15 | 12 |
| exdat_ozone | healthiar | PM2.5 exposure and COPD incidence in Switzerland | data.frame | 22 | 15 |
| exdat_pm | healthiar | PM2.5 exposure and COPD incidence in Switzerland | tbl_df | 1 | 15 |
| exdat_prepare_mdi | healthiar | Social indicators of the BEST-COST Multidimensional Deprivation Index (MDI) | data.frame | 581 | 17 |
| exdat_pwm_2 | healthiar | Geospatial outlines and populations of the Brussels-Capital region (Belgium) | sf | 19 | 5 |
| exdat_socialize | healthiar | Municipalities in Belgium ranked by BEST-COST Multidimensional Deprivation Index (MDI) | data.frame | 18074 | 9 |
| aoi_shp | tbepreport | Area of interest boundary for non-native species occurrence data | sf | 1 | 3 |
| bayseg_shp | tbepreport | Bay segment boundaries for non-native species occurrence data | sf | 7 | 5 |
| sgsegest | tbepreport | Seagrass coverage estimates by bay segment and year | data.frame | 126 | 3 |
| A128 | CoTiMA | A128 example matrix | matrix | 2 | |
| A313 | CoTiMA | A313 example matrix | matrix | 2 | |
| ageM1 | CoTiMA | ageM1 example vector | numeric | | |
| ageM128 | CoTiMA | ageM128 example vector | numeric | | |
| ageM18 | CoTiMA | ageM18 example vector | numeric | | |
| ageM201 | CoTiMA | ageM201 example vector | numeric | | |
| ageM313 | CoTiMA | ageM313 example vector | numeric | | |
| ageM32 | CoTiMA | ageM32 example vector | numeric | | |
| ageM4 | CoTiMA | ageM4 example vector | numeric | | |
| ageSD1 | CoTiMA | ageSD1 example vector | numeric | | |
| ageSD128 | CoTiMA | ageSD128 example vector | numeric | | |
| ageSD18 | CoTiMA | ageSD18 example vector | numeric | | |
| ageSD201 | CoTiMA | ageSD201 example vector | numeric | | |
| ageSD313 | CoTiMA | ageSD313 example vector | numeric | | |
| ageSD32 | CoTiMA | ageSD32 example vector | numeric | | |
| ageSD4 | CoTiMA | ageSD4 example vector | numeric | | |
| alphas128 | CoTiMA | alphas128 example vector | numeric | | |
| alphas313 | CoTiMA | alphas313 example vector | numeric | | |
| burnout1 | CoTiMA | burnout1 example vector | character | | |
| burnout128 | CoTiMA | burnout128 example vector | character | | |
| burnout18 | CoTiMA | burnout18 example vector | character | | |
| burnout201 | CoTiMA | burnout201 example vector | character | | |
| burnout313 | CoTiMA | burnout313 example vector | character | | |
| burnout32 | CoTiMA | burnout32 example vector | character | | |
| burnout4 | CoTiMA | burnout4 example vector | character | | |
| combineVariables128 | CoTiMA | combineVariables128 example vector | list | | |
| combineVariablesNames128 | CoTiMA | combineVariablesNames128 example vector | character | | |
| CoTiMABiG_D_BO | CoTiMA | ctmaBiG-object reproducing results of Guthier et al. (2020) | CoTiMAFit | | |
| CoTiMAFullFit_3 | CoTiMA | ctmaFit-object with a 'full' CoTiMA of 3 studies | CoTiMAFit | | |
| CoTiMAFullFit_6 | CoTiMA | ctmaFit-object with a 'full' CoTiMA of 6 studies | CoTiMAFit | | |
| CoTiMAFullFit_6_new | CoTiMA | ctmaFit-object with a 'full' CoTiMA of 6 studies | CoTiMAFit | | |
| CoTiMAFullInv23Fit_6 | CoTiMA | 1st fitted ctmaFit-object in a series of 2 to test equality of 2 cross effects | CoTiMAFit | | |
| CoTiMAFullInvEq23Fit_6 | CoTiMA | 2nd fitted ctmaFit-object in a series of 2 to test equality of 2 cross effects | CoTiMAFit | | |
| CoTiMAInitFit_3 | CoTiMA | ctmaInit-object with of 3 primary studies | CoTiMAFit | | |
| CoTiMAInitFit_6 | CoTiMA | ctmaInit-object with 6 primary studies | CoTiMAFit | | |
| CoTiMAInitFit_6_new | CoTiMA | ctmaInit-object with 6 primary studies | CoTiMAFit | | |
| CoTiMAInitFit_6_NUTS | CoTiMA | ctmaInit-object with a 'full' CoTiMA of 6 studies using NUTS sampler | CoTiMAFit | | |
| CoTiMAInitFit_D_BO | CoTiMA | ctmaInit-object created by Guthier et al. (2020) with 48 primary studies | CoTiMAFit | | |
| CoTiMAMod1onFullFit_6 | CoTiMA | ctmaFit-object with a categorical moderator of the full drift matrix | CoTiMAFit | | |
| CoTiMAMod1onFullFit_6_cats12 | CoTiMA | ctmaFit-object with a categorical moderator of the full drift matrix | CoTiMAFit | | |
| CoTiMAMod2on23Fit_6 | CoTiMA | ctmaFit-object with a continuous moderator of 2 cross effects | CoTiMAFit | | |
| CoTiMAPart134Inv3Fit_6 | CoTiMA | ctmaFit-object with with only one cross effect and this one set equal across primary studies | CoTiMAFit | | |
| CoTiMAPower_D_BO | CoTiMA | ctmaPower-object reproducing results of Guthier et al. (2020) | CoTiMAFit | | |
| CoTiMAStanctArgs | CoTiMA | This are preset arguments | list | | |
| CoTiMAstudyList_3 | CoTiMA | ctmaPrep-object created with 3 primary studies | CoTiMAFit | | |
| CoTiMAstudyList_6 | CoTiMA | ctmaPrep-object created with 6 primary studies | CoTiMAFit | | |
| CoTiMAstudyList_6_new | CoTiMA | ctmaPrep-object created with 6 primary studies | CoTiMAFit | | |
| country1 | CoTiMA | country1 example vector | character | | |
| country128 | CoTiMA | country128 example vector | character | | |
| country18 | CoTiMA | country18 example vector | character | | |
| country201 | CoTiMA | country201 example vector | character | | |
| country313 | CoTiMA | country313 example vector | character | | |
| country32 | CoTiMA | country32 example vector | character | | |
| country4 | CoTiMA | country4 example vector | character | | |
| delta_t1 | CoTiMA | delta_t1 example vector | numeric | | |
| delta_t128 | CoTiMA | delta_t128 example vector | numeric | | |
| delta_t18 | CoTiMA | delta_t18 example vector | numeric | | |
| delta_t201 | CoTiMA | delta_t201 example vector | numeric | | |
| delta_t228 | CoTiMA | delta_t228 example vector | logical | | |
| delta_t313 | CoTiMA | delta_t313 example vector | numeric | | |
| delta_t32 | CoTiMA | delta_t32 example vector | numeric | | |
| delta_t4 | CoTiMA | delta_t4 example vector | numeric | | |
| demands1 | CoTiMA | demands1 example vector | character | | |
| demands128 | CoTiMA | demands128 example vector | character | | |
| demands18 | CoTiMA | demands18 example vector | character | | |
| demands201 | CoTiMA | demands201 example vector | character | | |
| demands313 | CoTiMA | demands313 example vector | character | | |
| demands32 | CoTiMA | demands32 example vector | character | | |
| demands4 | CoTiMA | demands4 example vector | character | | |
| dl_link | CoTiMA | dl_link example path | character | | |
| empcov1 | CoTiMA | empcov1 example matrix | matrix | 4 | |
| empcov128 | CoTiMA | empcov128 example matrix | matrix | 4 | |
| empcov18 | CoTiMA | empcov18 example matrix | matrix | 4 | 4 |
| empcov201 | CoTiMA | empcov201 example matrix | matrix | 6 | 6 |
| empcov313 | CoTiMA | empcov313 example matrix | matrix | 6 | |
| empcov32 | CoTiMA | empcov32 example matrix | matrix | 4 | 4 |
| empcov4 | CoTiMA | empcov4 example matrix | matrix | 4 | |
| malePercent1 | CoTiMA | malePercent1 example vector | numeric | | |
| malePercent128 | CoTiMA | malePercent128 example vector | numeric | | |
| malePercent18 | CoTiMA | malePercent18 example vector | numeric | | |
| malePercent201 | CoTiMA | malePercent201 example vector | numeric | | |
| malePercent313 | CoTiMA | malePercent313 example vector | numeric | | |
| malePercent32 | CoTiMA | malePercent32 example vector | numeric | | |
| malePercent4 | CoTiMA | malePercent4 example vector | numeric | | |
| moderator1 | CoTiMA | moderator1 example vector | numeric | | |
| moderator128 | CoTiMA | moderator128 example vector | numeric | | |
| moderator18 | CoTiMA | moderator18 example vector | numeric | | |
| moderator201 | CoTiMA | moderator201 example vector | numeric | | |
| moderator313 | CoTiMA | moderator313 example vector | numeric | | |
| moderator32 | CoTiMA | moderator32 example vector | numeric | | |
| moderator4 | CoTiMA | moderator4 example vector | numeric | | |
| moderatorLabels | CoTiMA | moderatorLabels example vector | character | | |
| moderatorValues | CoTiMA | moderatorValues example vector | list | | |
| occupation1 | CoTiMA | occupation1 example vector | character | | |
| occupation128 | CoTiMA | occupation128 example vector | character | | |
| occupation18 | CoTiMA | occupation18 example vector | character | | |
| occupation201 | CoTiMA | occupation201 example vector | character | | |
| occupation313 | CoTiMA | occupation313 example vector | character | | |
| occupation32 | CoTiMA | occupation32 example vector | character | | |
| occupation4 | CoTiMA | occupation4 example vector | character | | |
| pairwiseN128 | CoTiMA | pairwiseN128 example vector | matrix | 9 | |
| pubList_8 | CoTiMA | pubList_8 example list | CoTiMAFit | | |
| rawData228 | CoTiMA | rawData228 example list | list | | |
| recodeVariables128 | CoTiMA | recodeVariables128 example vector | character | | |
| results128 | CoTiMA | results128 example list | list | | |
| sampleSize1 | CoTiMA | sampleSize1 example vector | numeric | | |
| sampleSize128 | CoTiMA | sampleSize128 example vector | numeric | | |
| sampleSize18 | CoTiMA | sampleSize18 example vector | numeric | | |
| sampleSize201 | CoTiMA | sampleSize201 example vector | numeric | | |
| sampleSize313 | CoTiMA | sampleSize313 example vector | numeric | | |
| sampleSize32 | CoTiMA | sampleSize32 example vector | numeric | | |
| sampleSize4 | CoTiMA | sampleSize4 example vector | numeric | | |
| source1 | CoTiMA | source1 example vector | character | | |
| source128 | CoTiMA | source128 example vector | character | | |
| source18 | CoTiMA | source18 example vector | character | | |
| source201 | CoTiMA | source201 example vector | character | | |
| source313 | CoTiMA | source313 example vector | character | | |
| source4 | CoTiMA | source4 example vector | character | | |
| targetVariables1 | CoTiMA | targetVariables1 example vector | character | | |
| targetVariables128 | CoTiMA | targetVariables128 example vector | character | | |
| targetVariables313 | CoTiMA | targetVariables313 example vector | character | | |
| targetVariables4 | CoTiMA | targetVariables4 example vector | character | | |
| variableNames128 | CoTiMA | variableNames128 example vector | character | | |
| variableNames18 | CoTiMA | variableNames18 example vector | character | | |
| variableNames201 | CoTiMA | variableNames201 example vector | character | | |
| variableNames32 | CoTiMA | variableNames32 example vector | character | | |
| qbBaha2017 | DPQmpfr | Accurate qbeta() values from Baharev et al (2017)'s Program | matrix | 9 | 22 |
| DiUbi | DEP | DiUbi - Ubiquitin interactors for different diubiquitin-linkages (UbIA-MS dataset) | data.frame | 4071 | 72 |
| DiUbi_ExpDesign | DEP | Experimental design of the DiUbi dataset | data.frame | 30 | 3 |
| UbiLength | DEP | UbiLength - Ubiquitin interactors of different linear ubiquitin lengths (UbIA-MS dataset) | data.frame | 3006 | 23 |
| UbiLength_ExpDesign | DEP | Experimental design of the UbiLength dataset | data.frame | 12 | 3 |
| corp_rep_data | seminr | Measurement Instrument for the Corporate Reputation Model | data.frame | 344 | 41 |
| corp_rep_data2 | seminr | A Second Measurement Instrument for the Corporate Reputation Model | data.frame | 347 | 41 |
| influencer_data | seminr | Measurement Instrument for the Influencer Model | data.frame | 222 | 28 |
| mobi | seminr | Measurement Instrument for the Mobile Phone Industry | data.frame | 250 | 24 |
| pt_Witkovsky_Tab1 | DPQ | Viktor Witosky's Table_1 pt() Examples | data.frame | 17 | 9 |
| rpu_boundaries | hydrogeofetch | RPU Boundaries Raster Processing Unit boundaries | sf | 70 | 4 |
| vpu_boundaries | hydrogeofetch | VPU Boundaries Vector Processing Unit boundaries | sf | 23 | 3 |
| data_test1 | lmhelprs | Sample Data: For Testing | data.frame | 100 | 8 |
| ofe_f2 | ofemeantest | On-Farm single strip treatment trial (Field F2) | sf | 3070 | 3 |
| ofe_p | ofemeantest | On-Farm single strip treatment trial (companion dataset) | sf | 3070 | 3 |
| aircraft | robustbase | Aircraft Data | data.frame | 23 | 5 |
| airmay | robustbase | Air Quality Data | data.frame | 31 | 4 |
| alcohol | robustbase | Alcohol Solubility in Water Data | data.frame | 44 | 7 |
| ambientNOxCH | robustbase | Daily Means of NOx (mono-nitrogen oxides) in air | data.frame | 366 | 14 |
| Animals2 | robustbase | Brain and Body Weights for 65 Species of Land Animals | data.frame | 65 | 2 |
| biomassTill | robustbase | Biomass Tillage Data | data.frame | 58 | 4 |
| bushfire | robustbase | Campbell Bushfire Data | data.frame | 38 | 5 |
| carrots | robustbase | Insect Damages on Carrots | data.frame | 24 | 4 |
| cloud | robustbase | Cloud point of a Liquid | data.frame | 19 | 2 |
| coleman | robustbase | Coleman Data Set | data.frame | 20 | 6 |
| condroz | robustbase | Condroz Data | data.frame | 428 | 2 |
| CrohnD | robustbase | Crohn's Disease Adverse Events Data | data.frame | 117 | 9 |
| cushny | robustbase | Cushny and Peebles Prolongation of Sleep Data | numeric | | |
| delivery | robustbase | Delivery Time Data | data.frame | 25 | 3 |
| education | robustbase | Education Expenditure Data | data.frame | 50 | 6 |
| epilepsy | robustbase | Epilepsy Attacks Data Set | data.frame | 59 | 11 |
| exAM | robustbase | Example Data of Antille and May - for Simple Regression | data.frame | 12 | 2 |
| foodstamp | robustbase | Food Stamp Program Participation | data.frame | 150 | 4 |
| hbk | robustbase | Hawkins, Bradu, Kass's Artificial Data | data.frame | 75 | 4 |
| heart | robustbase | Heart Catherization Data | data.frame | 12 | 3 |
| kootenay | robustbase | Waterflow Measurements of Kootenay River in Libby and Newgate | data.frame | 13 | 2 |
| lactic | robustbase | Lactic Acid Concentration Measurement Data | data.frame | 20 | 2 |
| los | robustbase | Length of Stay Data | integer | | |
| milk | robustbase | Daudin's Milk Composition Data | data.frame | 86 | 8 |
| NOxEmissions | robustbase | NOx Air Pollution Data | data.frame | 8088 | 4 |
| pension | robustbase | Pension Funds Data | data.frame | 18 | 2 |
| phosphor | robustbase | Phosphorus Content Data | data.frame | 18 | 3 |
| pilot | robustbase | Pilot-Plant Data | data.frame | 20 | 2 |
| possum.mat | robustbase | Possum Diversity Data | matrix | 151 | 14 |
| possumDiv | robustbase | Possum Diversity Data | data.frame | 151 | 9 |
| pulpfiber | robustbase | Pulp Fiber and Paper Data | data.frame | 62 | 8 |
| radarImage | robustbase | Satellite Radar Image Data from near Munich | data.frame | 1573 | 5 |
| salinity | robustbase | Salinity Data | data.frame | 28 | 4 |
| SiegelsEx | robustbase | Siegel's Exact Fit Example Data | data.frame | 9 | 2 |
| starsCYG | robustbase | Hertzsprung-Russell Diagram Data of Star Cluster CYG OB1 | data.frame | 47 | 2 |
| steamUse | robustbase | Steam Usage Data (Excerpt) | data.frame | 25 | 9 |
| telef | robustbase | Number of International Calls from Belgium | data.frame | 24 | 2 |
| toxicity | robustbase | Toxicity of Carboxylic Acids Data | data.frame | 38 | 10 |
| vaso | robustbase | Vaso Constriction Skin Data Set | data.frame | 39 | 3 |
| wagnerGrowth | robustbase | Wagner's Hannover Employment Growth Data | data.frame | 63 | 7 |
| wood | robustbase | Modified Data on Wood Specific Gravity | data.frame | 20 | 6 |
| x30o50 | robustbase | Extreme Data examples | numeric | | |
| mod | BayesPIM | Converged example BayesPIM fit | bayespim | | |
| xSim | lokern | Simulated Linear plus Exponential Peak | numeric | | |
| atrial_fibrillation | multinma | Stroke prevention in atrial fibrillation patients | data.frame | 63 | 11 |
| bcg_vaccine | multinma | BCG vaccination | data.frame | 26 | 6 |
| blocker | multinma | Beta blockers to prevent mortality after MI | data.frame | 44 | 5 |
| certolizumab | multinma | Certolizumab | data.frame | 24 | 5 |
| diabetes | multinma | Incidence of diabetes in trials of antihypertensive drugs | data.frame | 48 | 7 |
| dietary_fat | multinma | Reduced dietary fat to prevent mortality | data.frame | 21 | 7 |
| hta_psoriasis | multinma | HTA Plaque Psoriasis | data.frame | 36 | 9 |
| ndmm_agd | multinma | Newly diagnosed multiple myeloma | data.frame | 2819 | 6 |
| ndmm_agd_covs | multinma | Newly diagnosed multiple myeloma | data.frame | 4 | 15 |
| ndmm_ipd | multinma | Newly diagnosed multiple myeloma | data.frame | 1325 | 10 |
| parkinsons | multinma | Mean off-time reduction in Parkison's disease | data.frame | 15 | 7 |
| plaque_psoriasis_agd | multinma | Plaque psoriasis data | data.frame | 15 | 26 |
| plaque_psoriasis_ipd | multinma | Plaque psoriasis data | data.frame | 4118 | 16 |
| smoking | multinma | Smoking cessation data | data.frame | 50 | 5 |
| social_anxiety | multinma | Social Anxiety | tbl_df | 248 | 8 |
| statins | multinma | Statins for cholesterol lowering | data.frame | 38 | 7 |
| thrombolytics | multinma | Thrombolytic treatments data | data.frame | 102 | 5 |
| transfusion | multinma | Granulocyte transfusion in patients with neutropenia or neutrophil dysfunction | data.frame | 12 | 4 |
| arthritis | funcml | Arthritis survey data | data.frame | 4856 | 12 |
| bangladeshmaternalrisk | funcml | Bangladesh maternal risk data | data.frame | 1205 | 12 |
| birthweight | funcml | Birth weight data | data.frame | 189 | 10 |
| breastcancerdiagnostic | funcml | Breast cancer diagnostic data | data.frame | 569 | 31 |
| breastcancerwisconsin | funcml | Wisconsin breast cancer data | data.frame | 699 | 10 |
| cancerremission | funcml | Cancer remission data | data.frame | 27 | 3 |
| cd4counts | funcml | CD4 follow-up data | data.frame | 20 | 2 |
| cigsmoke | funcml | Youth tobacco survey data | data.frame | 3915 | 27 |
| doctorvisits | funcml | Doctor visits data | data.frame | 5190 | 12 |
| haberman | funcml | Haberman survival data | data.frame | 306 | 4 |
| heartdisease | funcml | Heart disease patient data | data.frame | 303 | 9 |
| heartfailure | funcml | Heart failure data | data.frame | 299 | 13 |
| infantmortality | funcml | Infant mortality data | data.frame | 105 | 5 |
| ketapain | funcml | Ketamine pain management data | data.frame | 184 | 11 |
| mammography | funcml | Mammography calcification data | data.frame | 11183 | 7 |
| newthyroid | funcml | Thyroid function data | data.frame | 215 | 6 |
| pimadiabetes | funcml | Pima diabetes data | data.frame | 532 | 9 |
| nudge | autodb | Nudge meta-analysis data | data.frame | 447 | 25 |
| omopViewerPanels | OmopViewer | Panels defined in the package. | list | | |
| omopViewerResults | OmopViewer | Mock results obtained from 'GiBleed' dataset. | summarised_result | 76784 | 13 |
| dyadic_bivariate_example | dyadicMarkov | Synthetic bivariate dyadic sequence example | data.frame | 90 | 5 |
| dyadic_univariate_example | dyadicMarkov | Synthetic univariate dyadic sequence example | data.frame | 90 | 3 |
| for_bus_gtfs | GTFSwizard | GTFS Data for Fortaleza (Bus System), Brazil. | wizardgtfs | | |
| for_rail_gtfs | GTFSwizard | GTFS Data for Fortaleza (Rail System), Brazil | wizardgtfs | | |
| batting | accbaseballr | ACC Batting Data | tbl_df | 3906 | 30 |
| league_context | accbaseballr | ACC baseball league context statistics | tbl_df | 16 | 21 |
| pitching | accbaseballr | ACC Pitching Data | data.frame | 3652 | 23 |
| players | accbaseballr | ACC Player Data | tbl_df | 3338 | 4 |
| amg_h_by_input_type | whep | AMG humification coefficient by carbon input type. | spec_tbl_df | 7 | 4 |
| animals_codes | whep | Animal codes and classifications | spec_tbl_df | 22 | 17 |
| biomass_coefs | whep | Biomass coefficients for crops and livestock products | tbl_df | 421 | 63 |
| cb_processing | whep | Commodity balance sheet processing fractions | spec_tbl_df | 127 | 5 |
| cbs_trade_codes | whep | CBS to trade item code mapping | spec_tbl_df | 710 | 4 |
| cft_mapping | whep | FAOSTAT crop to LPJmL crop functional type (CFT) mapping | spec_tbl_df | 153 | 5 |
| climate_mcf | whep | Climate-zone MCF values. | tbl_df | 25 | 3 |
| coello_synthetic_n | whep | Coello (2025) crop-specific synthetic nitrogen application rates | tbl_df | 579638 | 4 |
| conv_bouwman | whep | Bouwman feed conversion ratios. | tbl_df | 1071 | 5 |
| conv_krausmann | whep | Krausmann per head feed intake. | tbl_df | 8 | 3 |
| crops_eurostat | whep | Eurostat crop classification codes | spec_tbl_df | 13 | 2 |
| crops_manure_n | whep | Manure nitrogen application by crop and country | spec_tbl_df | 31648 | 4 |
| drainage_ranges | whep | Drainage-class bins keyed on annual soil drainage. | spec_tbl_df | 6 | 3 |
| feed_characteristics | whep | Feed characteristics by diet quality. | tbl_df | 3 | 5 |
| feed_taxonomy | whep | Feed taxonomy. | tbl_df | 199 | 8 |
| fertiliser_n2o_modifiers | whep | Fertiliser-type modifying factors for direct soil N2O. | spec_tbl_df | 16 | 4 |
| gleam_animal_weights | whep | GLEAM animal weights. | tbl_df | 32 | 5 |
| gleam_crop_residue_nitrogen | whep | Nitrogen parameters for crop residues of feed materials. | tbl_df | 68 | 6 |
| gleam_crop_residue_params | whep | GLEAM crop residue parameters. | tbl_df | 19 | 4 |
| gleam_dressing_percentages | whep | GLEAM dressing percentages. | tbl_df | 193 | 6 |
| gleam_energy_use_ef | whep | Energy use emission factors for livestock production. | tbl_df | 361 | 9 |
| gleam_enteric_params | whep | GLEAM enteric fermentation parameters. | tbl_df | 10 | 4 |
| gleam_feed_categories | whep | GLEAM feed categories. | tbl_df | 6 | 3 |
| gleam_feed_composition | whep | GLEAM feed use efficiency. | tbl_df | 21 | 4 |
| gleam_feed_conversion_ratios | whep | GLEAM feed conversion ratios for monogastrics. | tbl_df | 41 | 7 |
| gleam_feed_digestibility | whep | GLEAM feed digestibility for ruminants. | tbl_df | 27 | 5 |
| gleam_field_operation_ef | whep | Emission factors for field operations on feed materials. | tbl_df | 68 | 4 |
| gleam_fracremove | whep | Country-level fraction of crop residues removed. | tbl_df | 13 | 4 |
| gleam_geographic_hierarchy | whep | GLEAM geographic hierarchy. | tbl_df | 204 | 9 |
| gleam_livestock_categories | whep | GLEAM livestock categories. | tbl_df | 27 | 4 |
| gleam_mechanization_levels | whep | Country-level mechanization levels for feed materials. | tbl_df | 13332 | 6 |
| gleam_milk_production | whep | GLEAM milk production. | tbl_df | 9 | 5 |
| gleam_mms_shares | whep | GLEAM manure management system shares. | tbl_df | 14 | 5 |
| gleam_processing_transport_ef | whep | Processing and transport emission factors for feeds. | tbl_df | 68 | 5 |
| grazing_energy_coefs | whep | Grazing energy coefficients. | tbl_df | 1 | 3 |
| hwsd_texture_usda | whep | HWSD topsoil USDA texture code to texture-class crosswalk. | tbl_df | 13 | 2 |
| indirect_n2o_ef | whep | Indirect N2O emission factors. | tbl_df | 4 | 3 |
| ipcc_2006_enteric_ef | whep | IPCC 2006 Tier 1 enteric emission factors. | tbl_df | 22 | 3 |
| ipcc_2006_manure_ef | whep | IPCC 2006 Tier 1 manure emission factors. | tbl_df | 13 | 4 |
| ipcc_2006_mcf_temp | whep | IPCC 2006 MCF by temperature. | tbl_df | 16 | 3 |
| ipcc_2019_bo | whep | IPCC 2019 Bo values (Table 10.16). | tbl_df | 12 | 2 |
| ipcc_2019_cfi | whep | IPCC 2019 Cfi values (Table 10.4). | tbl_df | 6 | 3 |
| ipcc_2019_enteric_ef_cattle | whep | IPCC 2019 enteric EF for cattle. | tbl_df | 20 | 3 |
| ipcc_2019_enteric_ef_other | whep | IPCC 2019 enteric EF for non-cattle. | tbl_df | 9 | 2 |
| ipcc_2019_manure_ch4_ef_cattle | whep | IPCC 2019 manure CH4 EF for cattle. | tbl_df | 26 | 4 |
| ipcc_2019_manure_ch4_ef_other | whep | IPCC 2019 manure CH4 EF for non-cattle. | tbl_df | 10 | 3 |
| ipcc_2019_mcf_manure | whep | IPCC 2019 MCF for manure management. | tbl_df | 47 | 3 |
| ipcc_2019_n_excretion | whep | IPCC 2019 nitrogen excretion rates. | tbl_df | 30 | 3 |
| ipcc_2019_n2o_ef_direct | whep | IPCC 2019 direct N2O emission factors. | tbl_df | 18 | 2 |
| ipcc_2019_ym | whep | IPCC 2019 Ym values (Table 10.13). | tbl_df | 8 | 4 |
| ipcc_tier2_bo_values | whep | Tier 2 Bo values. | tbl_df | 12 | 2 |
| ipcc_tier2_energy_coefs | whep | Tier 2 energy coefficients. | tbl_df | 6 | 8 |
| ipcc_tier2_manure_ash | whep | Tier 2 manure ash content. | tbl_df | 9 | 2 |
| ipcc_tier2_n_retention | whep | Tier 2 nitrogen retention fractions. | tbl_df | 10 | 2 |
| ipcc_tier2_ym_values | whep | Tier 2 Ym values. | tbl_df | 17 | 3 |
| items_cbs | whep | Commodity balance sheet items | spec_tbl_df | 170 | 3 |
| items_full | whep | Full CBS item table | spec_tbl_df | 199 | 15 |
| items_prim | whep | Primary production items linked to CBS | tbl_df | 291 | 8 |
| items_prod | whep | Primary production items | spec_tbl_df | 304 | 3 |
| items_prod_full | whep | Full production item table | tbl_df | 310 | 29 |
| lassaletta_grassland_share | whep | Grassland share of synthetic nitrogen by country and year | spec_tbl_df | 6909 | 3 |
| liv_lu_coefs | whep | Livestock unit coefficients | spec_tbl_df | 16 | 2 |
| livestock_constants | whep | Livestock physical constants. | list | | |
| livestock_production_defaults | whep | Default production parameters. | tbl_df | 10 | 7 |
| manner_default_technique_mix | whep | Gross-default technique/incorporation-delay blend for MANNER. | spec_tbl_df | 4 | 4 |
| manner_incorporation_factor | whep | MANNER organic-manure incorporation-delay factor. | spec_tbl_df | 40 | 4 |
| manner_params | whep | MANNER process-based ammonia-volatilisation factors. | spec_tbl_df | 33 | 4 |
| manner_rain_factor | whep | MANNER synthetic-fertiliser rainfall factor. | spec_tbl_df | 72 | 4 |
| manner_rate_factor | whep | MANNER synthetic-fertiliser application-rate factor. | spec_tbl_df | 64 | 4 |
| manure_inorganic_n | whep | Inorganic (mineral) nitrogen fraction of excreted manure by species. | spec_tbl_df | 19 | 4 |
| max_intake_share | whep | Maximum intake shares. | tbl_df | 28 | 4 |
| meisinger_denitrification | whep | Meisinger and Randall topsoil denitrification share matrix. | spec_tbl_df | 108 | 7 |
| mueller_synthetic_n | whep | Synthetic nitrogen application rates by crop and country | spec_tbl_df | 5043 | 6 |
| n_attenuation_constants | whep | Nitrogen leaching-attenuation and indirect-N2O constants. | spec_tbl_df | 8 | 3 |
| n_boundary_params | whep | Planetary reactive-nitrogen boundary parameters. | tbl_df | 6 | 4 |
| n2o_efs_disaggregated | whep | Disaggregated direct soil N2O emission factors by climate and irrigation. | spec_tbl_df | 12 | 3 |
| n2o_efs_ipcc2006 | whep | IPCC 2006 Tier 1 direct soil N2O emission factors by climate and irrigation. | spec_tbl_df | 12 | 3 |
| nourish_levels | whep | Nourishment classification levels and colours. | tbl_df | 3 | 3 |
| nourishment_thresholds | whep | Nourishment protein and energy thresholds. | tbl_df | 9 | 5 |
| polities | whep | Polities | sf | 767 | 20 |
| polities_cats | whep | Polity categories and regional classifications | tbl_df | 198 | 39 |
| polity_area_crosswalk | whep | FAOSTAT/FABIO area-to-polity crosswalk | tbl_df | 647 | 26 |
| polity_label_aliases | whep | Source label to polity aliases | spec_tbl_df | 903 | 8 |
| primary_double | whep | Items with double-counting in production statistics | spec_tbl_df | 18 | 4 |
| regional_mms_distribution | whep | Regional MMS distribution. | tbl_df | 33 | 4 |
| regions_full | whep | Full polity and region reference table | tbl_df | 272 | 39 |
| residue_humification | whep | Humification fraction by carbon input type. | spec_tbl_df | 10 | 3 |
| sjos_levels | whep | Safe-and-just nitrogen classification levels and colours. | tbl_df | 6 | 3 |
| smil_2001_synthetic_n_global | whep | Smil (2001) global synthetic nitrogen production, 1913-2000 | spec_tbl_df | 18 | 2 |
| soc_soil_cover_curve | whep | Generic land-use soil-cover curve for the RothC/HSOC cover factor. | tbl_df | 36 | 3 |
| soc_turnover_params | whep | Soil organic carbon turnover parameters by model. | spec_tbl_df | 43 | 6 |
| soil_cn_ratios | whep | Soil carbon-to-nitrogen ratios for organic-matter balances. | spec_tbl_df | 4 | 5 |
| soil_hydraulic_by_texture | whep | Soil hydraulic properties by USDA texture class. | spec_tbl_df | 12 | 4 |
| som_ranges | whep | Soil organic matter content bins. | spec_tbl_df | 3 | 3 |
| subsoil_no3_reduction | whep | Subsoil nitrate reduction shares by source, climate and irrigation. | spec_tbl_df | 36 | 4 |
| temperature_adjustment | whep | Temperature adjustment factors for NEm. | tbl_df | 3 | 4 |
| uncertainty_ranges | whep | Uncertainty ranges for emission parameters. | tbl_df | 5 | 4 |
| urban_kgn_cap_reference | whep | Spain historical per-capita urban nitrogen rate. | spec_tbl_df | 10 | 2 |
| urban_n_reference | whep | Spain historical urban nitrogen applied to agriculture. | tbl_df | 10 | 4 |
| whep_inputs | whep | External inputs | spec_tbl_df | 74 | 3 |
| fruits | eulerr | Fruits | data.frame | 100 | 5 |
| organisms | eulerr | Organisms | matrix | 7 | 5 |
| pain | eulerr | Pain distribution data | table | 3 | 2 |
| plants | eulerr | Plants | list | | |
| dat.adams | altmeta | A Meta-Analysis on the Effects of Fluvastatin 20 mg/day on High-Density Lipoprotein | data.frame | 32 | 2 |
| dat.aex | altmeta | A Meta-Analysis for Evaluating the Effect of Aerobic Exercise on Visceral Adipose Tissue Content/Volume | data.frame | 29 | 2 |
| dat.annane | altmeta | A Meta-Analysis for Comparing the Effect of Steroids vs. Control in the Length of Intensive Care Unit (ICU) Stay | data.frame | 12 | 5 |
| dat.baker | altmeta | A Network Meta-Analysis on Effects of Pharmacologic Treatments for Chronic Obstructive Pulmonary Disease | data.frame | 91 | 4 |
| dat.barlow | altmeta | A Meta-Analysis on the Effect of Parent Training Programs vs. Control for Improving Parental Psychosocial Health Within 4 Weeks After Intervention | data.frame | 26 | 5 |
| dat.beck17 | altmeta | A Meta-Analysis of Prevalence of Depression or Depressive Symptoms Among Medical Students | data.frame | 6 | 2 |
| dat.bellamy | altmeta | A Meta-Analysis on Type 2 Diabetes Mellitus After Gestational Diabetes | data.frame | 40 | 4 |
| dat.bjelakovic | altmeta | A Meta-Analysis on the Beneficial and Harmful Effects of Vitamin D Supplementation for the Prevention of Mortality in Healthy Adults and Adults in a Stable Phase of Disease | data.frame | 53 | 6 |
| dat.bohren | altmeta | A Meta-Analysis on the Effects of Continuous, One-To-One Intrapartum Support on Spontaneous Vaginal Births, Compared With Usual Care | data.frame | 21 | 6 |
| dat.butters | altmeta | A Meta-Analysis on the Overall Response of the Addition of Drugs to a Chemotherapy Regimen for Metastatic Breast Cancer | data.frame | 16 | 7 |
| dat.carless | altmeta | A Meta-Analysis on the Efficacy of Platelet-Rich-Plasmapheresis in Reducing Peri-Operative Allogeneic Red Blood Cell Transfusion in Cardiac Surgery | data.frame | 20 | 6 |
| dat.chor | altmeta | A Meta-Analysis of Proportions on Chorioamnionitis | data.frame | 21 | 2 |
| dat.dep | altmeta | A Meta-Analysis of Binary and Continuous Outcomes on Depression | data.frame | 6 | 15 |
| dat.ducharme | altmeta | A Meta-Analysis on the Effect of Long-Acting Inhaled Beta2-Agonists vs. Control for Chronic Asthma | data.frame | 33 | 4 |
| dat.fib | altmeta | A Multivariate Meta-Analysis by the Fibrinogen Studies Collaboration | list | | |
| dat.ha | altmeta | A Meta-Analysis on the Effect of Placebo Interventions for All Clinical Conditions Regarding Patient-Reported Outcomes | data.frame | 109 | 2 |
| dat.henry | altmeta | A Meta-Analysis for Evaluating the Effect of Tranexamic Acid on Perioperative Allogeneic Blood Transfusion | data.frame | 26 | 4 |
| dat.hipfrac | altmeta | A Meta-Analysis on the Magnitude and Duration of Excess Mortality After Hip Fracture Among Older Men | data.frame | 17 | 2 |
| dat.hughes | altmeta | A Meta-Analysis on the Effects of Ovulation Suppression for Endometriosis in Subfertile Women | data.frame | 11 | 2 |
| dat.kaner | altmeta | A Meta-Analysis on the Effect of Brief Alcohol Interventions vs. Control in Primary Care Populations | data.frame | 13 | 7 |
| dat.lcj | altmeta | A Meta-Analysis on the Effect of Progressive Resistance Strength Training Exercise vs. Control | data.frame | 33 | 2 |
| dat.paige | altmeta | A Meta-Analysis on the Effectiveness of Spinal Manipulative Therapies (Other Than Sham) | data.frame | 6 | 5 |
| dat.plourde | altmeta | A Meta-Analysis for Comparing the Fluoroscopy Time in Percutaneous Coronary Intervention Between Radial and Femoral Accesses | data.frame | 19 | 5 |
| dat.poole | altmeta | A Meta-Analysis for Evaluating the Effect of Mucolytic on Bronchitis/Chronic Obstructive Pulmonary Disease | data.frame | 24 | 4 |
| dat.pte | altmeta | Meta-Analysis of Multiple Risk Factors for Pterygium | list | | |
| dat.sc | altmeta | A Network Meta-Analysis on Smoking Cessation | spec_tbl_df | 50 | 4 |
| dat.scheidler | altmeta | Meta-Analysis on the Utility of Lymphangiography, Computed Tomography, and Magnetic Resonance Imaging for the Diagnosis of Lymph Node Metastasis | data.frame | 44 | 5 |
| dat.sherrington | altmeta | A Meta-Analysis on the Effects of Exercise Interventions for Preventing Falls in Older People | data.frame | 19 | 6 |
| dat.slf | altmeta | A Meta-Analysis on the Effect of Nicotine Gum for Smoking Cessation | data.frame | 56 | 2 |
| dat.smith | altmeta | Meta-Analysis on the Diagnostic Accuracy of Ultrasound for Detecting Partial Thickness Rotator Cuff Tears | data.frame | 30 | 4 |
| dat.whiting | altmeta | A Meta-Analysis on Adverse Events for the Comparison Cannabinoid vs. Placebo | data.frame | 29 | 9 |
| dat.williams | altmeta | A Meta-Analysis on the Effect of Pharmacotherapy for Social Anxiety Disorder | data.frame | 20 | 7 |
| dat.xu | altmeta | A Network Meta-Analysis on Immune Checkpoint Inhibitor Drugs | spec_tbl_df | 47 | 4 |
| rf.data | RobustMetrics | Example Random Forest Data | data.frame | 29999 | 2 |
| demand_temp | jumps | Electricity demand and temperature: the analysis sample | data.frame | 1691 | 2 |
| employed_IT | jumps | Employed people in Italy by age class | mts | 126 | 10 |
| load_temp | jumps | Hourly electricity load of Northern Italy and temperature of Milan | data.frame | 24092 | 3 |
| A3 | trajectories | Trajectory | Track | | |
| storms | trajectories | Storm trajectories | TracksCollection | | |
| MTPL | insurancerating | Motor Third Party Liability (MTPL) portfolio | tbl_df | 30000 | 7 |
| MTPL2 | insurancerating | Motor Third Party Liability (MTPL) portfolio (3,000 policyholders) | tbl_df | 3000 | 6 |
| air | spacetime | Air quality data, rural background PM10 in Germany, daily averages 1998-2009 | matrix | 70 | |
| dates | spacetime | Air quality data, rural background PM10 in Germany, daily averages 1998-2009 | Date | | |
| DE | spacetime | Air quality data, rural background PM10 in Germany, daily averages 1998-2009 | SpatialPolygons | | |
| DE_NUTS1 | spacetime | Air quality data, rural background PM10 in Germany, daily averages 1998-2009 | SpatialPolygonsDataFrame | | |
| fires | spacetime | Northern Los Angeles County Fires | data.frame | 313 | 3 |
| stations | spacetime | Air quality data, rural background PM10 in Germany, daily averages 1998-2009 | SpatialPoints | | |
| blackbear.0 | secr | Black Bear Hair Snag Dataset | secr | | |
| blackbear.h2bk | secr | Black Bear Hair Snag Dataset | secr | | |
| blackbearCH | secr | Black Bear Hair Snag Dataset | capthist | | |
| captdata | secr | SECR Models Fitted to Demonstration Data | capthist | | |
| captXY | secr | SECR Models Fitted to Demonstration Data | data.frame | 235 | 5 |
| deermouse.ESG | secr | Deermouse Live-trapping Datasets | capthist | | |
| deermouse.WSG | secr | Deermouse Live-trapping Datasets | capthist | | |
| FTHL.0 | secr | Flat-tailed Horned Lizard Dataset | secr | | |
| FTHL.CL | secr | Flat-tailed Horned Lizard Dataset | secr | | |
| GSM | secr | Black Bear Hair Snag Dataset | sfc_POLYGON | | |
| hornedlizardCH | secr | Flat-tailed Horned Lizard Dataset | capthist | | |
| housemouse | secr | House mouse live trapping data | capthist | | |
| infraCH | secr | Skink Pitfall Data | capthist | | |
| lineoCH | secr | Skink Pitfall Data | capthist | | |
| LStraps | secr | Skink Pitfall Data | traps | 462 | 2 |
| ovenbird.model.1 | secr | Ovenbird Mist-netting Dataset | secr | | |
| ovenbird.model.D | secr | Ovenbird Mist-netting Dataset | secr | | |
| ovenbird.model.Dl | secr | Ovenbird Mist-netting Dataset | secr | | |
| ovenCH | secr | Ovenbird Mist-netting Dataset | capthist | | |
| ovenCHp | secr | Ovenbird Mist-netting Dataset | capthist | | |
| ovenmask | secr | Ovenbird Mist-netting Dataset | mask | | |
| ovensong.model.1 | secr | Ovenbird Acoustic Dataset | secr | | |
| ovensong.model.2 | secr | Ovenbird Acoustic Dataset | secr | | |
| OVpossumCH | secr | Orongorongo Valley Brushtail Possums | capthist | | |
| possum.model.0 | secr | Brushtail Possum Trapping Dataset | secr | | |
| possum.model.Ds | secr | Brushtail Possum Trapping Dataset | secr | | |
| possumarea | secr | Brushtail Possum Trapping Dataset | data.frame | 152 | 2 |
| possumCH | secr | Brushtail Possum Trapping Dataset | capthist | | |
| possummask | secr | Brushtail Possum Trapping Dataset | mask | 5120 | 2 |
| possumremovalarea | secr | Brushtail Possum Trapping Dataset | data.frame | 116 | 2 |
| secrdemo.0 | secr | SECR Models Fitted to Demonstration Data | secr | | |
| secrdemo.b | secr | SECR Models Fitted to Demonstration Data | secr | | |
| secrdemo.CL | secr | SECR Models Fitted to Demonstration Data | secr | | |
| signalCH | secr | Ovenbird Acoustic Dataset | capthist | | |
| stoat.model.EX | secr | Stoat DNA Data | secr | | |
| stoat.model.HN | secr | Stoat DNA Data | secr | | |
| stoatCH | secr | Stoat DNA Data | capthist | | |
| trapXY | secr | SECR Models Fitted to Demonstration Data | data.frame | 100 | 3 |
| epilepsy | brms | Epileptic seizure counts | data.frame | 236 | 9 |
| inhaler | brms | Clarity of inhaler instructions | data.frame | 572 | 5 |
| kidney | brms | Infections in kidney patients | data.frame | 76 | 7 |
| loss | brms | Cumulative Insurance Loss Payments | data.frame | 55 | 4 |
| dataEnrichmentMeans | rpact | Enrichment Dataset of Means | data.frame | 12 | 14 |
| dataEnrichmentMeansStratified | rpact | Stratified Enrichment Dataset of Means | data.frame | 24 | 14 |
| dataEnrichmentRates | rpact | Enrichment Dataset of Rates | data.frame | 6 | 10 |
| dataEnrichmentRatesStratified | rpact | Stratified Enrichment Dataset of Rates | data.frame | 12 | 10 |
| dataEnrichmentSurvival | rpact | Enrichment Dataset of Survival Data | data.frame | 9 | 8 |
| dataEnrichmentSurvivalStratified | rpact | Stratified Enrichment Dataset of Survival Data | data.frame | 12 | 10 |
| dataMeans | rpact | One-Arm Dataset of Means | data.frame | 3 | 7 |
| dataMultiArmMeans | rpact | Multi-Arm Dataset of Means | data.frame | 2 | 25 |
| dataMultiArmRates | rpact | Multi-Arm Dataset of Rates | data.frame | 2 | 13 |
| dataMultiArmSurvival | rpact | Multi-Arm Dataset of Survival Data | data.frame | 2 | 13 |
| dataRates | rpact | One-Arm Dataset of Rates | data.frame | 4 | 5 |
| dataSurvival | rpact | One-Arm Dataset of Survival Data | data.frame | 3 | 7 |
| rawDataTwoArmNormal | rpact | Raw Dataset Of A Two Arm Continuous Outcome With Covariates | data.frame | 522 | 8 |
| sailor_keys | trustmebro | key data on students from the sailor moon universe | tbl_df | 20 | 12 |
| sailor_students | trustmebro | assessment data on students from the sailor moon universe | tbl_df | 12 | 6 |
| trial_cea | CEACT | Example Trial-Based Cost-Effectiveness Dataset | data.frame | 500 | 10 |
| isocountry | isocountry | Country names with ISO country codes | tbl_df | 249 | 17 |
| isocurrency | isocountry | ISO currency codes | tbl_df | 262 | 6 |
| Fluency | NormData | Verbal fluency data | data.frame | 1241 | 3 |
| GCSE | NormData | GCSE exam score | data.frame | 1905 | 3 |
| Personality | NormData | Data of the Openness scale of a personality test | data.frame | 2137 | 3 |
| STAS | NormData | State-Trait Anger Scale (STAS) | data.frame | 316 | 3 |
| Substitution | NormData | Substitution test data | data.frame | 1650 | 5 |
| TMAS | NormData | TMAS data | data.frame | 523 | 3 |
| VLT | NormData | Verbal Learning Test data | data.frame | 1460 | 5 |
| sosialFiktiv | SSBtools | Fictitious datasets returned by SSBtoolsData() | data.frame | 5244 | 7 |
| z1 | SSBtools | Fictitious datasets returned by SSBtoolsData() | data.frame | 32 | 3 |
| z1micro | SSBtools | Fictitious datasets returned by SSBtoolsData() | data.frame | 596 | 3 |
| z1w | SSBtools | Fictitious datasets returned by SSBtoolsData() | data.frame | 8 | 5 |
| z2 | SSBtools | Fictitious datasets returned by SSBtoolsData() | data.frame | 44 | 5 |
| z2w | SSBtools | Fictitious datasets returned by SSBtoolsData() | data.frame | 11 | 7 |
| z3 | SSBtools | Fictitious datasets returned by SSBtoolsData() | data.frame | 432 | 7 |
| z3w | SSBtools | Fictitious datasets returned by SSBtoolsData() | data.frame | 36 | 15 |
| z3wb | SSBtools | Fictitious datasets returned by SSBtoolsData() | data.frame | 36 | 15 |
| CEMSChoice | psychotree | CEMS University Choice Data | data.frame | 303 | 10 |
| DIFSim | psychotree | Artificial Data with Differential Item Functioning | data.frame | 200 | 4 |
| DIFSimPC | psychotree | Artificial Data with Differential Item Functioning | data.frame | 500 | 4 |
| EuropeanValuesStudy | psychotree | European Values Study | data.frame | 3584 | 10 |
| SPISA | psychotree | SPIEGEL Studentenpisa Data (Subsample) | data.frame | 1075 | 6 |
| Topmodel2007 | psychotree | Attractiveness of Germany's Next Topmodels 2007 | data.frame | 192 | 6 |
| titanic | stablelearner | Passengers and Crew on the RMS Titanic | data.frame | 2207 | 11 |
| Bikes | pandemonium | Bike sharing data with model information | list | | |
| ns_between_importance_scores | openNCAI | Ecosystem Service Importance Scores (between-service-type) | list | | |
| ns_ci_relevance_matrices | openNCAI | Condition Indicator Relevance Matrix List | list | | |
| ns_ci_scores | openNCAI | Condition Indicator Score Matrix | data.frame | 23 | 38 |
| ns_custom_divisor_matrix | openNCAI | Nature Scot Custom Divisor Matrix | data.frame | 31 | 28 |
| ns_display_ci_names | openNCAI | Display names of Condition Indicators | character | | |
| ns_display_es_label_tree | openNCAI | Display name version of Ecosystem Services Label Tree | list | | |
| ns_display_habitats_label_tree | openNCAI | Display name version of Habitats Label Tree | list | | |
| ns_es_label_tree | openNCAI | Ecosystem Services Label Tree | list | | |
| ns_habitat_extent | openNCAI | Habitat Extent for Scotland | data.frame | 31 | 23 |
| ns_habitats_label_tree | openNCAI | Habitats Label Tree | list | | |
| ns_indicator_directory | openNCAI | Indicator Directory | data.frame | 38 | 4 |
| ns_provision_per_unit_scores | openNCAI | Provision Per Unit Scores | data.frame | 31 | 28 |
| ns_within_importance_scores | openNCAI | Ecosystem Service Importance Scores (within-service-type) | list | | |
| ns_year_list | openNCAI | Year List | character | | |
| supported_models | broom.helpers | Listing of Supported Models | tbl_df | 50 | 2 |
| NURE | ramps | Dataset of USGS NURE Uranium Measurements | data.frame | 298 | 9 |
| NURE.grid | ramps | Dataset of USGS NURE Uranium Measurements | data.frame | 239 | 3 |
| simGrid | ramps | Dataset of Simulated Measurements from JSS Publication | data.frame | 391 | 4 |
| simIowa | ramps | Dataset of Simulated Measurements from JSS Publication | data.frame | 699 | 7 |
| Abasement | BoundIRT | Adjectives Check List data | data.frame | 244 | 10 |
| ACL | BoundIRT | Adjectives Check List data | data.frame | 244 | 219 |
| ACL_index | BoundIRT | Adjectives Check List data | data.frame | 219 | 3 |
| out_beta | BoundIRT | Adjectives Check List data | BoundIRT | | |
| alimentation | coda.base | Food consumption in European countries | data.frame | 25 | 13 |
| arctic_lake | coda.base | Arctic lake sediments at different depths | data.frame | 39 | 5 |
| blood_mn | coda.base | The MN blood system | data.frame | 49 | 5 |
| bmi_activity | coda.base | Physical activity and body mass index | data.frame | 393 | 8 |
| eurostat_employment | coda.base | Employment distribution in EUROSTAT countries | data.frame | 29 | 17 |
| foraminiferals | coda.base | Paleocological compositions | data.frame | 30 | 5 |
| house_expend | coda.base | Household expenditures | data.frame | 27 | 15 |
| household_budget | coda.base | Household budget patterns | data.frame | 40 | 6 |
| kilauea_iki | coda.base | Chemical composition of volcanic rocks from Kilauea Iki | data.frame | 17 | 11 |
| mammals_milk | coda.base | Mammals' milk | data.frame | 24 | 6 |
| milk_cows | coda.base | Milk composition study | tbl_df | 116 | 10 |
| montana | coda.base | Concentration of minor elements in coal ashes | data.frame | 229 | 6 |
| parliament2017 | coda.base | Catalan Parliament election results in 2017 by region | tbl_df | 42 | 9 |
| petrafm | coda.base | Calc-alkaline and tholeiitic volcanic rocks | data.frame | 100 | 4 |
| pollen | coda.base | Pollen composition in fossils | data.frame | 30 | 4 |
| pottery | coda.base | Chemical compositions of Romano-British pottery | data.frame | 45 | 11 |
| serprot | coda.base | Serum proteins | data.frame | 36 | 7 |
| statistician_time | coda.base | A statistician's time budget | data.frame | 20 | 7 |
| waste | coda.base | Urban waste composition in Catalonia | data.frame | 215 | 10 |
| weibo_hotels | coda.base | Hotel posts in social media | data.frame | 10 | 5 |
| dta_file | labelled | Datasets for testing | data.frame | 47 | 6 |
| spss_file | labelled | Datasets for testing | list | | |
| x_haven_2.0 | labelled | Datasets for testing | haven_labelled | | |
| x_spss_haven_2.0 | labelled | Datasets for testing | haven_labelled_spss | | |
| ai_long | transitiontrees | AI-collaboration messages (long format) | data.frame | 8551 | 9 |
| engagement | transitiontrees | Student engagement state sequences (stslist) | stslist | 1000 | 25 |
| group_regulation_long | transitiontrees | Collaborative-regulation events (long format) | data.frame | 27533 | 6 |
| trajectories | transitiontrees | Student engagement trajectories | matrix | 138 | 15 |
| jellyfisher_example_tables | jellyfisher | Jellyfisher example data | list | | |
| peacekeeping | underdisp | UN peacekeeping contributions, state-years | data.frame | 4448 | 9 |
| achieve_3L_data | MLMES | Three-Level Student Achievement Dataset | data.frame | 10320 | 9 |
| hsb_data | MLMES | High School and Beyond (HSB) Dataset | data.frame | 7185 | 27 |
| readgrowth_data | MLMES | Early Reading Growth Dataset | data.frame | 316 | 18 |
| brysbaert | themescopeR | Brysbaert et al. (2014) concreteness norms | data.frame | 39954 | 2 |
| nc | ggincerta | North Carolina SIDS data | sf | 100 | 19 |
| nc_sim | ggincerta | Simulated North Carolina spatial patterns | sf | 600 | 23 |
| nc_sim1 | ggincerta | | sf | 600 | 23 |
| components | kpp2019 | Components of Population Change in Kenya (2020-2045) | tbl_df | 1920 | 4 |
| pop1 | kpp2019 | Base and Projected Age Distributions in Kenya (2020-2035, Annual Increments) | tbl_df | 41472 | 5 |
| pop5 | kpp2019 | Base and Projected Age Distributions in Kenya (2020-2045, Five-Year Increments) | tbl_df | 15552 | 5 |
| jalisco | RtForecastR | Weekly measles cases, Jalisco (larger-outbreak example) | data.frame | 41 | 2 |
| measles_cdmx | RtForecastR | Weekly measles cases, Ciudad de Mexico (worked example) | data.frame | 32 | 2 |
| crop_data | robustrcp | Contaminated Sample Dataset for Robust Estimation | data.frame | 500 | 3 |
| Rice_Zhao_etal | RAINBOWR | Rice_Zhao_etal: | list | | |
| mimiv_demo | CopulaSCR | MIMIC-IV Demo Dataset | data.table | 100 | 7 |
| mSCRdata | CopulaSCR | Example Semi-Competing Risks Dataset with Multiple Intermediate Events | mscrData | | |
| SCRdata | CopulaSCR | Example Semi-Competing Risks Dataset with a Single Intermediate Event | data.frame | 100 | 4 |
| SCRdata_by_tr | CopulaSCR | Example Semi-Competing Risks Dataset by Treatment Group | data.frame | 500 | 5 |
| HumanCellTypeMethAtlas | deconvR | The comprehensive human methylome reference atlas | data.frame | 6105 | 26 |
| IlluminaMethEpicB5ProbeIDs | deconvR | A dataset Illumina probe IDs of 400000 genomic loci (identified using the “seqnames”, “ranges”, and “strand” values). | GRanges | | |
| nor_covid19_cases_by_time_location | plnr | Covid-19 data for PCR-confirmed cases in Norway (nation and county) | data.table | 11028 | 18 |
| cross_1year | plot3logit | | data.frame | 3282 | 7 |
| USvote2016 | plot3logit | | tbl_df | 8000 | 7 |
| iris2 | tablesgg | A Reshaped Version of Anderson's Iris Data | data.frame | 600 | 5 |
| iris2_tab | tablesgg | Table of Summary Statistics for Anderson's Iris Data | tabular | 6 | 4 |
| mtcars_xtab | tablesgg | Table of Data from Motor Trend Magazine | xtableList | | |
| styles_pkg | tablesgg | Built-In Styles for Table Elements | list | | |
| tli_xtab | tablesgg | Table of Test Scores and Demographics for 20 Students | xtable | 20 | 5 |
| pk_tab | chromatographR | Goldenrod peak table | peak_table | | |
| Sa | chromatographR | Raw goldenrod root chromatograms | list | | |
| Sa_pr | chromatographR | Preprocessed goldenrod root chromatograms | list | | |
| Sa_warp | chromatographR | Warped goldenrod root chromatograms. | list | | |
| cigar | xtife | Dataset on US Cigarette Demand Panel | data.frame | 1380 | 9 |
| normforest | geodiv | NDVI errors for a portion of southwestern Oregon, USA. | PackedSpatRaster | | |
| orelevation | geodiv | SRTM elevation for a portion of southwestern Oregon, USA. | PackedSpatRaster | | |
| orforest | geodiv | NDVI for a portion of southwestern Oregon, USA. | PackedSpatRaster | | |
| ARNT.metadata | TFEA.ChIP | Metadata data frame | data.frame | 1 | 7 |
| ARNT.peaks.bed | TFEA.ChIP | ChIP-Seq dataset | data.frame | 2140 | 4 |
| chip_metadata | TFEA.ChIP | ChIP-seq transcription factor metadata | data.frame | 1267 | 3 |
| ChIPDB | TFEA.ChIP | TF-Gene List | list | | |
| DnaseHS_db | TFEA.ChIP | DHS databse | GRanges | | |
| Entrez.gene.IDs | TFEA.ChIP | List of Entrez Gene IDs | character | | |
| Genes.Upreg | TFEA.ChIP | List of Entrez Gene IDs | character | | |
| gr.list | TFEA.ChIP | List of one ChIP-Seq dataset | list | | |
| GSEA.result | TFEA.ChIP | Output of the function GSEA.run from the TFEA.ChIP package | list | | |
| hypoxia | TFEA.ChIP | RNA-Seq experiment | data.frame | 17527 | 4 |
| hypoxia_DESeq | TFEA.ChIP | RNA-Seq experiment | DESeqResults | | |
| log2.FC | TFEA.ChIP | List of Entrez Gene IDs | numeric | | |
| MetaData | TFEA.ChIP | TF-gene binding DB metadata | data.frame | 8103 | 6 |
| TF_ranking2 | TFEA.ChIP | Meta-analysis ranking of transcription factors | list | | |
| electData | elect | Three-state data for exploring 'elect' | data.frame | 764 | 5 |
| aastveit.barley.covs | agridat | Barley heights and environmental covariates in Norway | data.frame | 9 | 20 |
| aastveit.barley.height | agridat | Barley heights and environmental covariates in Norway | data.frame | 135 | 3 |
| acorsi.grayleafspot | agridat | Multi-environment trial evaluating 36 maize genotypes in 9 locations | data.frame | 648 | 4 |
| adugna.sorghum | agridat | Multi-environment trial of sorghum at 3 locations across 5 years | data.frame | 289 | 6 |
| aguileracarreras.bean.uniformity | agridat | Uniformity trial of beans in El Salvador | data.frame | 256 | 3 |
| allcroft.lodging | agridat | Multi-environment trial of cereal with lodging data | data.frame | 224 | 3 |
| alvarezcajas.sugarcane.uniformity | agridat | Uniformity trial of sugarcane in Guatemala | data.frame | 600 | 3 |
| alwan.lamb | agridat | For the 34 sheep sires, the number of lambs in each of 5 foot shape classes. | data.frame | 340 | 11 |
| andres.rice.uniformity | agridat | A uniformity trial of rice | data.frame | 990 | 3 |
| ansari.wheat.uniformity | agridat | Uniformity trial of wheat | data.frame | 768 | 3 |
| archbold.apple | agridat | Split-split plot experiment of apple trees | data.frame | 120 | 8 |
| ars.earlywhitecorn96 | agridat | Multi-environment trial of early white food corn | data.frame | 540 | 9 |
| athulya.rice.uniformity | agridat | Uniformity trial of rice in India | data.frame | 400 | 3 |
| australia.soybean | agridat | Multi-environment trial of soybean in Australia | data.frame | 464 | 10 |
| bachmaier.nitrogen | agridat | Trial of wheat with nitrogen fertilizer in two fertility zones | data.frame | 88 | 3 |
| baena.bean.uniformity | agridat | Uniformity trial of beans in Columbia | data.frame | 1296 | 3 |
| bailey.cotton.uniformity | agridat | Uniformity trial of cotton in Egypt | data.frame | 794 | 5 |
| baker.barley.uniformity | agridat | Uniformity trials of barley, 10 years on same ground | data.frame | 570 | 4 |
| baker.strawberry.uniformity | agridat | Uniformity trial of strawberry | data.frame | 700 | 4 |
| baker.wheat.uniformity | agridat | Uniformity trial of wheat | data.frame | 225 | 3 |
| bancroft.peanut.uniformity | agridat | Uniformity trial of peanuts | data.frame | 216 | 4 |
| barrantesaguilar.sugarcane.uniformity | agridat | Uniformity trial of sugarcane in Costa Rica | data.frame | 1600 | 3 |
| barrero.maize | agridat | Multi-environment trial of maize in Texas. | data.frame | 14568 | 14 |
| barrientosgarcia.maize.uniformity | agridat | Uniformity trials of sesame and maize in Guatemala | data.frame | 500 | 3 |
| barrientosgarcia.sesame.uniformity | agridat | Uniformity trials of sesame and maize in Guatemala | data.frame | 432 | 3 |
| batchelor.apple.uniformity | agridat | Uniformity trials of apples, lemons, oranges, and walnuts | data.frame | 224 | 3 |
| batchelor.lemon.uniformity | agridat | Uniformity trials of apples, lemons, oranges, and walnuts | data.frame | 364 | 3 |
| batchelor.navel1.uniformity | agridat | Uniformity trials of apples, lemons, oranges, and walnuts | data.frame | 1000 | 3 |
| batchelor.navel2.uniformity | agridat | Uniformity trials of apples, lemons, oranges, and walnuts | data.frame | 495 | 3 |
| batchelor.valencia.uniformity | agridat | Uniformity trials of apples, lemons, oranges, and walnuts | data.frame | 240 | 3 |
| batchelor.walnut.uniformity | agridat | Uniformity trials of apples, lemons, oranges, and walnuts | data.frame | 280 | 3 |
| battese.survey | agridat | Survey and satellite data for corn and soy areas in Iowa | data.frame | 37 | 9 |
| beall.webworms | agridat | Counts of webworms in a beet field, with insecticide treatments. | data.frame | 1300 | 7 |
| beaven.barley | agridat | Yields of 8 barley varieties in 1913 as used by Student. | data.frame | 160 | 4 |
| becker.chicken | agridat | Mating crosses of chickens | data.frame | 45 | 3 |
| beckett.maize.uniformity | agridat | A uniformity trial of maize in Ghana. | data.frame | 83 | 8 |
| belamkar.augmented | agridat | Multi-environment trial of wheat with Augmented design | data.frame | 2700 | 9 |
| besag.bayesian | agridat | RCB experiment of spring barley in United Kingdom | data.frame | 225 | 4 |
| besag.beans | agridat | Competition experiment in beans with height measurements | data.frame | 152 | 6 |
| besag.checks | agridat | Check variety yields in winter wheat. | data.frame | 364 | 4 |
| besag.elbatan | agridat | RCB experiment of wheat, 50 varieties in 3 blocks with strong spatial trend. | data.frame | 150 | 4 |
| besag.endive | agridat | Presence of footroot disease in an endive field | data.frame | 2506 | 3 |
| besag.met | agridat | Multi-environment trial of corn, incomplete-block design | data.frame | 1188 | 7 |
| besag.triticale | agridat | Four-way factorial agronomic experiment in triticale | data.frame | 54 | 7 |
| blackman.wheat | agridat | Multi-environment trial of wheat, conventional and semi-dwarf varieties | data.frame | 168 | 5 |
| bliss.borers | agridat | Corn borer infestation under four treatments | data.frame | 48 | 3 |
| bond.diallel | agridat | Diallel cross of winter beans | data.frame | 36 | 11 |
| borden.sugarcane.uniformity | agridat | Uniformity trial of sugarcane in Hawaii | data.frame | 96 | 4 |
| bose.multi.uniformity | agridat | Uniformity trials of barley, wheat, lentils | data.frame | 1170 | 5 |
| box.cork | agridat | Weight of cork samples on four sides of trees | data.frame | 112 | 3 |
| bradley.multi.uniformity | agridat | Uniformity trial of 4 crops on the same land | data.frame | 440 | 5 |
| brandle.rape | agridat | Multi-environment trial of rape in Manitoba | data.frame | 135 | 4 |
| brandt.switchback | agridat | Switchback experiment on dairy cattle, milk yield for two treatments | data.frame | 30 | 5 |
| bridges.cucumber | agridat | Multi-environment trial of cucumbers in a latin square design | data.frame | 32 | 5 |
| broadbalk.wheat | agridat | Long term wheat yields on Broadbalk fields at Rothamsted. | data.frame | 1258 | 4 |
| bryan.corn.uniformity | agridat | Uniformity trial of corn at 3 locations in Iowa. | data.frame | 1728 | 4 |
| buesocampos.melon.uniformity | agridat | Uniformity trial of melon in Guatemala | data.frame | 480 | 3 |
| buesocampos.tomato.uniformity | agridat | A uniformity trial of tomato in Guatemala | data.frame | 260 | 3 |
| buntaran.wheat | agridat | Multi-environment trial of wheat in Sweden in 2016. | data.frame | 1069 | 6 |
| burgueno.alpha | agridat | Incomplete block alpha design | data.frame | 48 | 6 |
| burgueno.rowcol | agridat | Row-column design | data.frame | 128 | 5 |
| burgueno.unreplicated | agridat | Field experiment with unreplicated genotypes plus one repeated check. | data.frame | 434 | 4 |
| butron.maize | agridat | Multi-environment trial of maize with pedigrees | data.frame | 245 | 5 |
| byers.apple | agridat | Diameters of apples | data.frame | 480 | 6 |
| caribbean.maize | agridat | Multi-environment trial of maize with fertilization | data.frame | 612 | 10 |
| carlson.germination | agridat | Germination of alfalfa seeds at various salt concentrations | data.frame | 120 | 3 |
| carmer.density | agridat | Nonlinear maize yield-density model | data.frame | 32 | 3 |
| cate.potassium | agridat | Relative cotton yield for different soil potassium concentrations | data.frame | 24 | 2 |
| chakravertti.factorial | agridat | Factorial experiment of rice, 3x5x3x3 | data.frame | 405 | 7 |
| chinloy.fractionalfactorial | agridat | Fractional factorial of sugarcane, 1/3 3^5 = 3x3x3x3x3 | data.frame | 81 | 10 |
| christidis.competition | agridat | Competition between varieties in cotton | data.frame | 270 | 8 |
| christidis.cotton.uniformity | agridat | Uniformity trial of cotton | data.frame | 1024 | 4 |
| christidis.wheat.uniformity | agridat | Uniformity trial of wheat | data.frame | 288 | 3 |
| cleveland.soil | agridat | Soil resistivity in a field | data.frame | 8641 | 5 |
| clewer.wheat | agridat | Randomized complete block design for wheat | data.frame | 12 | 5 |
| cochran.beets | agridat | Yield and number of plants in a sugarbeet fertilizer experiment | data.frame | 42 | 4 |
| cochran.bib | agridat | Multi-environment trial of corn, balanced incomplete block design | data.frame | 52 | 3 |
| cochran.crd | agridat | Potato scab infection with sulfur treatments | data.frame | 32 | 4 |
| cochran.eelworms | agridat | Counts of eelworms before and after fumigant treatments | data.frame | 48 | 10 |
| cochran.factorial | agridat | Factorial experiment of beans, 2x2x2x2 | data.frame | 32 | 8 |
| cochran.latin | agridat | Latin square design in wheat | data.frame | 36 | 4 |
| cochran.lattice | agridat | Balanced lattice experiment in cotton | data.frame | 80 | 5 |
| cochran.wireworms | agridat | Wireworms controlled by fumigants in a latin square | data.frame | 25 | 4 |
| connolly.potato | agridat | Potato yields in single-drill plots | data.frame | 80 | 6 |
| coombs.rice.uniformity | agridat | Uniformity trial of rice in Malaysia | data.frame | 54 | 3 |
| cornelius.maize | agridat | Multi-environment trial of maize for 9 cultivars at 20 locations. | data.frame | 180 | 3 |
| corsten.interaction | agridat | Multi-environment trial of corn | data.frame | 140 | 3 |
| cox.stripsplit | agridat | Strip-split-plot of barley with fertilizer, calcium, and soil factors. | data.frame | 96 | 5 |
| cramer.cucumber | agridat | Cucumber yields and quantitative traits | data.frame | 24 | 9 |
| crampton.pig | agridat | Weight gain in pigs for different treatments | data.frame | 50 | 5 |
| crossa.wheat | agridat | Multi-environment trial of wheat for 18 genotypes at 25 locations | data.frame | 450 | 5 |
| crowder.seeds | agridat | Germination of Orobanche seeds for two genotypes and two treatments. | data.frame | 21 | 5 |
| cullis.earlygen | agridat | Early generation variety trial in wheat | data.frame | 670 | 6 |
| damesa.maize | agridat | Incomplete-block experiment of maize in Ethiopia. | data.frame | 264 | 8 |
| darwin.maize | agridat | Darwin's maize data of crossed/inbred plant heights | data.frame | 30 | 4 |
| dasilva.maize | agridat | Multi-environment trial of maize | data.frame | 1485 | 4 |
| dasilva.soybean.uniformity | agridat | Uniformity trial of soybean | data.frame | 1152 | 3 |
| davidian.soybean | agridat | Growth of soybean varieties in 3 years | data.frame | 412 | 5 |
| davies.pasture.uniformity | agridat | Uniformity trial of pasture. | data.frame | 760 | 3 |
| day.wheat.uniformity | agridat | Uniformity trial of wheat | data.frame | 3100 | 4 |
| delgadillolopez.sesame.uniformity | agridat | A uniformity trial of sesame | data.frame | 384 | 3 |
| denis.missing | agridat | Multi-environment trial with structured missing values | data.frame | 130 | 3 |
| denis.ryegrass | agridat | Multi-environment trial of perennial ryegrass in France | data.frame | 147 | 3 |
| depalluel.sheep | agridat | Latin square of four breeds of sheep with four diets | data.frame | 32 | 5 |
| desousa.potato | agridat | On-farm tricot evaluation of potatoes | data.frame | 168 | 11 |
| devries.pine | agridat | Graeco-Latin Square experiment in pine | data.frame | 36 | 7 |
| digby.jointregression | agridat | Multi-environment trial of wheat | data.frame | 134 | 3 |
| diggle.cow | agridat | Bodyweight of cows in a 2-by-2 factorial experiment | data.frame | 598 | 5 |
| draper.safflower.uniformity | agridat | Uniformity trial of safflower | data.frame | 640 | 4 |
| ducker.groundnut.uniformity | agridat | Uniformity trial of groundnut | data.frame | 215 | 3 |
| durban.competition | agridat | Sugar beet yields with competition effects | data.frame | 114 | 5 |
| durban.rowcol | agridat | Row-column experiment of spring barley, many varieties | data.frame | 544 | 5 |
| durban.splitplot | agridat | Split-plot experiment of barley with fungicide treatments | data.frame | 560 | 6 |
| eden.nonnormal | agridat | Height of barley plants in a study of non-normal data | data.frame | 256 | 3 |
| eden.potato | agridat | Potato yields in response to potash and nitrogen fertilizer | data.frame | 225 | 9 |
| eden.tea.uniformity | agridat | Uniformity trial of tea | data.frame | 144 | 4 |
| edwards.oats | agridat | Multi-environment trial of oats in United States, 5 locations, 7 years. | data.frame | 3694 | 7 |
| engelstad.nitro | agridat | Multi-environment trial of corn with nitrogen fertilizer | data.frame | 60 | 4 |
| evans.sugarcane.uniformity | agridat | Uniformity trial of sugarcane | data.frame | 710 | 3 |
| fan.stability | agridat | Multi-environment trial of maize hybrids in China | data.frame | 260 | 5 |
| federer.diagcheck | agridat | Wheat experiment with diagonal checks | data.frame | 180 | 4 |
| federer.tobacco | agridat | RCB of tobacco, height plants exposed to radiation | data.frame | 56 | 4 |
| fisher.barley | agridat | Multi-environment trial of 5 barley varieties, 6 locations, 2 years | data.frame | 60 | 4 |
| fisher.latin | agridat | Latin square experiment on mangolds | data.frame | 25 | 4 |
| forster.wheat.uniformity | agridat | Uniformity trial of wheat in Australia. | data.frame | 160 | 3 |
| foulley.calving | agridat | Calving difficulty by calf sex and age of dam | data.frame | 54 | 4 |
| fox.wheat | agridat | Multi-environment trial of wheat, 22 varieties at 14 sites in Australia | data.frame | 308 | 4 |
| garber.frames.uniformity | agridat | Uniformity trials of soybeans and wheat in artificially constructed frames | data.frame | 180 | 7 |
| garber.multi.uniformity | agridat | Uniformity trials of oat hay and wheat grain | data.frame | 3060 | 6 |
| gartner.corn | agridat | Yield monitor data from a corn field in Minnesota | data.frame | 4949 | 8 |
| gathmann.bt | agridat | Impact of Bt corn on non-target species | data.frame | 16 | 3 |
| gauch.soy | agridat | Multi-environment trial of soybeans in New York, 1977 to 1988 | data.frame | 1454 | 6 |
| george.wheat | agridat | Multi-location/year breeding trial in California | data.frame | 13996 | 5 |
| giles.wheat | agridat | Straw length and ear emergence for wheat genotypes. | data.frame | 247 | 4 |
| gilmour.serpentine | agridat | Wheat yield in South Australia with serpentine row/col effects | data.frame | 330 | 5 |
| gilmour.slatehall | agridat | Slate Hall Farm 1978 | data.frame | 150 | 5 |
| gomez.fractionalfactorial | agridat | Fractional factorial of rice, 1/2 2^6 = 2x2x2x2x2x2 | data.frame | 64 | 12 |
| gomez.groupsplit | agridat | Group balanced split-plot design in rice | data.frame | 270 | 7 |
| gomez.heterogeneity | agridat | RCB experiment of rice, heterogeneity of regressions | data.frame | 21 | 3 |
| gomez.heteroskedastic | agridat | RCB experiment of rice, heteroskedastic varieties | data.frame | 105 | 4 |
| gomez.multilocsplitplot | agridat | Multi-environment trial of rice, split-plot design | data.frame | 108 | 5 |
| gomez.nitrogen | agridat | Soil nitrogen at three times for eight fertilizer treatments | data.frame | 96 | 4 |
| gomez.nonnormal1 | agridat | Insecticide treatment effectiveness | data.frame | 36 | 3 |
| gomez.nonnormal2 | agridat | RCB experiment of rice, measuring white heads | data.frame | 42 | 3 |
| gomez.nonnormal3 | agridat | RCB experiment of rice, 12 varieties with leafhopper survival | data.frame | 36 | 3 |
| gomez.rice.uniformity | agridat | Uniformity trial of rice | data.frame | 648 | 3 |
| gomez.seedrate | agridat | RCB experiment of rice, 6 densities | data.frame | 24 | 3 |
| gomez.splitplot.subsample | agridat | Split-plot experiment of rice, with subsamples | data.frame | 192 | 5 |
| gomez.splitsplit | agridat | Split-split-plot experiment of rice | data.frame | 135 | 7 |
| gomez.stripplot | agridat | Strip-plot experiment of rice | data.frame | 54 | 6 |
| gomez.stripsplitplot | agridat | Strip-split-plot experiment of rice | data.frame | 108 | 7 |
| gomez.wetdry | agridat | Rice yield in wet & dry seasons with nitrogen fertilizer treatments | data.frame | 30 | 4 |
| gorski.oats.uniformity | agridat | Uniformity trial of oats in Poland | data.frame | 500 | 4 |
| gotway.hessianfly | agridat | Hessian fly damage to wheat varieties | data.frame | 64 | 6 |
| goulden.barley.uniformity | agridat | Uniformity trial of barley | data.frame | 2304 | 3 |
| goulden.eggs | agridat | Sample of egg weights on 24 consecutive days | data.frame | 240 | 2 |
| goulden.latin | agridat | Latin square experiment for testing fungicide | data.frame | 25 | 4 |
| goulden.splitsplit | agridat | Split-split-plot experiment of wheat | data.frame | 160 | 9 |
| graybill.heteroskedastic | agridat | Multi-environment trial of wheat varieties with heteroskedastic yields | data.frame | 52 | 3 |
| gregory.cotton | agridat | Factorial experiment of cotton in Sudan. | data.frame | 144 | 6 |
| grover.diallel | agridat | Diallel 6x6 | data.frame | 144 | 5 |
| grover.rcb.subsample | agridat | Rice RCB with subsamples | data.frame | 144 | 4 |
| gumpertz.pepper | agridat | Phytophtera disease incidence in a pepper field | data.frame | 800 | 6 |
| hadasch.lettuce | agridat | Lettuce resistance to downy mildew resistance (with marker data) | data.frame | 703 | 4 |
| hadasch.lettuce.markers | agridat | Lettuce resistance to downy mildew resistance (with marker data) | data.frame | 89 | 301 |
| hanks.sprinkler | agridat | Wheat yields in a line-source sprinkler experiment | data.frame | 108 | 7 |
| hanover.whitepine | agridat | Mating crosses of white pine trees | data.frame | 112 | 4 |
| hansen.multi.uniformity | agridat | Multi-year uniformity trial in Denmark | data.frame | 662 | 6 |
| haritonenko.sugarbeet.uniformity | agridat | Uniformity trial of sugar beet | data.frame | 416 | 3 |
| harris.multi.uniformity | agridat | Uniformity trials with multiple crops, 15 years on the same land | data.frame | 1058 | 5 |
| harris.wateruse | agridat | Water use by horticultural trees | data.frame | 1040 | 5 |
| harrison.priors | agridat | Ranges of analytes in soybean from other authors | data.frame | 80 | 5 |
| hartman.tomato.uniformity | agridat | Uniformity trial of tomato | data.frame | 384 | 3 |
| harvey.lsmeans | agridat | Average daily gain of 65 steers for 3 lines, 9 sires. | data.frame | 65 | 7 |
| harville.lamb | agridat | Birth weight of lambs from different lines/sires | data.frame | 62 | 4 |
| hayman.tobacco | agridat | Diallel cross of Aztec tobacco | data.frame | 256 | 5 |
| hazell.vegetables | agridat | Gross profit for 4 vegetable crops in 6 years | data.frame | 6 | 5 |
| heady.fertilizer | agridat | Yield of corn, alfalfa, clover with two fertilizers | data.frame | 648 | 6 |
| heath.cabbage.uniformity | agridat | Uniformity trial of cabbage. | data.frame | 48 | 3 |
| heath.radish.uniformity | agridat | Uniformity trial of radish | data.frame | 400 | 4 |
| henderson.milkfat | agridat | Milk fat yields for a single cow | data.frame | 35 | 2 |
| hernandez.nitrogen | agridat | Multi-environment trial of corn with nitrogen fertilizer at 5 sites. | data.frame | 136 | 5 |
| hernandezdavila.potato.uniformity | agridat | Uniformity trial of potato in Guatemala | data.frame | 1008 | 4 |
| hessling.argentina | agridat | Relation between wheat yield and weather in Argentina | data.frame | 30 | 16 |
| hildebrand.systems | agridat | Multi-environment trial of maize for four cropping systems | data.frame | 56 | 4 |
| holland.arthropods | agridat | Counts of arthropods in a grid-sampled wheat field | data.frame | 63 | 8 |
| holshouser.splitstrip | agridat | Split-strip-plot of soybeans | data.frame | 160 | 8 |
| holtsmark.timothy.uniformity | agridat | Uniformity trial of timothy grass in Norway | data.frame | 240 | 3 |
| huehn.wheat | agridat | Multi-environment trial of wheat to illustrate stability statistics | data.frame | 200 | 3 |
| hughes.grapes | agridat | RCB experiment of grape, disease incidence | data.frame | 270 | 6 |
| hunter.corn | agridat | Multi-environment trial of corn with nitrogen fertilizer | data.frame | 54 | 4 |
| hutchinson.cotton.uniformity | agridat | Uniformity trial of cotton | data.frame | 2000 | 3 |
| igue.sugarcane.uniformity | agridat | Uniformity trial with sugarcane | data.frame | 1512 | 3 |
| ilri.sheep | agridat | Birth weight and weaning weight of Dorper x Red Maasi lambs | data.frame | 882 | 12 |
| immer.sugarbeet.uniformity | agridat | Uniformity trial of sugarbeets, measurements of yield, sugar, purity | data.frame | 1200 | 6 |
| ivins.herbs | agridat | Percent ground cover of herbage species and nettles. | data.frame | 78 | 4 |
| iyer.wheat.uniformity | agridat | Uniformity trials of wheat in India | data.frame | 2000 | 3 |
| jansen.apple | agridat | Infestation of apple shoots by apple canker. | data.frame | 36 | 5 |
| jansen.carrot | agridat | Infestation of carrots by fly larvae | data.frame | 96 | 5 |
| jansen.strawberry | agridat | Ordered disease ratings of strawberry crosses. | data.frame | 144 | 5 |
| jayaraman.bamboo | agridat | Bamboo progeny trial in India | data.frame | 216 | 5 |
| jayaraman.bamboo.uncorrected | agridat | Bamboo progeny trial in India | data.frame | 216 | 5 |
| jegorow.oats.uniformity | agridat | Uniformity trial of oats in Russia | data.frame | 240 | 3 |
| jenkyn.mildew | agridat | Yields from treatment for mildew control | data.frame | 38 | 4 |
| john.alpha | agridat | Alpha lattice design of spring oats | data.frame | 72 | 7 |
| johnson.blight | agridat | Potato blight due to weather in Prosser, Washington | data.frame | 25 | 6 |
| johnson.douglasfir | agridat | A study of small-plots of old-growth Douglas Fir in Oregon. | data.frame | 1600 | 3 |
| jones.corn.uniformity | agridat | Uniformity trial of corn. | data.frame | 144 | 3 |
| jurowski.wheat.uniformity | agridat | Uniformity trial of wheat in Russia | data.frame | 480 | 3 |
| kadam.millet.uniformity | agridat | Uniformity trial of millet | data.frame | 240 | 4 |
| kalamkar.potato.uniformity | agridat | Uniformity trial of potatoes | data.frame | 576 | 3 |
| kalamkar.wheat.uniformity | agridat | Uniformity trial of wheat | data.frame | 1280 | 4 |
| kang.maize | agridat | Multi-environment trial of maize in Louisianna at 4 locs in 3 years | data.frame | 204 | 5 |
| kang.peanut | agridat | Multi-environment trial of peanuts for 10 genotypes in 15 environments | data.frame | 590 | 4 |
| karcher.turfgrass | agridat | Turfgrass ratings for different treatments | data.frame | 128 | 6 |
| kayad.alfalfa | agridat | Yield monitor data for 4 cuttings of alfalfa in Saudi Arabia. | data.frame | 8628 | 4 |
| keen.potatodamage | agridat | Damage to potato tubers from lifting rods. | data.frame | 1152 | 6 |
| kempton.barley.uniformity | agridat | Uniformity trial of barley | data.frame | 196 | 3 |
| kempton.competition | agridat | Sugar beet trial with competition effects | data.frame | 108 | 5 |
| kempton.rowcol | agridat | Row-column experiment of wheat | data.frame | 68 | 5 |
| kempton.slatehall | agridat | Slate Hall Farm 1976 spring wheat | data.frame | 150 | 5 |
| kenward.cattle | agridat | Repeated measurement of weights of calves with two treatments. | data.frame | 660 | 4 |
| kerr.sugarcane.uniformity | agridat | Uniformity trials of sugarcane, 4 fields | data.frame | 564 | 4 |
| khan.brassica.uniformity | agridat | Uniformity trial of brassica. | data.frame | 648 | 4 |
| khin.rice.uniformity | agridat | Uniformity trial of rice | data.frame | 1080 | 3 |
| kiesselbach.oats.uniformity | agridat | Uniformity trial of oats | data.frame | 207 | 3 |
| kirk.potato | agridat | Variety trial of potatoes, highly replicated | data.frame | 380 | 5 |
| kirk.potato.uniformity | agridat | Uniformity trial of potato in Saskatchewan | data.frame | 36 | 3 |
| kling.augmented | agridat | Augmented design of meadowfoam | data.frame | 68 | 7 |
| kotowski.potato.uniformity | agridat | Uniformity trial of potato in Poland. | data.frame | 152 | 5 |
| kreusler.maize | agridat | Growth of maize plants in Germany during 1875-1878 | data.frame | 165 | 16 |
| kristensen.barley.uniformity | agridat | Uniformity trial of barley | data.frame | 242 | 3 |
| kulkarni.sorghum.uniformity | agridat | Uniformity trial of sorghum | data.frame | 480 | 4 |
| lambert.soiltemp | agridat | Average monthly soil temperature near Zurich | data.frame | 84 | 3 |
| lander.multi.uniformity | agridat | Uniformity trials of wheat and chari, 4 years on the same land. | data.frame | 780 | 5 |
| larsen.timothy.uniformity | agridat | Uniformity trial of timothy grass in Norway | data.frame | 960 | 3 |
| lasrosas.corn | agridat | Yield monitor data for a corn field in Argentina with variable nitrogen. | data.frame | 3443 | 9 |
| lavoranti.eucalyptus | agridat | Height of Eucalyptus trees in southern Brazil | data.frame | 490 | 4 |
| laycock.tea.uniformity | agridat | Uniformity trials of tea | data.frame | 86 | 4 |
| lechadolopez.sorghum.uniformity | agridat | Uniformity trial of sorghum in Nicaragua | data.frame | 512 | 3 |
| lee.potatoblight | agridat | Repeated measurements of resistance to potato blight | data.frame | 14570 | 7 |
| lehmann.millet.uniformity | agridat | Uniformity trial of millet in India | data.frame | 396 | 5 |
| lehmann.rice.uniformity | agridat | Uniformity trial of rice in India | data.frame | 153 | 5 |
| lehner.soybeanmold | agridat | Yield, white mold, and sclerotia for soybeans in Brazil | data.frame | 382 | 9 |
| lessman.sorghum.uniformity | agridat | Uniformity trial of sorghum | data.frame | 2640 | 3 |
| li.millet.uniformity | agridat | Uniformity trial of millet | data.frame | 600 | 3 |
| lillemo.wheat | agridat | Multi-environment trial of wheat susceptibile to powdery mildew | data.frame | 408 | 4 |
| lin.superiority | agridat | Multi-environment trial of 33 barley genotypes in 12 locations | data.frame | 396 | 4 |
| lin.unbalanced | agridat | Multi-environment trial of 33 barley genotypes in 18 locations | data.frame | 405 | 4 |
| linder.wheat | agridat | Multi-environment trial of wheat in Switzerland | data.frame | 252 | 4 |
| little.splitblock | agridat | Split-block experiment of sugar beets | data.frame | 80 | 6 |
| loesell.bean.uniformity | agridat | Uniformity trial of white pea beans | data.frame | 1890 | 3 |
| lonnquist.maize | agridat | Multi-environment trial of maize, half diallel | data.frame | 78 | 3 |
| lord.rice.uniformity | agridat | Uniformity trial of rice | data.frame | 560 | 5 |
| love.cotton.uniformity | agridat | Uniformity trial of cotton | data.frame | 170 | 3 |
| love.sugarcane.uniformity | agridat | Uniformity Trial of Sugarcane in Puerto Rico | data.frame | 400 | 3 |
| lu.stability | agridat | Multi-environment trial of maize, to illustrate stability statistics | data.frame | 120 | 4 |
| lucas.switchback | agridat | Switchback experiment on dairy cattle, milk yield for 3 treatments | data.frame | 36 | 5 |
| lyon.potato.uniformity | agridat | Uniformity trial of potatoes | data.frame | 204 | 3 |
| lyons.wheat | agridat | Multi-environment trial of winter wheat at 12 sites in 4 years. | data.frame | 48 | 3 |
| macdonald.cotton.uniformity | agridat | Uniformity trial of cotton in South Africa | data.frame | 1152 | 4 |
| magistad.pineapple.uniformity | agridat | Uniformity trial of pineapple | data.frame | 137 | 6 |
| mamani.bean.uniformity | agridat | Uniformity trial of bean | data.frame | 1680 | 3 |
| masood.rice.uniformity | agridat | Uniformity trial of rice | data.frame | 288 | 3 |
| matusgutierrez.maize.uniformity | agridat | Uniformity trial of maize in Nicaragua | data.frame | 576 | 3 |
| mauricio.maize.uniformity | agridat | Uniformity trial of maize in Ecuador | data.frame | 624 | 3 |
| mcclelland.corn.uniformity | agridat | Uniformity trial of corn | data.frame | 438 | 3 |
| mcconway.turnip | agridat | RCB experiment of turnips | data.frame | 64 | 5 |
| mckinstry.cotton.uniformity | agridat | Uniformity trial of cotton in South Rhodesia | data.frame | 480 | 3 |
| mcleod.barley | agridat | Multi-environment trial of barley in South Canterbury with yield and yield components | data.frame | 40 | 10 |
| mead.cauliflower | agridat | Leaves for cauliflower plants at different times | data.frame | 14 | 3 |
| mead.cowpea.maize | agridat | Intercropping experiment of maize/cowpea | data.frame | 72 | 6 |
| mead.germination | agridat | Seed germination with different temperatures/concentrations | data.frame | 64 | 5 |
| mead.lamb | agridat | Number of lambs born to 3 breeds on 3 farms | data.frame | 36 | 4 |
| mead.strawberry | agridat | RCB experiment of strawberry | data.frame | 32 | 5 |
| mead.turnip | agridat | Density/spacing experiment for turnips in 3 blocks. | data.frame | 60 | 4 |
| mercer.mangold.uniformity | agridat | Uniformity trial of mangolds | data.frame | 200 | 4 |
| mercer.wheat.uniformity | agridat | Uniformity trial of wheat | data.frame | 500 | 4 |
| metzger.multi.uniformity | agridat | Uniformity trials with multiple crops, 9 years on the same land | data.frame | 468 | 4 |
| miguez.biomass | agridat | Biomass of 3 crops in Greece | data.frame | 212 | 5 |
| minnesota.barley.weather | agridat | Monthly weather at 6 sites in Minnesota 1927-1936. | data.frame | 719 | 8 |
| minnesota.barley.yield | agridat | Multi-environment trial of barley in Minnesota at 6 sites in 1927-1936. | data.frame | 2083 | 5 |
| montgomery.wheat.uniformity | agridat | Uniformity trial of wheat, 2 years on the same land | data.frame | 448 | 4 |
| moore.bushbean.uniformity | agridat | Uniformity trials of pole beans, bush beans, sweet corn, carrots, spring and fall cauliflower | data.frame | 576 | 3 |
| moore.carrot.uniformity | agridat | Uniformity trials of pole beans, bush beans, sweet corn, carrots, spring and fall cauliflower | data.frame | 288 | 3 |
| moore.fallcauliflower.uniformity | agridat | Uniformity trials of pole beans, bush beans, sweet corn, carrots, spring and fall cauliflower | data.frame | 240 | 4 |
| moore.polebean.uniformity | agridat | Uniformity trials of pole beans, bush beans, sweet corn, carrots, spring and fall cauliflower | data.frame | 144 | 3 |
| moore.springcauliflower.uniformity | agridat | Uniformity trials of pole beans, bush beans, sweet corn, carrots, spring and fall cauliflower | data.frame | 240 | 4 |
| moore.sweetcorn.uniformity | agridat | Uniformity trials of pole beans, bush beans, sweet corn, carrots, spring and fall cauliflower | data.frame | 288 | 4 |
| morgan.multi.uniformity | agridat | Uniformity trial of wheat and corn | data.frame | 126 | 4 |
| nagai.strawberry.uniformity | agridat | Uniformity trial of strawberry | data.frame | 432 | 3 |
| nair.turmeric.uniformity | agridat | Uniformity trial of turmeric. | data.frame | 864 | 3 |
| narain.sorghum.uniformity | agridat | Uniformity trial of sorghum | data.frame | 160 | 3 |
| nass.barley | agridat | U.S. historical crop yields by state | data.frame | 4839 | 4 |
| nass.corn | agridat | U.S. historical crop yields by state | data.frame | 6381 | 4 |
| nass.cotton | agridat | U.S. historical crop yields by state | data.frame | 2338 | 4 |
| nass.hay | agridat | U.S. historical crop yields by state | data.frame | 5044 | 4 |
| nass.rice | agridat | U.S. historical crop yields by state | data.frame | 662 | 4 |
| nass.sorghum | agridat | U.S. historical crop yields by state | data.frame | 1647 | 4 |
| nass.soybean | agridat | U.S. historical crop yields by state | data.frame | 2528 | 4 |
| nass.wheat | agridat | U.S. historical crop yields by state | data.frame | 5963 | 4 |
| nebraska.farmincome | agridat | Nebraska farm income in 2007 by county | data.frame | 93 | 4 |
| nonnecke.peas.uniformity | agridat | Uniformity trial of canning peas | data.frame | 540 | 5 |
| nonnecke.sweetcorn.uniformity | agridat | Uniformity trial of sweet corn | data.frame | 1728 | 4 |
| obsi.potato.uniformity | agridat | Uniformity trial of potato in Africa 2001 | data.frame | 2569 | 4 |
| odland.soybean.uniformity | agridat | Uniformity trials of soy hay and soybeans | data.frame | 1540 | 3 |
| odland.soyhay.uniformity | agridat | Uniformity trials of soy hay and soybeans | data.frame | 1008 | 3 |
| oliveira.lupin.uniformity | agridat | Uniformity trial of lupin in Portugal | data.frame | 400 | 3 |
| oliveira.potato.uniformity | agridat | Uniformity trial of potato | data.frame | 200 | 3 |
| omer.sorghum | agridat | Multi-environment trial of sorghum, 6 environments | data.frame | 432 | 4 |
| onofri.winterwheat | agridat | Multi-environment trial of winter wheat, 7 years | data.frame | 168 | 5 |
| onyiah.maize | agridat | Graeco-Latin experiment with maize | data.frame | 20 | 5 |
| ortiz.tomato.covs | agridat | Multi-environment trial of tomato in Latin America, weight/yield and environmental covariates | data.frame | 18 | 19 |
| ortiz.tomato.yield | agridat | Multi-environment trial of tomato in Latin America, weight/yield and environmental covariates | data.frame | 270 | 4 |
| pacheco.soybean | agridat | Multi-environment trial of soybean in Brazil. | data.frame | 198 | 3 |
| paez.coffee.uniformity | agridat | Uniformity trial of coffee | data.frame | 4190 | 5 |
| palenciaortiz.sugarcane.uniformity | agridat | Uniformity trial of sugarcane in Guatemala | data.frame | 468 | 3 |
| panse.cotton.uniformity | agridat | Uniformity trial of cotton | data.frame | 1280 | 3 |
| parker.orange.uniformity | agridat | Uniformity trial of oranges | data.frame | 1890 | 4 |
| patterson.switchback | agridat | Switchback experiment on dairy cattle, milk yield for 4 treatments | data.frame | 36 | 4 |
| payne.wheat | agridat | Long term rotation experiment at Rothamsted | data.frame | 480 | 4 |
| pearce.apple | agridat | Apple tree yields for 6 treatments with covariate | data.frame | 24 | 4 |
| pearl.kernels | agridat | Counts of yellow/white and sweet/starchy maize kernels by 15 observers | data.frame | 59 | 6 |
| pederson.lettuce.repeated | agridat | Repeated measurements of lettuce growth | data.frame | 594 | 4 |
| perry.springwheat | agridat | Multi-environment trial of wheat cultivars introduced 1860-1982. | data.frame | 560 | 6 |
| petersen.sorghum.cowpea | agridat | Intercropping experiment of sorghum/cowpea | data.frame | 18 | 5 |
| petersen.wheat | agridat | Augmented design of durum wheat | data.frame | 48 | 4 |
| piepho.barley.uniformity | agridat | Uniformity trial of barley | data.frame | 1080 | 3 |
| piepho.cocksfoot | agridat | Multi-environment trial of cock's foot, heading dates for 25 varieties in 7 years | data.frame | 111 | 3 |
| polson.safflower.uniformity | agridat | Uniformity trial of safflower | data.frame | 1716 | 3 |
| rangaswamy.groundnut.uniformity | agridat | Uniformity trial of groundnut | data.frame | 96 | 3 |
| ratkowsky.onions | agridat | Onion yields for different densities at two locations | data.frame | 84 | 3 |
| rau.coffee.uniformity | agridat | Uniformity trial of coffee in India | data.frame | 144 | 3 |
| reid.grasses | agridat | Yields of four grasses for a wide range of nitrogen fertilizer | data.frame | 210 | 5 |
| riddle.wheat | agridat | Modified Latin Square experiments of wheat | data.frame | 650 | 7 |
| ridout.appleshoots | agridat | Root counts for propagated columnar apple shoots. | data.frame | 270 | 4 |
| robinson.peanut.uniformity | agridat | Uniformity trial of peanuts | data.frame | 1152 | 4 |
| roemer.sugarbeet.uniformity | agridat | Uniformity trial of sugar beets | data.frame | 192 | 4 |
| rothamsted.brussels | agridat | RCB experiment of brussels sprouts, 9 fertilizer treatments | data.frame | 48 | 5 |
| rothamsted.oats | agridat | RCB experiment of oats, straw and grain, 9 fertilizer treatments | data.frame | 96 | 6 |
| ryder.groundnut | agridat | RCB experiment of groundut, wet and dry yields | data.frame | 24 | 6 |
| salmon.bunt | agridat | Fungus infection in varieties of wheat | data.frame | 400 | 4 |
| saunders.maize.uniformity | agridat | Uniformity trial of maize in South Africa | data.frame | 2500 | 4 |
| sawyer.multi.uniformity | agridat | Uniformity trials of wheat, swedes, oats, 3 years on the same land | data.frame | 329 | 5 |
| sayer.sugarcane.uniformity | agridat | Uniformity trial of sugarcane in India, 1932, 1933 & 1934. | data.frame | 2056 | 4 |
| scott.augmented | agridat | Augmented RCB experiment using soybean | data.frame | 30 | 4 |
| senshu.rice | agridat | Multi-environment trial of rice, with solar radiation and temperature | data.frame | 40 | 7 |
| shafi.tomato.uniformity | agridat | Uniformity trial of tomato | data.frame | 200 | 3 |
| shafii.rapeseed | agridat | Multi-environment trial of rapeseed in U.S. | data.frame | 648 | 5 |
| sharma.met | agridat | Multi-environment trial | data.frame | 126 | 5 |
| shaw.oats | agridat | Multi-environment trial of oats in India | data.frame | 390 | 5 |
| siao.cotton.uniformity | agridat | Uniformity trials of cotton in China | data.frame | 858 | 4 |
| silva.cotton | agridat | Number of cotton bolls for different levels of defoliation. | data.frame | 250 | 9 |
| sinclair.clover | agridat | Clover yields in a factorial fertilizer experiment | data.frame | 25 | 3 |
| smith.beans.uniformity | agridat | Uniformity trials of beans, 2 species in 2 years | data.frame | 912 | 4 |
| smith.corn.uniformity | agridat | Uniformity trial of corn, 3 years on same ground | data.frame | 360 | 5 |
| smith.wheat.uniformity | agridat | Uniformity trial of wheat | data.frame | 1080 | 4 |
| snedecor.asparagus | agridat | Asparagus yields for different cutting treatments | data.frame | 80 | 4 |
| snijders.fusarium | agridat | Fusarium infection in wheat varieties | data.frame | 204 | 4 |
| stephens.sorghum.uniformity | agridat | Uniformity trial of sorghum silage | data.frame | 2000 | 3 |
| steptoe.morex.geno | agridat | Multi-environment trial of barley, phenotypic and genotypic data for a population of Steptoe x Morex | dh | | |
| steptoe.morex.pheno | agridat | Multi-environment trial of barley, phenotypic and genotypic data for a population of Steptoe x Morex | data.frame | 2432 | 10 |
| stickler.sorghum.uniformity | agridat | Uniformity trial of sorghum | data.frame | 1600 | 4 |
| stirret.borers | agridat | Corn borer control by application of fungal spores. | data.frame | 60 | 4 |
| streibig.competition | agridat | Competition experiment between barley and sinapis. | data.frame | 135 | 8 |
| strickland.apple.uniformity | agridat | Uniformity trial in apple | data.frame | 198 | 3 |
| strickland.grape.uniformity | agridat | Uniformity trial of grape | data.frame | 155 | 3 |
| strickland.peach.uniformity | agridat | Uniformity trial of peach | data.frame | 144 | 3 |
| strickland.tomato.uniformity | agridat | Uniformity trial of tomato | data.frame | 180 | 3 |
| stroup.nin | agridat | RCB experiment of wheat at the Nebraska Intrastate Nursery | data.frame | 242 | 5 |
| stroup.splitplot | agridat | Split-plot experiment of simulated data | data.frame | 24 | 4 |
| student.barley | agridat | Multi-environment trial of barley | data.frame | 102 | 7 |
| summerby.multi.uniformity | agridat | Uniformity trial of maize, oat, alfalfa, mangolds | data.frame | 2600 | 6 |
| tai.potato | agridat | Multi-environment trial of potato | data.frame | 48 | 6 |
| talbot.potato.traits | agridat | Multi-environment trial of potato in UK, yields and trait scores at 12 locations | data.frame | 126 | 3 |
| talbot.potato.yield | agridat | Multi-environment trial of potato in UK, yields and trait scores at 12 locations | data.frame | 108 | 3 |
| tesfaye.millet | agridat | Multi-environment trial of millet | data.frame | 415 | 9 |
| theobald.barley | agridat | Multi-environment trial of barley, multiple years & fertilizer levels | data.frame | 105 | 5 |
| theobald.covariate | agridat | Multi-environment trial of corn silage, Year * Loc * Variety with covariate | data.frame | 256 | 5 |
| thompson.cornsoy | agridat | Multi-environment trial of corn & soybean, 1930-1962, with temperature and precipitation | data.frame | 165 | 12 |
| trought.cotton.uniformity | agridat | Uniformity trial of cotton in Sudan | data.frame | 192 | 3 |
| tulaikow.wheat.uniformity | agridat | Uniformity trial of winter/spring wheat | data.frame | 480 | 4 |
| turner.herbicide | agridat | Herbicide control of larkspur | data.frame | 12 | 4 |
| urquhart.feedlot | agridat | Weight gain calves in a feedlot | data.frame | 67 | 5 |
| usda.gmoadoption | agridat | Adoption of genetically modified crops in the United States | data.frame | 145 | 3 |
| usgs.herbicides | agridat | Concentrations of herbicides in streams in the United States | data.frame | 184 | 19 |
| vaneeuwijk.drymatter | agridat | Multi-environment trial of maize, dry matter content | data.frame | 168 | 4 |
| vaneeuwijk.fusarium | agridat | Infection of wheat varieties by Fusarium strains from 1990 to 1993 | data.frame | 560 | 4 |
| vaneeuwijk.nematodes | agridat | Number of cysts on 11 potato genotypes for 5 potato cyst nematode populations. | data.frame | 55 | 3 |
| vargas.txe.covs | agridat | Treatment x environment interaction in agronomy trials | data.frame | 10 | 28 |
| vargas.txe.yield | agridat | Treatment x environment interaction in agronomy trials | data.frame | 240 | 3 |
| vargas.wheat1.covs | agridat | Wheat yields in 7 years with genetic and environment covariates | data.frame | 6 | 17 |
| vargas.wheat1.traits | agridat | Wheat yields in 7 years with genetic and environment covariates | data.frame | 126 | 19 |
| vargas.wheat2.covs | agridat | Multi-environment trial of wheat with environmental covariates | data.frame | 21 | 14 |
| vargas.wheat2.yield | agridat | Multi-environment trial of wheat with environmental covariates | data.frame | 168 | 3 |
| verbyla.lupin | agridat | Multi-environment trial of lupin, multiple varieties and densities | data.frame | 1420 | 13 |
| versteeg.maize | agridat | Maize and pigeon pea criss-cross trial in Benin | data.frame | 150 | 4 |
| vishnaadevi.rice.uniformity | agridat | Uniformity trial of rice | data.frame | 288 | 3 |
| vold.longterm | agridat | Long-term barley yields at different fertilizer levels | data.frame | 76 | 3 |
| vsn.lupin3 | agridat | Multi-environment trial of lupin, early generation trial | data.frame | 1236 | 5 |
| wallace.iowaland | agridat | Iowa farmland values by county in 1925 | data.frame | 99 | 10 |
| walsh.cottonprice | agridat | Acres and price of cotton 1910-1943 | data.frame | 34 | 9 |
| wassom.brome.uniformity | agridat | Uniformity trials of bromegrass | data.frame | 3888 | 4 |
| waynick.soil | agridat | Soil nitrogen and carbon in two fields | data.frame | 200 | 6 |
| wedderburn.barley | agridat | Multi-environment trial of barley, percent of leaves affected by leaf blotch | data.frame | 90 | 3 |
| wehner.cucumber.uniformity | agridat | Uniformity trial of cucumber in North Carolina | data.frame | 150 | 3 |
| weiss.incblock | agridat | Soybean balanced incomplete block experiment | data.frame | 186 | 5 |
| weiss.lattice | agridat | Lattice experiment in soybeans. | data.frame | 196 | 5 |
| welch.bermudagrass | agridat | Factorial experiment of bermuda grass, 4x4x4, N, P, K fertilizers | data.frame | 64 | 4 |
| wheatley.carrot | agridat | Insecticide treatments for carrot fly larvae | data.frame | 36 | 6 |
| wiebe.wheat.uniformity | agridat | Uniformity trial of wheat | data.frame | 1500 | 3 |
| wiedemann.safflower.uniformity | agridat | Uniformity trial of safflower | data.frame | 1782 | 3 |
| williams.barley.uniformity | agridat | Uniformity trial of barley | data.frame | 720 | 3 |
| williams.cotton.uniformity | agridat | Uniformity trial of cotton | data.frame | 288 | 3 |
| williams.trees | agridat | Multi-environment trial of trees, height / survival of 37 species at 6 sites in Thailand | data.frame | 192 | 4 |
| woodman.pig | agridat | Weight gain in pigs for different treatments | data.frame | 30 | 10 |
| wyatt.multi.uniformity | agridat | Uniformity trial of oats and wheat on the same ground. | data.frame | 258 | 5 |
| yan.winterwheat | agridat | Multi-environment trial of winter wheat in Ontario | data.frame | 162 | 3 |
| yang.barley | agridat | Multi-environment trial of barley in Alberta, 6 varieties at 18 locations in Alberta. | data.frame | 108 | 3 |
| yates.missing | agridat | Factorial experiment of potato, 3x3 with missing values | data.frame | 80 | 6 |
| yates.oats | agridat | Split-plot experiment of oats | data.frame | 72 | 8 |
| zuidhof.broiler | agridat | Daily weight, feed, egg measurements for a broiler chicken | data.frame | 59 | 6 |
| couple | COMPoissonReg | Couple dataset | data.frame | 387 | 3 |
| freight | COMPoissonReg | Freight dataset | data.frame | 10 | 2 |
| hg19.GoNLsnps | omicsPrint | Dataframe with overlaps GoNL variants and 450K probes | DFrame | | |
| hm450.manifest.pop.GoNL | omicsPrint | HM450 population-specific probe-masking recommendations | GRanges | | |
| toydata_relapses | msprog | Synthetic Relapse Data | tbl_df | 5 | 3 |
| toydata_visits | msprog | Synthetic Longitudinal EDSS and SDMT Data | tbl_df | 57 | 5 |
| abstract.table | labeleR | abstract.table | data.frame | 8 | 4 |
| attendance.table | labeleR | attendance.table | data.frame | 4 | 1 |
| badges.table | labeleR | badges.table | data.frame | 8 | 2 |
| collection.table | labeleR | collection.table | data.frame | 40 | 8 |
| herbarium.table | labeleR | herbarium.table | data.frame | 6 | 19 |
| multichoice.table | labeleR | multichoice.table | data.frame | 7 | 5 |
| participation.table | labeleR | participation.table | data.frame | 4 | 5 |
| tiny.table | labeleR | tiny.table | data.frame | 40 | 8 |
| asr | omicRexposome | 'ResultSet' for testing and illustration purpouses | ResultSet | | |
| crs | omicRexposome | 'ResultSet' for testing and illustration purpouses | ResultSet | | |
| mds | omicRexposome | 'MultiDataSet' for testing and illustration purpouses | MultiDataSet | | |
| Tropheus | vcvComp | Tropheus dataset | data.frame | 723 | 57 |
| Tropheus.IK.coord | vcvComp | Tropheus IK coord dataset | data.frame | 511 | 58 |
| london | osmplotr | london | list | | |
| cog_data | lwc2022 | Cognition Data | data.frame | 10 | 35 |
| cog_data_score | lwc2022 | Scored Cognition Data | data.frame | 10 | 6 |
| ivySE | ivygapSE | ivySE: SummarizedExperiment for IvyGAP expression data and metadata | SummarizedExperiment | | |
| AIMSmodel | BreastSubtypeR | AIMSmodel: Model object for AIMS | list | | |
| BreastSubtypeRobj | BreastSubtypeR | BreastSubtypeRobj: Resources for NC-based methods | list | | |
| Gene.ID.ann | BreastSubtypeR | Gene.ID.ann: Gene annotation table | data.frame | 19675 | 6 |
| OSLO2EMIT0obj | BreastSubtypeR | OSLO2EMIT0obj: Example dataset (OSLO2-EMIT0 cohort subset) | list | | |
| sspbc.models | BreastSubtypeR | sspbc.models: Short names for 11 SSPBC predictors | list | | |
| sspbc.models.fullname | BreastSubtypeR | sspbc.models.fullname: Full names for 11 SSPBC predictors | list | | |
| TCGABRCAobj | BreastSubtypeR | TCGABRCAobj: Example dataset (TCGA-BRCA subset) | list | | |
| metric | mapi | 'metric' test dataset | data.table | 19900 | 3 |
| samples | mapi | 'samples' test dataset | data.table | 200 | 4 |
| . | weyl | Class "dot" | dot | | |
| d | weyl | Generating elements for the first Weyl algebra | weyl | | |
| x | weyl | Generating elements for the first Weyl algebra | weyl | | |
| data_land | PrInDT | Landscape analysis | data.frame | 149 | 28 |
| data_speaker | PrInDT | Subject pronouns and a predictor with one very frequent level | data.frame | 3370 | 6 |
| data_vowel | PrInDT | Vowel length | data.frame | 82 | 22 |
| data_zero | PrInDT | Subject pronouns | data.frame | 1024 | 7 |
| participant_zero | PrInDT | Participants of subject pronoun study | factor | | |
| mfd_single | flocker | Data for example single-season flocker model | list | | |
| ParamCubic | CluMP | Parameters of cubic model | data.frame | 2 | 8 |
| ParamExpon | CluMP | Parameters of exponential model | data.frame | 2 | 7 |
| ParamLinear | CluMP | Parameters of linear model | data.frame | 2 | 6 |
| ParamQuadrat | CluMP | Parameters of quadratic model | data.frame | 2 | 7 |
| cr_outline | paisaje | Costa Rica Full Outline (Continental + Islands) | sf | 1 | 1 |
| cr_outline_c | paisaje | Costa Rica Continental Outline | sf | 1 | 1 |
| actor_genres | cooccure | IMDB actor-genre long table (1970-2024) | data.frame | 2502 | 2 |
| actors | cooccure | IMDB actor-movie long table (1970-2024) | data.frame | 1267 | 7 |
| demo | cooccure | Demo actor-movie-genre table | data.frame | 89 | 3 |
| movies | cooccure | IMDB movie metadata (1970-2024) | data.frame | 1000 | 7 |
| wrds_products | wrds | WRDS product catalog | tbl_df | 613 | 2 |
| ames_grid_search | tune | Example Analysis of Ames Housing Data | tune_results | 10 | 4 |
| ames_iter_search | tune | Example Analysis of Ames Housing Data | iteration_results | 110 | 5 |
| ames_wflow | tune | Example Analysis of Ames Housing Data | workflow | | |
| NCDS | PSweight | Illustrative dataset for PSweight | data.frame | 3642 | 16 |
| psdata | PSweight | Simulated dataset for PSweight | data.frame | 1500 | 8 |
| psdata_cl | PSweight | Simulated dataset for PSweight | data.frame | 1500 | 9 |
| midsch | eeptools | A dataframe of aggregate test scores for schools in a Midwest state. | data.frame | 19985 | 16 |
| stuatt | eeptools | Student Attributes from the Strategic Data Project Toolkit | data.frame | 87534 | 9 |
| stulevel | eeptools | A synthetic data set of K-12 student attributes. | data.frame | 2700 | 32 |
| pronghornAreas | Rdistance | Pronghorn Line Transect Data | data.frame | 4 | 2 |
| pronghornDf | Rdistance | Pronghorn Line Transect Data | rowwise_df | 300 | 6 |
| sparrowDetectionData | Rdistance | Brewer's Sparrow detection data | data.frame | 356 | 5 |
| sparrowDf | Rdistance | Brewer's Sparrow detection data frame in Rdistance >4.0.0 format. | rowwise_df | 72 | 9 |
| sparrowDfuncObserver | Rdistance | Brewer's Sparrow detection function | dfunc | | |
| sparrowSiteData | Rdistance | Brewer's Sparrow site data | data.frame | 72 | 8 |
| thrasherDetectionData | Rdistance | Sage Thrasher detection data | data.frame | 193 | 3 |
| thrasherDf | Rdistance | Sage Thrasher detection data frame in Rdistance >4.0.0 format | rowwise_df | 120 | 8 |
| thrasherSiteData | Rdistance | Sage Thrasher site data. | data.frame | 120 | 7 |
| drift_data_JKI | pfm | Deposition from spray drift expressed as percent of the applied dose as published by the JKI | list | | |
| drift_parameters_focus | pfm | Regression parameters for the Rautmann drift data | tbl_df | 49 | 8 |
| EFSA_GW_interception_2014 | pfm | Subset of EFSA crop interception default values for groundwater modelling | matrix | 7 | 10 |
| EFSA_washoff_2017 | pfm | Subset of EFSA crop washoff default values | matrix | 7 | 10 |
| FOCUS_GW_scenarios_2012 | pfm | A very small subset of the FOCUS Groundwater scenario definitions | list | | |
| FOCUS_Step_12_scenarios | pfm | Step 1/2 scenario data as distributed with the FOCUS Step 1/2 calculator | list | | |
| perc_runoff_exposit | pfm | Runoff loss percentages as used in Exposit 3 | data.frame | 12 | 3 |
| perc_runoff_reduction_exposit | pfm | Runoff reduction percentages as used in Exposit | list | | |
| soil_scenario_data_EFSA_2015 | pfm | Properties of the predefined scenarios from the EFSA guidance from 2015 | data.frame | 6 | 10 |
| soil_scenario_data_EFSA_2017 | pfm | Properties of the predefined scenarios from the EFSA guidance from 2017 | data.frame | 6 | 12 |
| D24_2014 | mkin | Aerobic soil degradation data on 2,4-D from the EU assessment in 2014 | mkindsg | | |
| dimethenamid_2018 | mkin | Aerobic soil degradation data on dimethenamid and dimethenamid-P from the EU assessment in 2018 | mkindsg | | |
| ds_dfop | mkin | Synthetic data for hierarchical kinetic degradation models | list | | |
| ds_dfop_sfo | mkin | Synthetic data for hierarchical kinetic degradation models | list | | |
| ds_fomc | mkin | Synthetic data for hierarchical kinetic degradation models | list | | |
| ds_hs | mkin | Synthetic data for hierarchical kinetic degradation models | list | | |
| ds_sfo | mkin | Synthetic data for hierarchical kinetic degradation models | list | | |
| experimental_data_for_UBA_2019 | mkin | Experimental datasets used for development and testing of error models | list | | |
| FOCUS_2006_A | mkin | Datasets A to F from the FOCUS Kinetics report from 2006 | data.frame | 8 | 3 |
| FOCUS_2006_B | mkin | Datasets A to F from the FOCUS Kinetics report from 2006 | data.frame | 8 | 3 |
| FOCUS_2006_C | mkin | Datasets A to F from the FOCUS Kinetics report from 2006 | data.frame | 9 | 3 |
| FOCUS_2006_D | mkin | Datasets A to F from the FOCUS Kinetics report from 2006 | data.frame | 44 | 3 |
| FOCUS_2006_DFOP_ref_A_to_B | mkin | Results of fitting the DFOP model to Datasets A to B of FOCUS (2006) | data.frame | 15 | 8 |
| FOCUS_2006_E | mkin | Datasets A to F from the FOCUS Kinetics report from 2006 | data.frame | 18 | 3 |
| FOCUS_2006_F | mkin | Datasets A to F from the FOCUS Kinetics report from 2006 | data.frame | 27 | 3 |
| FOCUS_2006_FOMC_ref_A_to_F | mkin | Results of fitting the FOMC model to Datasets A to F of FOCUS (2006) | data.frame | 45 | 7 |
| FOCUS_2006_HS_ref_A_to_F | mkin | Results of fitting the HS model to Datasets A to F of FOCUS (2006) | data.frame | 36 | 8 |
| FOCUS_2006_SFO_ref_A_to_F | mkin | Results of fitting the SFO model to Datasets A to F of FOCUS (2006) | data.frame | 50 | 6 |
| focus_soil_moisture | mkin | FOCUS default values for soil moisture contents at field capacity, MWHC and 1/3 bar | matrix | 12 | 3 |
| mccall81_245T | mkin | Datasets on aerobic soil metabolism of 2,4,5-T in six soils | data.frame | 141 | 4 |
| NAFTA_SOP_Appendix_B | mkin | Example datasets from the NAFTA SOP published 2015 | data.frame | 14 | 3 |
| NAFTA_SOP_Appendix_D | mkin | Example datasets from the NAFTA SOP published 2015 | data.frame | 18 | 3 |
| NAFTA_SOP_Attachment | mkin | Example datasets from Attachment 1 to the NAFTA SOP published 2015 | list | | |
| schaefer07_complex_case | mkin | Metabolism data set used for checking the software quality of KinGUI | data.frame | 8 | 6 |
| schaefer07_complex_results | mkin | Metabolism data set used for checking the software quality of KinGUI | data.frame | 14 | 5 |
| synthetic_data_for_UBA_2014 | mkin | Synthetic datasets for one parent compound with two metabolites | list | | |
| test_data_from_UBA_2014 | mkin | Three experimental datasets from two water sediment systems and one soil | list | | |
| parabolic | discrim | Parabolic class boundary data | tbl_df | 500 | 3 |
| annotation_data_sources | nichenetr | Annotation table of all data sources used in the NicheNet model | data.frame | 57 | 5 |
| expression_settings_validation | nichenetr | Expression datasets for validation | list | | |
| geneinfo_2022 | nichenetr | Gene annotation information: version 2 - january 2022 | tbl_df | 122797 | 4 |
| geneinfo_alias_human | nichenetr | Gene annotation information: version 2 - january 2022 - suited for alias conversion | spec_tbl_df | 132328 | 3 |
| geneinfo_alias_mouse | nichenetr | Gene annotation information: version 2 - january 2022 - suited for alias conversion | spec_tbl_df | 167942 | 3 |
| geneinfo_human | nichenetr | Gene annotation information | tbl_df | 18985 | 4 |
| hyperparameter_list | nichenetr | Optimized hyperparameter values | tbl_df | 4 | 3 |
| ncitations | nichenetr | Number of citations for genes | tbl_df | 37660 | 5 |
| optimized_source_weights_df | nichenetr | Optimized data source weights | tbl_df | 57 | 3 |
| source_weights_df | nichenetr | Data source weights | tbl_df | 57 | 2 |
| LPdata | zCompositions | La Paloma data set | data.frame | 96 | 15 |
| LPdataZM | zCompositions | La Paloma data set (incl. zeros and missing data) | data.frame | 96 | 15 |
| mdl | zCompositions | Water data set: matrix of limits of detection | matrix | 100 | |
| Pigs | zCompositions | Pigs data set | data.frame | 29 | 6 |
| Water | zCompositions | Water data set | data.frame | 100 | 4 |
| analytics | DIVINE | DIVINE's table on laboratory data | tbl_df | 5813 | 9 |
| comorbidities | DIVINE | DIVINE's table on information about comorbidities | tbl_df | 5813 | 37 |
| complications | DIVINE | DIVINE's table on complications data | tbl_df | 5813 | 9 |
| concomitant_medication | DIVINE | DIVINE's table on treatments previous to hospital admission | tbl_df | 5813 | 11 |
| demographic | DIVINE | DIVINE's demographic table | tbl_df | 5813 | 8 |
| end_followup | DIVINE | DIVINE's table on closure data | tbl_df | 5813 | 8 |
| icu | DIVINE | DIVINE's table on icu data | tbl_df | 5813 | 14 |
| inhosp_antibiotics | DIVINE | DIVINE's table on antibiotics received during hospitalization | tbl_df | 5813 | 17 |
| inhosp_antivirals | DIVINE | DIVINE's table on antivirals received during hospitalization | tbl_df | 5813 | 10 |
| inhosp_other_treatments | DIVINE | DIVINE's table on other treatments received during hospitalization. | tbl_df | 5813 | 6 |
| scores | DIVINE | DIVINE's table on severity scores at hospital admission | tbl_df | 5813 | 10 |
| symptoms | DIVINE | DIVINE's symptoms table | tbl_df | 5813 | 24 |
| vaccine | DIVINE | DIVINE's vaccine table | tbl_df | 5813 | 6 |
| vital_signs | DIVINE | DIVINE's table on vital signs | tbl_df | 5813 | 13 |
| intcal | rintcal | IntCal20 json file | list | | |
| cohort_a_ranking | spqrp | Protein-importance ranking for plasma cohort "A" | tbl_df | 729 | 2 |
| dominant1 | paramlink2 | Dominant linkage analysis example | list | | |
| durcomp.dispensing | AdhereR | Example dispensing events for 16 patients. | data.table | 1794 | 6 |
| durcomp.hospitalisation | AdhereR | Example special periods for 10 patients. | data.table | 28 | 3 |
| durcomp.prescribing | AdhereR | Example prescription events for 16 patients. | data.table | 1502 | 8 |
| med.events | AdhereR | Example medication events records for 100 patients. | data.frame | 1080 | 5 |
| med.events.ATC | AdhereR | | data.frame | 1564 | 7 |
| med.groups | AdhereR | | character | | |
| safo | flowchart | Random generated dataset from the SAFO study | tbl_df | 925 | 21 |
| french_cities | blvim | French cities | data.frame | 121 | 10 |
| french_cities_distances | blvim | French cities distances | matrix | 121 | 121 |
| french_cities_times | blvim | French cities distances | matrix | 121 | 121 |
| french_departments | blvim | French departments | data.frame | 101 | 3 |
| french_regions | blvim | French regions | data.frame | 18 | 2 |
| ex_table3 | mvalpha | Published Examples | data.frame | 4 | 3 |
| ex_table8a | mvalpha | Published Examples | data.frame | 3 | 3 |
| ex_table8b | mvalpha | Published Examples | data.frame | 3 | 3 |
| ex_table8c | mvalpha | Published Examples | data.frame | 3 | 3 |
| ex_table9a | mvalpha | Published Examples | data.frame | 3 | 3 |
| ex_table9b | mvalpha | Published Examples | data.frame | 3 | 3 |
| ex_table9c | mvalpha | Published Examples | data.frame | 3 | 3 |
| aw | predmicror | Data of aw | data.frame | 9 | 3 |
| bixina | predmicror | Data concerning _Staphylococcus aureus_ microbial inactivation in beef | spec_tbl_df | 18 | 3 |
| growthfull | predmicror | Data of a complete curve of microbial growth | data.frame | 13 | 3 |
| growthnolag | predmicror | Data of a no lag curve of microbial growth | data.frame | 10 | 3 |
| growthred | predmicror | Data of a reduced curve of microbial growth | data.frame | 9 | 3 |
| inh | predmicror | Data of INH antimicrobials | data.frame | 8 | 3 |
| mafart2005Li11 | predmicror | Data of microbial inactivation Albert and Mafart (2005) | data.frame | 10 | 2 |
| ph | predmicror | Data pH | data.frame | 14 | 3 |
| salmonella | predmicror | Potential growth of _Salmonella typhimurium_ on cooked chicken | data.frame | 21 | 3 |
| gtex_expr | GWENA | Transcriptomic muscle data from GTEx consorsium RNA-seq data | data.frame | 50 | 15000 |
| gtex_traits | GWENA | Traits data linked to samples in transcriptomic data from GTEx | data.frame | 50 | 4 |
| kuehne_expr | GWENA | Transcriptomic data from the Kuehne et al. publication | data.frame | 48 | 15801 |
| kuehne_traits | GWENA | Traits data linked to samples in transcriptomic data from the Kuehne et al. publication | data.frame | 48 | 5 |
| benthos | dimensio | Benthos | data.frame | 92 | 13 |
| colours | dimensio | Colours | data.frame | 4 | 4 |
| countries | dimensio | Countries | data.frame | 13 | 6 |
| calibration | LightFitR | calibration data | data.frame | 373392 | 6 |
| example_closest | LightFitR | closest intensities | matrix | 9 | |
| example_intensities | LightFitR | example intensities | matrix | 9 | |
| example_regime | LightFitR | regime matrix | matrix | 13 | 10 |
| helio.dyna.leds | LightFitR | heliospectra DYNA LEDs | data.frame | 9 | 3 |
| helio.eventLimit | LightFitR | Maximum number of events | numeric | | |
| target_irradiance | LightFitR | target irradiances | matrix | 9 | |
| time_vector | LightFitR | time vector | POSIXct | | |
| algae.rd | respR | Oxygen production respirometry data | data.table | 1200 | 2 |
| background_con.rd | respR | Background respirometry data (constant) | data.table | 20664 | 2 |
| background_exp.rd | respR | Background respirometry data (exponential) | data.table | 20664 | 2 |
| background_lin.rd | respR | Background respirometry data (linear) | data.table | 20664 | 2 |
| flowthrough_mult.rd | respR | Multi-column flowthrough respirometry data | data.table | 3740 | 15 |
| flowthrough_sim.rd | respR | Flowthrough respirometry data with increasing background rate | data.table | 3740 | 4 |
| flowthrough.rd | respR | Flowthrough respirometry data on the chiton, _Mopalia lignosa_ | data.table | 935 | 4 |
| intermittent.rd | respR | Respirometry data of the sea urchin, _Heliocidaris Erythrogramma_ | data.table | 4831 | 2 |
| sardine.rd | respR | Respirometry data of the sardine, _Sardinops sagax_ | data.table | 7513 | 3 |
| squid.rd | respR | Respirometry data of the squid, _Doryteuthis opalescens_ | data.table | 34120 | 2 |
| urchins.rd | respR | Multi-column respirometry data of the sea urchin, _Heliocidaris Erythrogramma_, including background respiration | data.table | 271 | 19 |
| zeb_intermittent.rd | respR | Respirometry data of a zebrafish, _Danio rerio_ | data.table | 79251 | 2 |
| calculateMetricsExample | neuroSCC | Precomputed Inputs for SCC vs. SPM Performance Evaluation | list | | |
| generatePoissonClonesExample | neuroSCC | Example Input for Poisson Clone Generation | matrix | 1 | |
| SCCcomp | neuroSCC | Example SCC Computation Result | image | | |
| sccOneGroup | neuroSCC | Example SCC object for one-group analysis | image | | |
| Banks | gofCopula | Volatility-adjusted log returns of the two Banks Citigroup and Bank of America. | list | | |
| CryptoCurrencies | gofCopula | Volatility-adjusted log returns of the two Cryptocurrencies Bitcoin and Litecoin. | list | | |
| IndexReturns2D | gofCopula | Log returns of european stock indices | matrix | 100 | 2 |
| IndexReturns3D | gofCopula | Log returns of european stock indices | matrix | 200 | 3 |
| Fst_dat | PopGenHelpR | A genetic differentiation matrix and locality information for each population. This data was generated by subsetting data of Farleigh et al., 2021. | list | | |
| Het_dat | PopGenHelpR | A data frame of hypothetical heterozygosity data produced by Heterozygosity. | data.frame | 3 | 5 |
| HornedLizard_Pop | PopGenHelpR | A population assignment data frame to be used in 'Heterozygosity' and 'Differentiation'. | data.frame | 72 | 4 |
| HornedLizard_VCF | PopGenHelpR | A vcfR object to be used in 'Heterozygosity' and 'Differentiation'. | vcfR | | |
| Q_dat | PopGenHelpR | A list representing a q-matrix and the locality information associated with the qmatrix | list | | |
| CanadaMite | RSE | mite incidence in moss patches of 32 locations of western Canada (Chen et al. 2015) | matrix | 412 | 2 |
| HerpetologicalData | RSE | Abundance of herpetofauna in the conserved and human disturbed areas of Mexico (Suazo-Ortuno et al. 2008) | data.frame | 62 | 2 |
| mlb2025 | ropper | Baseball Winning Percentages in 2025 | data.frame | 29 | 8 |
| data.check | SoyNAM | Datasets | data.frame | 9043 | 18 |
| data.check.in | SoyNAM | Datasets | data.frame | 1650 | 21 |
| data.check.qa | SoyNAM | Datasets | data.frame | 10721 | 11 |
| data.line | SoyNAM | Datasets | data.frame | 60744 | 18 |
| data.line.in | SoyNAM | Datasets | data.frame | 12790 | 21 |
| data.line.qa | SoyNAM | Datasets | data.frame | 62572 | 11 |
| gen.in | SoyNAM | Datasets | matrix | 5555 | 4240 |
| gen.qa | SoyNAM | Datasets | matrix | 5180 | 4312 |
| gen.raw | SoyNAM | Datasets | matrix | 5590 | 4611 |
| test_smet | smetlite | Test SMET data | smet | 47 | 5 |
| AcousticWeightingTable | OpeNoise | Weighting acoustic table | data.frame | 32 | 5 |
| dataset_impulsive1 | OpeNoise | Noise dataset of impulsive event (100 ms acquisition time) | data.frame | 3299 | 78 |
| dataset_impulsive2 | OpeNoise | Noise dataset of impulsive event (100 ms acquisition time) | data.frame | 3008 | 78 |
| dfBW | OpeNoise | Table's 1/3 octave bandwidth | data.frame | 36 | 6 |
| exampleHourlyData | OpeNoise | Noise hourly data of misure in environmental open space | data.frame | 1920 | 5 |
| iso | OpeNoise | Parameters table of equal loudness curve A (ISO 226:1987 “Acoustics - Normal equal-loudness-level contours”) | data.frame | 36 | 5 |
| markers | OpeNoise | Dataset with markers | data.frame | 14 | 5 |
| P1FA | OpeNoise | Noise data of misure in house open window condition | data.frame | 1626 | 44 |
| P1FC | OpeNoise | Noise data of misure in house close window condition | data.frame | 2027 | 44 |
| PTFA | OpeNoise | Noise data of misure in house open window condition | data.frame | 1652 | 44 |
| PTFC | OpeNoise | Noise data of misure in house close window condition | data.frame | 912 | 44 |
| anolis.ssd | ouch | Greater Antillean anolis lizard sexual size dimorphism data | data.frame | 38 | 7 |
| bimac | ouch | Anolis bimaculatus lizard size data | data.frame | 45 | 11 |
| geospiza | ouch | Data on Darwin's finches | list | | |
| tnrs_testfile | TNRS | 100 scientific names. | data.frame | 100 | 2 |
| sample_data | HIViz | Sample HIV Epidemiology Dataset | tbl_df | 1040 | 11 |
| cpsmarch2015 | strat | A Subset of March CPS 2015 Sample | data.frame | 14358 | 5 |
| fieldbook | huito | Fieldbook experimental design | data.frame | 24 | 5 |
| crashDat | elrm | Crash Dataset: Calibration of Crash Dummies in Automobile Safety Tests | data.frame | 58 | 5 |
| diabDat | elrm | Simulated Diabetes Dataset | data.frame | 229 | 7 |
| drugDat | elrm | Drug Dataset | data.frame | 4 | 4 |
| titanDat | elrm | Titanic Dataset | data.frame | 14 | 5 |
| utiDat | elrm | Urinary Tract Infection and Contraceptive Use | data.frame | 55 | 11 |
| Tourism | wINEQ | Sample survey on trips | tbl_df | 5319 | 17 |
| Well_being | wINEQ | Sample survey on quality of life | tbl_df | 1197 | 27 |
| admin | nonprobsvy | Admin Data (Non-Probability Survey) | data.frame | 9344 | 6 |
| jvs | nonprobsvy | Job Vacancy Survey | data.frame | 6523 | 6 |
| freMTPLmini | IBLM | French Motor Insurance Claims Dataset | tbl_df | 25000 | 8 |
| dCox_sim | bigPLScox | Simulated survival dataset for Cox models | data.frame | 10000 | 5 |
| micro.censure | bigPLScox | Microsat features and survival times | data.frame | 117 | 43 |
| sim_data | bigPLScox | Simulated dataset | data.frame | 1000 | 11 |
| Xmicro.censure_compl_imp | bigPLScox | Imputed Microsat features | data.frame | 117 | 40 |
| atf3_chr1 | COCOA | Atf3 binding regions. | GRanges | | |
| brcaATACCoord1 | COCOA | A GRanges object with coordinates for select BRCA ATAC-seq peak regions from chr1. | GRanges | | |
| brcaATACData1 | COCOA | A matrix with ATAC-seq counts in select peak regions from chromosome 1 for 37 patients. | matrix | 4053 | 37 |
| brcaMCoord1 | COCOA | A GRanges object with genomic coordinates for cytosines from chr1 for the package's built-in DNA methylation data | GRanges | | |
| brcaMetadata | COCOA | A data.frame with patient metadata for breast cancer patients. | data.frame | 329 | 6 |
| brcaMethylData1 | COCOA | A matrix with DNA methylation levels from some CpGs on chromosome 1 | matrix | 6004 | 300 |
| brcaPCScores | COCOA | A matrix with principal component scores for PCs 1-4 for four breast cancer patients. | matrix | 4 | 4 |
| brcaPCScores657 | COCOA | A data.frame with principal component scores for PCs 1-4 for 657 breast cancer patients as well as a column with estrogen receptor status. | data.frame | 657 | 5 |
| esr1_chr1 | COCOA | Estrogen receptor alpha binding regions. | GRanges | | |
| gata3_chr1 | COCOA | Gata3 binding regions. | GRanges | | |
| nrf1_chr1 | COCOA | Nrf1 binding regions. | GRanges | | |
| rsScores | COCOA | Example COCOA Results (made up) | data.frame | 5 | 3 |
| data | DelayedEffect.Design | Data for examples | data.frame | 500 | 9 |
| example.assignedREDseq | REDseq | an example assigned REDseq dataset | list | | |
| example.map | REDseq | an example REmap dataset | GRanges | | |
| example.REDseq | REDseq | an example sequencing dataset from a restoration enzyme digestion (RED) experiment | GRanges | | |
| ExampleData | ClusterVAR | Datasets included in the ClusterVAR package | matrix | 18000 | 8 |
| SyntheticData | ClusterVAR | Datasets included in the ClusterVAR package | data.frame | 12998 | 10 |
| dis_data | BayesDissolution | A dissolution data set taken from Ocana et al. (2009). | data.frame | 24 | 9 |
| abm | medicaldata | Observational Dataset of Acute Bacterial Meningitis | tbl_df | 581 | 22 |
| blood_storage | medicaldata | Retrospective Cohort Study of the Effects of Blood Storage on Prostate Cancer | data.frame | 316 | 20 |
| cath | medicaldata | Observational Dataset of Cardiac Catheterization | tbl_df | 3504 | 6 |
| cdystonia | medicaldata | RCT of Botulinum Toxin B for Cervical Dystonia | tbl_df | 631 | 7 |
| covid_testing | medicaldata | Deidentified Results of COVID-19 testing at the Children's Hospital of Pennsylvania (CHOP) in 2020 | spec_tbl_df | 15524 | 17 |
| cytomegalovirus | medicaldata | Retrospective Cohort Study of the Effects of Donor KIR genotype on the reactivation of cytomegalovirus (CMV) after myeloablative allogeneic hematopoietic stem cell transplant. | data.frame | 64 | 26 |
| diabetes | medicaldata | Diabetes Prediction Dataset from the Pima Indian Tribe and the NIDDK | data.frame | 768 | 9 |
| esoph_ca | medicaldata | esoph_ca: Esophageal Cancer dataset | data.frame | 88 | 5 |
| indo_rct | medicaldata | RCT of Indomethacin for Prevention of Post-ERCP Pancreatitis | tbl_df | 602 | 33 |
| indometh | medicaldata | Cohort Study of the Pharmacokinetics of Intravenous Indomethacin | nfnGroupedData | 66 | 3 |
| laryngoscope | medicaldata | Randomized, Comparison Trial of Video vs. Standard Laryngoscope | data.frame | 99 | 22 |
| licorice_gargle | medicaldata | Randomized, Controlled Trial of Licorice Gargle before Intubation for Elective Thoracic Surgery | data.frame | 235 | 19 |
| opt | medicaldata | Obstetrics and Periodontal Therapy Dataset | data.frame | 823 | 171 |
| polyps | medicaldata | RCT of Sulindac for Polyp Prevention in Familial Adenomatous Polyposis | data.frame | 22 | 7 |
| scurvy | medicaldata | Randomized Trial of Six Therapies for Scurvy | tbl_df | 12 | 8 |
| smartpill | medicaldata | Prospective Cohort Study of Intestinal Transit using a SmartPill to Compare Trauma Patients to Healthy Volunteers | data.frame | 95 | 22 |
| strep_tb | medicaldata | RCT of Streptomycin Therapy for Tuberculosis | data.frame | 107 | 13 |
| supraclavicular | medicaldata | Study of Supraclavicular Anesthesia | data.frame | 103 | 17 |
| theoph | medicaldata | Cohort Study of the Pharmacokinetics of Oral Theophylline | nfnGroupedData | 132 | 5 |
| thiomon | medicaldata | Deidentified Laboratory Data on IBD Patients using Thiourines for at least 4 weeks and their Eventual Remission/Active Status after at least 12 Weeks of Therapy. | tbl_df | 5168 | 32 |
| Worldclim_tmp | USE | A subset of WorldClim bioclimatic variables | data.frame | 18953 | 8 |
| G01InitPop | EmiR | Data set for example G01 | matrix | 20 | 13 |
| choice_matrix | cIRT | Choice Matrix Data | data.frame | 3780 | 7 |
| payout_matrix | cIRT | Payout Matrix Data | data.frame | 252 | 4 |
| survey_data | cIRT | Survey Data | data.frame | 252 | 2 |
| trial_matrix | cIRT | Trial Matrix Data | data.frame | 252 | 30 |
| earwigs | allometry | Morphological measurement taken from 113 maritime earwigs (Anisolabis maritima); 68 males and 45 females. | spec_tbl_df | 113 | 12 |
| helmsi | allometry | Morphological measurement of 507 Helm’s stag beetles (Geodorcus helmsi) from Stewart Island, New Zealand. | tbl_df | 507 | 17 |
| AtomWeight | shinyNORRRM | The standard atomic weights | data.frame | 92 | 2 |
| Deccan | shinyNORRRM | Data collection of igneous rocks from the Deccan region (India) | data.frame | 7019 | 30 |
| EAP | shinyNORRRM | Data collection of igneous rocks from Eastern Mexican Alkaline Province | data.frame | 46 | 28 |
| IUGS | shinyNORRRM | Data collection of igneous rocks used in IUGSTAS software | data.frame | 37 | 28 |
| MinWeight | shinyNORRRM | The oxides molecular weights of normative minerals | data.frame | 36 | 5 |
| OxiWeight | shinyNORRRM | The molecular weights | data.frame | 26 | 2 |
| TephraKam | shinyNORRRM | Data collection of igneous rocks from the Kamchatka volcanic arc (northwestern Pacific) | data.frame | 7596 | 32 |
| er_network | missSBM | ER ego centered network | dgCMatrix | | |
| frenchblog2007 | missSBM | Political Blogosphere network prior to 2007 French presidential election | igraph | | |
| war | missSBM | War data set | list | | |
| coldis | sphet | Object of class distance for Columbus dataset 10-nearest neighbors matrix for columbus dataset | sphet | | |
| knn10columbus | sphet | Object of class distance for Columbus dataset 10-nearest neighbors matrix for columbus dataset | data.frame | 489 | 3 |
| perfectionism | negligible | Perfectionism Data | data.frame | 83 | 17 |
| tiwqs_data | gWQS | Measurement of 38 nutrients (NHANES dataset) | data.frame | 5960 | 50 |
| wqs_data | gWQS | Exposure concentrations of 34 PCB (simulated dataset) | data.frame | 500 | 68 |
| hnscc | highMLR | High dimensional head and neck cancer survival and gene expression data | data.frame | 565 | 104 |
| srdata | highMLR | High dimensional protein gene expression survival data | data.frame | 288 | 250 |
| neotropical_comm | FishPhyloMaker | Abundance of stream fish species in Parana and Paraguay streams | data.frame | 20 | 61 |
| spp_afrotropic | FishPhyloMaker | List of fish species with occurrence in Afrotropical ecoregion | character | | |
| taxon_data_PhyloMaker | FishPhyloMaker | Data frame with species names needed to assemble the phylogenetic tree | data.frame | 45 | 3 |
| fake_outbreak | outbreaker2 | Small simulated outbreak | list | | |
| benchmark_models | NBDCtools | Benchmark Models | list | | |
| racemap | rpkgdemo | Race mapping | character | | |
| rped | rpkgdemo | RPED: R Package Example Data | tbl_df | 1000 | 6 |
| zipcodes | rpkgdemo | ZIP code data | tbl_df | 37961 | 3 |
| esophagus | survSAKK | SAKK Esophagus Cancer Data | data.frame | 297 | 6 |
| nut_survey_map | oxthema | Example map data for showing map plotting with Oxford colours | sf | 15 | 12 |
| oxford_colours | oxthema | Oxford colours based on University of Oxford's visual identity guidelines | data.frame | 34 | 5 |
| Inputpara | rTRIPLEXCWFlux | Information about input parameters | data.frame | 1 | 31 |
| Inputvariable | rTRIPLEXCWFlux | Information about input variables | data.frame | 17520 | 18 |
| onemonth_exam | rTRIPLEXCWFlux | onemonth_exam | data.frame | 1488 | 18 |
| result | rTRIPLEXCWFlux | Information about model output | data.frame | 17520 | 25 |
| province | qcpm | Province dataset example | data.frame | 110 | 16 |
| medicalRecords | eikosograms | Mining medical records (fictional) | data.frame | 16 | 5 |
| tuberculosis_1910 | eikosograms | Tuberculosis 1910 death rates in New York and in Richmond | table | | |
| bali | bigergm | Bali terrorist network | network | | |
| bunt | bigergm | Van de Bunt friendship network | network | | |
| kapferer | bigergm | Kapferer collaboration network | network | | |
| reed | bigergm | A network of friendships between students at Reed College. | network | | |
| rice | bigergm | A network of friendships between students at Rice University. | network | | |
| state_twitter | bigergm | Twitter (X) network of U.S. state legislators | network | | |
| toyNet | bigergm | A toy network to play 'bigergm' with. | network | | |
| margin | rddensity | RD Senate Data | numeric | | |
| ant.final | GoodFibes | Ant muscle fibers finalized | list | | |
| ant.raw | GoodFibes | Raw ant muscle fibers | list | | |
| consensus | PolicyPortfolios | Policy portfolios for the dataset of the CONSENSUS research project. | tbl_df | 509220 | 6 |
| consensus.instruments | PolicyPortfolios | Meta-data on Instruments for the dataset of the CONSENSUS research project. | tbl_df | 19 | 4 |
| consensus.targets | PolicyPortfolios | Meta-data on Targets for the dataset of the CONSENSUS research project. | tbl_df | 67 | 4 |
| P.education | PolicyPortfolios | Simulated policy portfolio with fake data for the education sector. | tbl_df | 4950 | 6 |
| P.energy | PolicyPortfolios | Simulated policy portfolio with fake data for the energy sector. | tbl_df | 12375 | 6 |
| gcms1 | osd | Data from a GC-MS experiment | matrix | 71 | |
| gcms2 | osd | Data from a GC-MS experiment | matrix | 51 | |
| ref.spects | osd | Five MS reference spectra from the MassBank MS library for spectra comparison | list | | |
| scpairs_testdata | scPairs | Synthetic Seurat Test Object for scPairs Examples and Tests | Seurat | | |
| combined_graphs_example | DrDimont | Combined graphs | list | | |
| correlation_matrices_example | DrDimont | Correlation matrices | list | | |
| differential_graph_example | DrDimont | Differential graph | igraph | | |
| drug_gene_interactions | DrDimont | Drug-gene interactions | tbl_df | 22972 | 3 |
| drug_response_scores_example | DrDimont | Drug response score | data.frame | 261 | 2 |
| drug_target_edges_example | DrDimont | Drug target nodes in combined network | list | | |
| individual_graphs_example | DrDimont | Individual graphs | list | | |
| interaction_score_graphs_example | DrDimont | Interaction score graphs | list | | |
| layers_example | DrDimont | Formatted layers object | list | | |
| metabolite_data | DrDimont | Metabolomics data | list | | |
| metabolite_protein_interactions | DrDimont | Metabolite protein interaction data | tbl_df | 55499 | 3 |
| mrna_data | DrDimont | mRNA expression data | list | | |
| phosphosite_data | DrDimont | Phosphosite data | list | | |
| protein_data | DrDimont | Protein data | list | | |
| clootl_data | clootl | A complex data store used in the package. | list | | |
| concrete_example | tidyposterior | Example Data Sets | vfold_cv | 50 | 16 |
| contrast_samples | tidyposterior | Example Data Sets | posterior_diff | 10000 | 4 |
| noisy_example | tidyposterior | Example Data Sets | vfold_cv | 100 | 15 |
| posterior_samples | tidyposterior | Example Data Sets | posterior | 15000 | 2 |
| precise_example | tidyposterior | Example Data Sets | vfold_cv | 10 | 29 |
| ts_example | tidyposterior | Example Data Sets | rolling_origin | 52 | 14 |
| first_sentences | js4shiny | First Sentences of Books | tbl_df | 40 | 3 |
| us_cities_ranked | js4shiny | 125 US Cities Ranked, 2019 | tbl_df | 125 | 26 |
| ow | lax | Oxford and Worthing annual maximum temperatures | data.frame | 160 | 4 |
| beans | PlackettLuce | Preferred Bean Varieties in Nicaragua | data.frame | 842 | 14 |
| nascar | PlackettLuce | Results from 2002 NASCAR Season | matrix | 36 | 43 |
| pudding | PlackettLuce | Paired Comparisons of Chocolate Pudding | data.frame | 15 | 6 |
| salad | PlackettLuce | Rankings of Salad Dressings | data.frame | 32 | 4 |
| ba1986 | SimplyAgree | reps | data.frame | 17 | 5 |
| recpre_long | SimplyAgree | Data | tbl_df | 30 | 6 |
| reps | SimplyAgree | reps | data.frame | 20 | 3 |
| temps | SimplyAgree | Data | tbl_df | 60 | 10 |
| gripsYR1 | RCTRecruit | Daily recruitment data for the 1st year of the GRIPS study | data.frame | 159 | 2 |
| gripsYR2 | RCTRecruit | Daily recruitment data for the 2nd year of the GRIPS study | data.frame | 292 | 2 |
| gripsYR2Weekly | RCTRecruit | Weekly recruitment data for the 2nd year of the GRIPS study | data.frame | 52 | 4 |
| workforceHistory | hR | Workforce history data for a sample team of employees and contractors. | data.table | 45 | 10 |
| pbc_gmfamm | gmfamm | Subset of PBC data set for GMFAMM | data.frame | 5943 | 10 |
| simu | truncAIPW | A Simulated Data Set under Left Truncation but No Right Censoring | data.frame | 500 | 5 |
| simu_c1 | truncAIPW | A Simulated Data Set under Left Truncation and Right Censoring where Censoring can be before Truncation | data.frame | 500 | 5 |
| simu_c2 | truncAIPW | A Simulated Data Set under Left Truncation and Right Censoring where Censoring is always after Truncation | data.frame | 500 | 5 |
| autoboost.res.x | SelectBoost | Autoboost lasso diabetes first order. | selectboost | 13 | 10 |
| autoboost.res.x.adapt | SelectBoost | Autoboost adaptative lasso diabetes first order. | selectboost | 13 | 10 |
| autoboost.res.x2 | SelectBoost | Autoboost lasso diabetes second order. | selectboost | 13 | 64 |
| autoboost.res.x2.adapt | SelectBoost | Autoboost adaptative lasso diabetes second order. | selectboost | 13 | 64 |
| F_score_C | SelectBoost | Simulations for reverse-engineering | matrix | 100 | |
| F_score_PB | SelectBoost | Simulations for reverse-engineering | matrix | 100 | |
| F_score_PB_075_075 | SelectBoost | Simulations for reverse-engineering | matrix | 100 | |
| F_score_PB_095_075 | SelectBoost | Simulations for reverse-engineering | matrix | 100 | |
| F_score_PB_W | SelectBoost | Simulations for reverse-engineering | matrix | 100 | |
| F_score_PL | SelectBoost | Simulations for reverse-engineering | matrix | 100 | |
| F_score_PL2 | SelectBoost | Simulations for reverse-engineering | matrix | 100 | |
| F_score_PL2_tW | SelectBoost | Simulations for reverse-engineering | matrix | 100 | |
| F_score_PL2_W | SelectBoost | Simulations for reverse-engineering | matrix | 100 | |
| F_score_PSel | SelectBoost | Simulations for reverse-engineering | matrix | 100 | |
| F_score_PSel_W | SelectBoost | Simulations for reverse-engineering | matrix | 100 | |
| F_score_PSel.5 | SelectBoost | Simulations for reverse-engineering | matrix | 100 | |
| F_score_PSel.5.e2 | SelectBoost | Simulations for reverse-engineering | matrix | 100 | |
| F_score_PSel.e2 | SelectBoost | Simulations for reverse-engineering | matrix | 100 | |
| F_score_robust | SelectBoost | Simulations for reverse-engineering | matrix | 100 | |
| fastboost.res.x | SelectBoost | Fastboost lasso diabetes first order. | selectboost | 13 | 10 |
| fastboost.res.x.adapt | SelectBoost | Fastboost adaptative lasso diabetes first order. | selectboost | 13 | 10 |
| fastboost.res.x2 | SelectBoost | Fastboost lasso diabetes second order. | selectboost | 13 | 64 |
| fastboost.res.x2.adapt | SelectBoost | Fastboost adaptative lasso diabetes second order. | selectboost | 13 | 64 |
| M | SelectBoost | Simulated Cascade network and inference | micro_array | | |
| Net | SelectBoost | Simulated Cascade network and inference | network | | |
| net_confidence | SelectBoost | Confidence indices | network.confidence | | |
| net_confidence_.5 | SelectBoost | Confidence indices | network.confidence | | |
| net_confidence_thr | SelectBoost | Confidence indices | network.confidence | | |
| Net_inf_C | SelectBoost | Simulated Cascade network and inference | network | | |
| nv_C | SelectBoost | Simulations for reverse-engineering | numeric | | |
| nv_PB | SelectBoost | Simulations for reverse-engineering | numeric | | |
| nv_PB_075_075 | SelectBoost | Simulations for reverse-engineering | numeric | | |
| nv_PB_095_075 | SelectBoost | Simulations for reverse-engineering | numeric | | |
| nv_PB_W | SelectBoost | Simulations for reverse-engineering | numeric | | |
| nv_PL | SelectBoost | Simulations for reverse-engineering | numeric | | |
| nv_PL2 | SelectBoost | Simulations for reverse-engineering | numeric | | |
| nv_PL2_tW | SelectBoost | Simulations for reverse-engineering | numeric | | |
| nv_PL2_W | SelectBoost | Simulations for reverse-engineering | numeric | | |
| nv_PSel | SelectBoost | Simulations for reverse-engineering | numeric | | |
| nv_PSel_W | SelectBoost | Simulations for reverse-engineering | numeric | | |
| nv_PSel.5 | SelectBoost | Simulations for reverse-engineering | numeric | | |
| nv_PSel.5.e2 | SelectBoost | Simulations for reverse-engineering | numeric | | |
| nv_PSel.e2 | SelectBoost | Simulations for reverse-engineering | numeric | | |
| nv_robust | SelectBoost | Simulations for reverse-engineering | numeric | | |
| predictive_positive_value_C | SelectBoost | Simulations for reverse-engineering | matrix | 100 | |
| predictive_positive_value_PB | SelectBoost | Simulations for reverse-engineering | matrix | 100 | |
| predictive_positive_value_PB_075_075 | SelectBoost | Simulations for reverse-engineering | matrix | 100 | |
| predictive_positive_value_PB_095_075 | SelectBoost | Simulations for reverse-engineering | matrix | 100 | |
| predictive_positive_value_PB_W | SelectBoost | Simulations for reverse-engineering | matrix | 100 | |
| predictive_positive_value_PL | SelectBoost | Simulations for reverse-engineering | matrix | 100 | |
| predictive_positive_value_PL2 | SelectBoost | Simulations for reverse-engineering | matrix | 100 | |
| predictive_positive_value_PL2_tW | SelectBoost | Simulations for reverse-engineering | matrix | 100 | |
| predictive_positive_value_PL2_W | SelectBoost | Simulations for reverse-engineering | matrix | 100 | |
| predictive_positive_value_PSel | SelectBoost | Simulations for reverse-engineering | matrix | 100 | |
| predictive_positive_value_PSel_W | SelectBoost | Simulations for reverse-engineering | matrix | 100 | |
| predictive_positive_value_PSel.5 | SelectBoost | Simulations for reverse-engineering | matrix | 100 | |
| predictive_positive_value_PSel.5.e2 | SelectBoost | Simulations for reverse-engineering | matrix | 100 | |
| predictive_positive_value_PSel.e2 | SelectBoost | Simulations for reverse-engineering | matrix | 100 | |
| predictive_positive_value_robust | SelectBoost | Simulations for reverse-engineering | matrix | 100 | |
| sensitivity_C | SelectBoost | Simulations for reverse-engineering | matrix | 100 | |
| sensitivity_PB | SelectBoost | Simulations for reverse-engineering | matrix | 100 | |
| sensitivity_PB_075_075 | SelectBoost | Simulations for reverse-engineering | matrix | 100 | |
| sensitivity_PB_095_075 | SelectBoost | Simulations for reverse-engineering | matrix | 100 | |
| sensitivity_PB_W | SelectBoost | Simulations for reverse-engineering | matrix | 100 | |
| sensitivity_PL | SelectBoost | Simulations for reverse-engineering | matrix | 100 | |
| sensitivity_PL2 | SelectBoost | Simulations for reverse-engineering | matrix | 100 | |
| sensitivity_PL2_tW | SelectBoost | Simulations for reverse-engineering | matrix | 100 | |
| sensitivity_PL2_W | SelectBoost | Simulations for reverse-engineering | matrix | 100 | |
| sensitivity_PSel | SelectBoost | Simulations for reverse-engineering | matrix | 100 | |
| sensitivity_PSel_W | SelectBoost | Simulations for reverse-engineering | matrix | 100 | |
| sensitivity_PSel.5 | SelectBoost | Simulations for reverse-engineering | matrix | 100 | |
| sensitivity_PSel.5.e2 | SelectBoost | Simulations for reverse-engineering | matrix | 100 | |
| sensitivity_PSel.e2 | SelectBoost | Simulations for reverse-engineering | matrix | 100 | |
| sensitivity_robust | SelectBoost | Simulations for reverse-engineering | matrix | 100 | |
| test.seq_C | SelectBoost | Simulations for reverse-engineering | matrix | 100 | |
| test.seq_PB | SelectBoost | Simulations for reverse-engineering | matrix | 100 | |
| test.seq_PB_075_075 | SelectBoost | Simulations for reverse-engineering | matrix | 100 | |
| test.seq_PB_095_075 | SelectBoost | Simulations for reverse-engineering | matrix | 100 | |
| test.seq_PB_W | SelectBoost | Simulations for reverse-engineering | matrix | 100 | |
| test.seq_PL | SelectBoost | Simulations for reverse-engineering | matrix | 100 | |
| test.seq_PL2 | SelectBoost | Simulations for reverse-engineering | matrix | 100 | |
| test.seq_PL2_tW | SelectBoost | Simulations for reverse-engineering | matrix | 100 | |
| test.seq_PL2_W | SelectBoost | Simulations for reverse-engineering | matrix | 100 | |
| test.seq_PSel | SelectBoost | Simulations for reverse-engineering | matrix | 100 | |
| test.seq_PSel_W | SelectBoost | Simulations for reverse-engineering | matrix | 100 | |
| test.seq_PSel.5 | SelectBoost | Simulations for reverse-engineering | matrix | 100 | |
| test.seq_PSel.5.e2 | SelectBoost | Simulations for reverse-engineering | matrix | 100 | |
| test.seq_PSel.e2 | SelectBoost | Simulations for reverse-engineering | matrix | 100 | |
| test.seq_robust | SelectBoost | Simulations for reverse-engineering | matrix | 100 | |
| Eyam | MultiBD | Eyam plague. | data.frame | 8 | 4 |
| meteo | geotopbricks | Bondone Dataset | zoo | 72792 | 6 |
| gom | threshr | Storm peak significant wave heights from the Gulf of Mexico | numeric | | |
| ns | threshr | Storm peak significant wave heights from the North Sea | numeric | | |
| luad_maf | SelectSim | Lung adenocarcinoma MAF from TCGA cohort | data.frame | 220734 | 8 |
| luad_result | SelectSim | Lung adenocarcinoma from TCGA cohort as SelectSim run results | data.frame | 253 | 22 |
| luad_run_data | SelectSim | Lung adenocarcinoma from TCGA cohort as SelectSim run object | list | | |
| oncokb_genes | SelectSim | OncoKB v3.9 cancer genes | character | | |
| oncokb_truncating_genes | SelectSim | OncoKB v3.9 cancer genes consider for truncating mutations | character | | |
| variant_catalogue | SelectSim | OncoKB v3.9 cancer genes | data.frame | 2478 | 3 |
| darwin | robustmatrix | DARWIN (Diagnosis AlzheimeR WIth haNdwriting) | array | | |
| weather | robustmatrix | Glacier weather data – Sonnblick observatory | array | | |
| vatlongos | interlineaR | A corpus of 10 texts of the Vatlongos (vtk) language | list | | |
| datos | saery | Dataset for saery package | data.frame | 2000 | 6 |
| iris.l1.cluster | directlabels | Clustering of the iris data with the l1 clusterpath | data.frame | 9643 | 8 |
| LOPART.ROC | directlabels | ROC curve for LOPART algorithm and competitors | list | | |
| LOPART100 | directlabels | Labeled Optimal Partitioning (LOPART) results | list | | |
| normal.l2.cluster | directlabels | Clustering of some normal data in 2d with the l2 clusterpath | list | | |
| odd_timings | directlabels | Odd timings | data.frame | 116 | 4 |
| projectionSeconds | directlabels | Timings of projection algorithms | data.frame | 603 | 6 |
| prostate | directlabels | Prostate Cancer Data | data.frame | 97 | 10 |
| SegCost | directlabels | Cost of segmentation models | data.frame | 560 | 5 |
| svmtrain | directlabels | False positive rates from several 1-SVM models | data.frame | 378 | 5 |
| motor | dbnR | Multivariate time series dataset on the temperature of an electric motor | data.table | 3000 | 11 |
| world_map | orbis | World country polygons | data.frame | 26953 | 4 |
| core_426 | coreCT | Three computed tomography scans from a Spartina alterniflora core | list | | |
| lookup_table | readepi | Lookup table | data.frame | 11 | 3 |
| request_parameters | readepi | Request parameters | data.frame | 3 | 12 |
| sampleMultiTradeData | highfrequency | Multivariate tick by tick data | data.table | 43581 | 4 |
| sampleOneMinuteData | highfrequency | One minute data | data.table | 8602 | 3 |
| sampleQData | highfrequency | Sample of cleaned quotes for stock XXX for 2 days measured in microseconds | data.table | 46564 | 8 |
| sampleQDataRaw | highfrequency | Sample of raw quotes for stock XXX for 2 days measured in microseconds | data.table | 131401 | 7 |
| sampleTData | highfrequency | Sample of cleaned trades for stock XXX for 2 days | data.table | 7168 | 5 |
| sampleTDataEurope | highfrequency | European data | data.table | 33488 | 5 |
| sampleTDataRaw | highfrequency | Sample of raw trades for stock XXX for 2 days | data.table | 77263 | 7 |
| SPYRM | highfrequency | SPY realized measures | data.table | 1495 | 14 |
| geneMetadata | fastCNV | Genes Data from Ensembl Version 113 | tbl_df | 91726 | 10 |
| alignment_scores_values | pacta.executive.summary | Help dataset for plotting alignment scores on an axis | tbl_df | 6 | 5 |
| p4i_p4b_sector_technology_mapper | pacta.executive.summary | Sector and technology names mapped from P4I to P4B style | tbl_df | 28 | 4 |
| remaining_carbon_budgets | pacta.executive.summary | Sector level carbon budgets for scenarios | tbl_df | 8 | 5 |
| scenario_thresholds | pacta.executive.summary | Scenario names mapped to temperature thresholds | tbl_df | 9 | 3 |
| scores_real_estate | pacta.executive.summary | Help dataset for plotting real estate scores | tbl_df | 7 | 5 |
| toy_data_alignment_table | pacta.executive.summary | An example output data of 'prep_alignment_table()' | tbl_df | 40 | 7 |
| toy_data_diagram | pacta.executive.summary | An example output data of 'prep_diagram()' | tbl_df | 2 | 8 |
| toy_data_emissions_scorecard | pacta.executive.summary | An example output data of 'prep_emissions_scorecard()' | tbl_df | 4 | 3 |
| toy_data_exposures_scorecard | pacta.executive.summary | An example output data of 'prep_exposures_scorecard()' | tbl_df | 8 | 3 |
| toy_data_exposures_survey | pacta.executive.summary | An example output data of 'prep_exposures_survey()' | tbl_df | 12 | 4 |
| toy_data_fossil_bars | pacta.executive.summary | An example output data of 'prep_fossil_bars()' | tbl_df | 18 | 7 |
| toy_data_green_brown_bars | pacta.executive.summary | An example output data of 'prep_green_brown_bars()' | tbl_df | 18 | 6 |
| toy_data_scatter | pacta.executive.summary | An example output data of 'prep_scatter()' | tbl_df | 14 | 6 |
| toy_data_scores | pacta.executive.summary | An example output data of 'prep_scores()' | tbl_df | 28 | 5 |
| toy_data_scores_scorecard_re | pacta.executive.summary | An example input data to 'plot_scores_scorecard_real_estate()' | tbl_df | 2 | 2 |
| mixnorm_data | OVL.CI | Simulated data with normal and mixture of normal distributions | data.frame | 100 | 2 |
| test_data | OVL.CI | Simulated data with normal distributions | tbl_df | 100 | 2 |
| longDF | JointAI | Longitudinal example dataset | data.frame | 329 | 21 |
| NHANES | JointAI | National Health and Nutrition Examination Survey (NHANES) Data | data.frame | 186 | 13 |
| PBC | JointAI | PBC data | data.frame | 1945 | 21 |
| simLong | JointAI | Simulated Longitudinal Data in Long and Wide Format | data.frame | 2400 | 16 |
| simWide | JointAI | Simulated Longitudinal Data in Long and Wide Format | data.frame | 200 | 81 |
| wideDF | JointAI | Cross-sectional example dataset | data.frame | 100 | 13 |
| PWT_60_07 | csdm | Penn World Tables panel (93 countries, 1960-2007) | tbl_df | 4464 | 6 |
| Jester5k | recommenderlab | Jester dataset (5k sample) | realRatingMatrix | | |
| JesterJokes | recommenderlab | Jester dataset (5k sample) | character | | |
| MovieLense | recommenderlab | MovieLense Dataset (100k) | realRatingMatrix | | |
| MovieLenseMeta | recommenderlab | MovieLense Dataset (100k) | data.frame | 1664 | 22 |
| MovieLenseUser | recommenderlab | MovieLense Dataset (100k) | data.frame | 943 | 5 |
| MSWeb | recommenderlab | Anonymous web data from www.microsoft.com | binaryRatingMatrix | | |
| MaxTemp | SimCop | Extreme temperatures at two West Australian meteorological stations | data.frame | 96 | 2 |
| augusta_nlcd | landscapemetrics | Augusta NLCD 2011 | PackedSpatRaster | | |
| landscape | landscapemetrics | Example map (random cluster neutral landscape model). | PackedSpatRaster | | |
| lsm_abbreviations_names | landscapemetrics | Tibble of abbreviations coming from FRAGSTATS | tbl_df | 133 | 5 |
| podlasie_ccilc | landscapemetrics | Podlasie ESA CCI LC | PackedSpatRaster | | |
| BanglaContrac | HRW | Contraception use in Bangladesh | data.frame | 1934 | 5 |
| BCR | HRW | Berry, Carroll and Ruppert (2002) data | data.frame | 490 | 3 |
| BostonMortgages | HRW | Mortgage applications in Boston | data.frame | 2380 | 13 |
| brainImage | HRW | Brain image | data.frame | 80 | 37 |
| capm | HRW | Stock indices | data.frame | 2363 | 6 |
| carAuction | HRW | Cars purchased at auction | data.frame | 72983 | 51 |
| CHD | HRW | Coronory heart disease | data.frame | 256 | 4 |
| coral | HRW | Coral organisms in French Polynesia | data.frame | 11894 | 4 |
| femSBMD | HRW | Female spinal bone mineral densities | data.frame | 1003 | 7 |
| growthIndiana | HRW | Adolescent somatic growth in Indiana, U.S.A. | data.frame | 4123 | 5 |
| indonRespir | HRW | Respiratory infection in Indonesian children | data.frame | 1200 | 12 |
| lidar | HRW | Light detection and ranging | data.frame | 221 | 2 |
| ozoneSub | HRW | Ozone levels in midwest U.S.A. | data.frame | 147 | 3 |
| plankton | HRW | Flow cytometric measurements on plankton organisms | data.frame | 4000 | 7 |
| protein | HRW | Protein intake dietery study | data.frame | 588 | 6 |
| ragweed | HRW | Ragweed pollen in Kalamazoo, U.S.A. | data.frame | 334 | 7 |
| scallop | HRW | Scallop abundance off Long Island, U.S.A. | data.frame | 148 | 3 |
| schoolResults | HRW | School results in the United Kingdom | data.frame | 1905 | 5 |
| SydneyRealEstate | HRW | Sydney real estate | data.frame | 37676 | 39 |
| SydneyRealEstateBdry | HRW | Polygonal boundary of Sydney | data.frame | 203 | 2 |
| TreasuryRate | HRW | U.S. Treasury rate | data.frame | 3117 | 2 |
| UtahPEF | HRW | Peak expiratory flow in Utah, U.S.A. | data.frame | 3502 | 5 |
| WarsawApts | HRW | Apartment prices in Warsaw, Poland | data.frame | 409 | 6 |
| yields | HRW | U.S., European and Japanese yield curves | data.frame | 1565 | 91 |
| Agrawal.db | arulesNBMiner | Synthetic Example Dataset Agrawal | transactions | | |
| Agrawal.pat | arulesNBMiner | Synthetic Example Dataset Agrawal | itemsets | | |
| adhd | DTRlearn2 | A 2-stage SMART data of children with ADHD | data.frame | 150 | 11 |
| CD166_19_S1 | itraxR | Itrax core scanner data for core CD166_19_S1 | list | | |
| abdata | ivreg2r | Arellano-Bond (1991) UK Employment Panel | data.frame | 1031 | 16 |
| card | ivreg2r | Card (1995) College Proximity Dataset | data.frame | 3010 | 33 |
| cigar | ivreg2r | Cigarette Demand Panel (Baltagi-Levin / Baltagi-Griffin-Xiong) | data.frame | 1380 | 9 |
| griliches | ivreg2r | Griliches (1976) Young Men's Wages Dataset | data.frame | 758 | 20 |
| grunfeld | ivreg2r | Grunfeld (1958) Corporate Investment Panel | data.frame | 200 | 6 |
| klein | ivreg2r | Klein (1950) Model I Macroeconomic Data | data.frame | 22 | 12 |
| mroz | ivreg2r | Mroz (1987) Female Labor Supply Dataset | data.frame | 753 | 22 |
| nlswork | ivreg2r | NLS Young Women Panel (Stata '[XT]' Manual Extract) | data.frame | 28534 | 21 |
| phillips | ivreg2r | Phillips Curve Macroeconomic Dataset | data.frame | 49 | 11 |
| stockwatson | ivreg2r | Stock and Watson U.S. Macroeconomic Dataset | data.frame | 168 | 17 |
| wagepan | ivreg2r | Wooldridge Wage Panel Dataset | data.frame | 4360 | 11 |
| sim_outbreak | tidyabc | The 'sim_outbreak' dataset | list | | |
| DengueBEASTMCC | treespace | Dengue fever BEAST MCC tree | phylo | | |
| DengueSeqs | treespace | Dengue fever sequences | DNAbin | 17 | |
| DengueTrees | treespace | BEAST analysis of Dengue fever | multiPhylo | | |
| fluTrees | treespace | BEAST analysis of seasonal influenza (A/H3N2) | multiPhylo | | |
| woodmiceTrees | treespace | Bootstrap trees from woodmouse dataset | multiPhylo | | |
| ALLMARKERS | codestral | Markers for Codestral | data.frame | 5 | 2 |
| ENDPOINTS | codestral | Endpoints for the Codestral API. | list | | |
| seg_verbs | adobeanalyticsr | Verbs available in segment rules. | data.frame | 34 | 5 |
| STEHE | VIC5 | Sample datasets of the Stehekin for the running of the VIC model provided by UW Hydro | list | | |
| veglib_IGBP | VIC5 | IGBP vegetation library for VIC model | data.table | 17 | 58 |
| data_P_WL | hydroEvents | Example sub-daily rainfall and tidal water level data | list | | |
| dataBassRiver | hydroEvents | Streamflow data | numeric | | |
| dataCatchment | hydroEvents | Catchment data | list | | |
| dataLoch | hydroEvents | Rainfall data | numeric | | |
| hourlyQ | hydroEvents | Hourly streamflow data | data.frame | 89523 | 2 |
| WQ_Q | hydroEvents | Example water quality and streamflow data | list | | |
| toy_data | MAPCtools | Synthetic Age-Period-Cohort Dataset | tbl_df | 10000 | 7 |
| sbc_example | biocharkit | Synthetic example biochar dataset | data.frame | 9 | 7 |
| bakshy | webtrackR | Bakshy Top500 Ideological alignment of 500 domains based on facebook data | data.table | 500 | 7 |
| domain_list | webtrackR | Domain list classification of domains into news,portals, search, and social media | data.table | 663 | 2 |
| fake_tracking | webtrackR | Fake data | data.frame | 500 | 3 |
| news_types | webtrackR | News Types | data.table | 690 | 2 |
| testdt_survey_l | webtrackR | Test survey | tbl_df | 15 | 7 |
| testdt_survey_w | webtrackR | Test survey | data.frame | 5 | 8 |
| testdt_tracking | webtrackR | Test data | data.frame | 49612 | 5 |
| example_data | bulletcp | Example of an average of 2D crosscuts from the Hamby 44 data set. | data.frame | 3346 | 2 |
| fpar | lue | Fpar data | RasterLayer | | |
| par1 | lue | Photosynthetically Active Radiation | RasterBrick | | |
| tdew | lue | Dewpoint Temperature | RasterBrick | | |
| tmax | lue | Maximum temperature data | RasterBrick | | |
| tmin | lue | Minimum temperature data | RasterBrick | | |
| NELS88 | copulaData | National Education Longitudinal Study Data | data.frame | 1000 | 11 |
| nursingHomes | copulaData | Wisconsin Nursing Homes Utilization Data | data.frame | 2497 | 10 |
| database | iTALK | Ligand-Receptor Interaction Database | data.frame | 2649 | 6 |
| aflw | mulgar | AFLW player statistics | tbl_df | 381 | 35 |
| anomaly1 | mulgar | Data sets with anomalies | tbl_df | 201 | 4 |
| anomaly2 | mulgar | Data sets with anomalies | tbl_df | 201 | 4 |
| anomaly3 | mulgar | Data sets with anomalies | tbl_df | 97 | 4 |
| anomaly4 | mulgar | Data sets with anomalies | tbl_df | 201 | 4 |
| anomaly5 | mulgar | Data sets with anomalies | tbl_df | 201 | 4 |
| assoc1 | mulgar | Data sets with different types of association | tbl_df | 136 | 4 |
| assoc2 | mulgar | Data sets with different types of association | tbl_df | 322 | 4 |
| assoc3 | mulgar | Data sets with different types of association | tbl_df | 175 | 4 |
| box | mulgar | 3D plane in 5D | data.frame | 200 | 5 |
| bushfires | mulgar | Australian bushfires 2019-2020 | tbl_df | 1021 | 60 |
| c1 | mulgar | Cluster challenge data sets | tbl_df | 300 | 6 |
| c2 | mulgar | Cluster challenge data sets | tbl_df | 600 | 6 |
| c3 | mulgar | Cluster challenge data sets | tbl_df | 729 | 10 |
| c4 | mulgar | Cluster challenge data sets | tbl_df | 400 | 7 |
| c5 | mulgar | Cluster challenge data sets | tbl_df | 200 | 10 |
| c6 | mulgar | Cluster challenge data sets | tbl_df | 1500 | 4 |
| c7 | mulgar | Cluster challenge data sets | tbl_df | 1300 | 6 |
| clusters | mulgar | Three clusters in 5D | data.frame | 300 | 6 |
| clusters_nonlin | mulgar | Four unusually shaped clusters in 4D | data.frame | 1268 | 4 |
| copclayton | mulgar | Data sets generated using copulas | data.frame | 467 | 5 |
| copfrank | mulgar | Data sets generated using copulas | data.frame | 467 | 5 |
| copjoe | mulgar | Data sets generated using copulas | data.frame | 467 | 5 |
| copnorm | mulgar | Data sets generated using copulas | data.frame | 467 | 5 |
| fake_trees | mulgar | A high-dimensional tree data structure with 10 branching points. | data.frame | 3000 | 101 |
| multicluster | mulgar | Multiple clusters of different sizes, shapes and distance from each other | tbl_df | 400 | 11 |
| pisa | mulgar | PISA scores | grouped_df | 10548 | 31 |
| plane | mulgar | 2D plane in 5D | data.frame | 100 | 5 |
| plane_nonlin | mulgar | Non-linear relationship in 5D | data.frame | 100 | 5 |
| simple_clusters | mulgar | Two clusters in 2D | data.frame | 137 | 3 |
| sketches_test | mulgar | Images of sketches for testing | tbl_df | 1200 | 786 |
| sketches_train | mulgar | Images of sketches for training | tbl_df | 5998 | 786 |
| ExampleData1 | airship | Example Data 1 | data.frame | 81000 | 7 |
| ExampleData2 | airship | Example Data 2 | data.frame | 10080 | 18 |
| admixed | ggmix | Simulated Dataset with 1D Geography | list | | |
| karim | ggmix | Karim's Simulated Data | list | | |
| cvt | invivoPKfit | CvTdb data | tbl_df | 22182 | 60 |
| cvt_2.0.0 | invivoPKfit | CvTdb data for invivoPKfit 2.0.0 release (old) | tbl_df | 7918 | 62 |
| cvt_date | invivoPKfit | CvTdb download date | Date | | |
| cvtdb_original | invivoPKfit | SQL query result (current) | data.frame | 35669 | 59 |
| model_1comp | invivoPKfit | 1-compartment model | list | | |
| model_2comp | invivoPKfit | 2-compartment model | list | | |
| model_flat | invivoPKfit | Flat model | list | | |
| model_httk_gas_pbtk | invivoPKfit | Gas pbtk 'httk' model | list | | |
| pkdataset_nheerlcleaned | invivoPKfit | Toxicokinetic data from the "Concentration vs. Time Database" | data.table | 2454 | 19 |
| status_data_info | invivoPKfit | Status ID for data summary info | integer | | |
| status_fit | invivoPKfit | Status ID for fitting | integer | | |
| status_init | invivoPKfit | Status ID for initialization | integer | | |
| status_prefit | invivoPKfit | Status ID for pre-fitting | integer | | |
| status_preprocess | invivoPKfit | Status ID for preprocessing | integer | | |
| time_conversions | invivoPKfit | Time conversion table | data.frame | 121 | 3 |
| time_units | invivoPKfit | Allowable time units | character | | |
| dataisl | recluster | West Mediterranean island butterflies provided with the package recluster | matrix | 31 | 124 |
| datamod | recluster | Virtual island faunas provided with the package recluster | matrix | 9 | 31 |
| multiboot | recluster | A multiboot result obtained with the dataisl dataset. | data.frame | 29 | 30 |
| treebut | recluster | Phylogenetic tree for the butterfly species included in dataisl dataset | phylo | | |
| treemod | recluster | Hypothetical phylogenetic tree for the virtual island faunas provided with the package recluster | phylo | | |
| kootenayBlocks | ForestTools | Kootenay forest - Cut blocks | sf | 3 | 4 |
| kootenayCHM | ForestTools | Kootenay forest - Canopy height model | PackedSpatRaster | | |
| kootenayCrowns | ForestTools | Kootenay forest - Tree crowns | sf | 891 | 5 |
| kootenayOrtho | ForestTools | Kootenay forest - Orthomosaic | PackedSpatRaster | | |
| kootenayTrees | ForestTools | Kootenay forest - Dominant trees over 2 m | sf | 891 | 4 |
| quesnelBlocks | ForestTools | Quesnel forest - Cut blocks | sf | 9 | 2 |
| quesnelCHM | ForestTools | Quesnel forest - Canopy height model | PackedSpatRaster | | |
| quesnelTrees | ForestTools | Quesnel forest - Dominant trees over 2 m | sf | 24465 | 4 |
| A_thaliana | GOCompare | A thaliana functional enrichment analysis of 2224 ortholog genes related to cancer-hallmarks | data.frame | 4063 | 6 |
| A_thaliana_compress | GOCompare | A thaliana functional enrichment analysis results for "AID","DCE","RCD","SPS" cancer-hallmarks | data.frame | 120 | 6 |
| comparison_ex_compress | GOCompare | Functional enrichment analysis comparison between H. sapiens and A. thaliana for "AID","DCE","RCD","SPS" cancer-hallmarks | list | | |
| comparison_ex_compress_CH | GOCompare | Functional enrichment analysis comparison between H. sapiens and A. thaliana for "DCE", and "RCD" cancer-hallmarks. This dataset contains 10 GO terms per category to allow a fast run of the function graph_two_GOspecies. | list | | |
| H_sapiens | GOCompare | H. sapiens functional enrichment analysis of 5494 genes related to cancer-hallmarks | data.frame | 5000 | 6 |
| H_sapiens_compress | GOCompare | H. sapiens functional enrichment analysis results for "AID","DCE","RCD","SPS" cancer-hallmarks | data.frame | 120 | 6 |
| msdat3 | mmbcv | Clustered multistate simulated dataset (counting-process format) | data.frame | 3004 | 9 |
| nhanes | pomcheckr | National Health and Nutrition Examination Survey 2011-2012 | tbl_df | 9756 | 16 |
| ologit | pomcheckr | Simulated data for ordinal logistic regression example. | tbl_df | 400 | 4 |
| datt_rural | pipr | Datt (1998) grouped data for rural india, 1983 | data.frame | 13 | 6 |
| datt_urban | pipr | Grouped data for urban india, 1983 | data.frame | 13 | 6 |
| bed.example | StratigrapheR | Data for examples | data.frame | 36 | 6 |
| boundary.example | StratigrapheR | Data for examples | data.frame | 7 | 2 |
| chron.example | StratigrapheR | Data for examples | data.frame | 3 | 4 |
| example.ammonite | StratigrapheR | Data for examples | data.frame | 277 | 4 |
| example.ammonite.svg | StratigrapheR | Data for examples | character | | |
| example.belemnite | StratigrapheR | Data for examples | data.frame | 153 | 4 |
| example.breccia | StratigrapheR | Data for examples | data.frame | 23 | 4 |
| example.HB2000.svg | StratigrapheR | Data for examples | character | | |
| example.lense | StratigrapheR | Data for examples | data.frame | 27 | 4 |
| example.liquefaction | StratigrapheR | Data for examples | data.frame | 55 | 4 |
| fossil.example | StratigrapheR | Data for examples | data.frame | 4 | 2 |
| irreg.example | StratigrapheR | Data for examples | data.frame | 274 | 2 |
| log.loop.tex | StratigrapheR | Data for examples | character | | |
| noise_emd | StratigrapheR | Data for examples | data.frame | 6000 | 11 |
| oufti99 | StratigrapheR | Collections of symbols | list | | |
| proxy.example | StratigrapheR | Data for examples | data.frame | 76 | 3 |
| proxy.example.litho | StratigrapheR | Data for examples | data.frame | 76 | 4 |
| tie.points.example | StratigrapheR | Data for examples | data.frame | 10 | 6 |
| zeq_example | StratigrapheR | Data for examples | data.frame | 13 | 5 |
| example_svg_data | SVG | Example Spatial Transcriptomics Data | list | | |
| ehr_data | PheCAP | A Synthetic EHR Dataset | data.frame | 10000 | 589 |
| CRediT | contribution | CRediT | spec_tbl_df | 14 | 2 |
| demo | contribution | A demo for plotting contribution table | tbl_df | 5 | 4 |
| palette | contribution | palette | tbl_df | 5 | 27 |
| cemfgs_rb | diathor | CEMFGS_RB | data.frame | 495 | 2 |
| dbc_offline | diathor | DBC (offline) | tbl_df | 9233 | 112 |
| ddi | diathor | DDI | data.frame | 137 | 3 |
| des | diathor | DES | data.frame | 528 | 3 |
| diat_sampleData | diathor | Sample Data | data.frame | 164 | 109 |
| disp | diathor | DISP | data.frame | 141 | 3 |
| edi | diathor | EDI | data.frame | 147 | 3 |
| epid | diathor | EPID | data.frame | 1070 | 3 |
| idap | diathor | IDAP | data.frame | 194 | 3 |
| idch | diathor | ID-CH | data.frame | 551 | 3 |
| idp | diathor | IDP | data.frame | 298 | 2 |
| ilm | diathor | ILM | data.frame | 495 | 3 |
| ips | diathor | IPS | data.frame | 6914 | 3 |
| lobo | diathor | LOBO | data.frame | 297 | 3 |
| pbidw | diathor | PBIDW | data.frame | 79 | 2 |
| pdise | diathor | PDISE | data.frame | 455 | 3 |
| sla | diathor | SLA | data.frame | 976 | 3 |
| spear | diathor | SPEAR(h) | data.frame | 300 | 2 |
| taxaList | diathor | taxaList | data.frame | 9806 | 1 |
| tdi | diathor | TDI | data.frame | 4146 | 3 |
| gramA | clampSeg | Patch clamp recording of gramicidin A | numeric | | |
| pokedex | rrapply | Pokedex of Pokemon GO | list | | |
| renewable_energy_by_country | rrapply | UNSD renewable energy share by country in 2016 | list | | |
| GM12878.40kb.raw.chr2 | TADCompare | A subset of chomosome 2 contact matrix, GM12878 cell line. | matrix | 1001 | 1001 |
| IMR90.40kb.raw.chr2 | TADCompare | A subset of chomosome 2 contact matrix, IMR90 cell line. | matrix | 1001 | 1001 |
| rao_chr22_prim | TADCompare | Chromosome 22 combined intrachromosomal primary contact matrix from Rao et al. 2014. | matrix | 704 | 704 |
| rao_chr22_rep | TADCompare | Chromosome 22 combined intrachromosomal replicate contact matrix from Rao et al. 2014. | matrix | 704 | 704 |
| time_mats | TADCompare | Chromosome 22 time-varying contact matrices from Rao et al. 2017. | list | | |
| fac | Rcriticor | factor "year" | factor | | |
| sit | Rcriticor | Cereal aphids in Rennes | numeric | | |
| sit2 | Rcriticor | Sitobion with replicates | numeric | | |
| time | Rcriticor | Fictitious daily temperatures | numeric | | |
| time3 | Rcriticor | mean daily temperature data in Rennes (France) | numeric | | |
| wy | Rcriticor | Series of fictitious observations | numeric | | |
| wy2 | Rcriticor | Series of fictitious observations | numeric | | |
| erplots_data | erplots | Simulated exposure-response data | tbl_df | 4000 | 16 |
| civic_education | civic.icarm | civic_education dataset | tbl_df | 800 | 9 |
| civic_german_credit | civic.icarm | civic_german_credit dataset | tbl_df | 1000 | 8 |
| civic_racism_survey | civic.icarm | Synthetic Racism and Civic Participation Survey | tbl_df | 1000 | 16 |
| civic_voting | civic.icarm | civic_voting dataset | tbl_df | 1000 | 10 |
| academy | pixarfilms | Pixar Academy awards and nominations | tbl_df | 91 | 3 |
| box_office | pixarfilms | Box office reception numbers | tbl_df | 31 | 5 |
| genres | pixarfilms | Genres describing Pixar films | tbl_df | 228 | 3 |
| pixar_films | pixarfilms | Pixar films | tbl_df | 31 | 6 |
| pixar_franchises | pixarfilms | Pixar film franchises | tbl_df | 17 | 2 |
| pixar_people | pixarfilms | People behind Pixar | tbl_df | 286 | 3 |
| pixar_rankings | pixarfilms | Pixar film rankings | tbl_df | 526 | 3 |
| public_response | pixarfilms | Critical and public response | tbl_df | 31 | 17 |
| themes_vox | pixarfilms | Themes in Pixar films by Vox | tbl_df | 124 | 2 |
| swamps | optimus | Dharawal National Park Upland Heath Swamps Plot Network | data.frame | 54 | 171 |
| taxus_bin | diffval | _Taxus baccata_ forests | matrix | 209 | 33 |
| exp_data | cdgd | Simulated example data | data.frame | 1000 | 5 |
| llraDat1 | eRm | An Artificial LLRA Data Set | data.frame | 150 | 26 |
| llraDat2 | eRm | An Artificial LLRA Data Set | data.frame | 70 | 21 |
| llradat3 | eRm | An Artificial LLRA Data Set | data.frame | 60 | 6 |
| lltmdat1 | eRm | Data for Computing Extended Rasch Models | data.frame | 100 | 30 |
| lltmdat2 | eRm | Data for Computing Extended Rasch Models | data.frame | 15 | 5 |
| lpcmdat | eRm | Data for Computing Extended Rasch Models | data.frame | 20 | 6 |
| lrsmdat | eRm | Data for Computing Extended Rasch Models | data.frame | 20 | 6 |
| pcmdat | eRm | Data for Computing Extended Rasch Models | data.frame | 20 | 7 |
| pcmdat2 | eRm | Data for Computing Extended Rasch Models | data.frame | 300 | 4 |
| raschdat1 | eRm | Data for Computing Extended Rasch Models | data.frame | 100 | 30 |
| raschdat1_RM_fitted | eRm | Data for Computing Extended Rasch Models | dRm | | |
| raschdat1_RM_lrres2 | eRm | Data for Computing Extended Rasch Models | LR | | |
| raschdat1_RM_plotDIF | eRm | Data for Computing Extended Rasch Models | list | | |
| raschdat2 | eRm | Data for Computing Extended Rasch Models | data.frame | 25 | 6 |
| raschdat3 | eRm | Data for Computing Extended Rasch Models | data.frame | 500 | 6 |
| raschdat4 | eRm | Data for Computing Extended Rasch Models | data.frame | 500 | 6 |
| rsmdat | eRm | Data for Computing Extended Rasch Models | data.frame | 20 | 6 |
| xmpl | eRm | Example Data | matrix | 300 | 30 |
| xmplbig | eRm | Example Data | matrix | 4096 | |
| Census2000 | SGDinference | Census2000 | data.frame | 26120 | 3 |
| mcmc_output_example | EcoDiet | The MCMC output for running the example dataset | jagsUI | | |
| hurricane | mverse | Data on Atlantic hurricanes in the U.S. between 1950 and 2012. | tbl_df | 94 | 14 |
| soccer | mverse | Number of cards given for each referee-player pair in soccer. | data.frame | 146028 | 26 |
| exampleNprcgenekeeprConfig | nprcgenekeepr | Example nprcgenekeepr configuration file (loadable) | character | | |
| examplePedigree | nprcgenekeepr | Example pedigree object (from ExamplePedigree.csv) | data.frame | 3694 | 13 |
| finalRpt | nprcgenekeepr | Genetic-value report list prior to ranking | list | | |
| focalAnimals | nprcgenekeepr | Focal animal IDs from examplePedigree | data.frame | 327 | 1 |
| lacy1989Ped | nprcgenekeepr | Small hypothetical pedigree (Lacy 1989) | data.frame | 7 | 5 |
| lacy1989PedAlleles | nprcgenekeepr | Gene-drop alleles for lacy1989Ped (5000 iterations) | data.frame | 14 | 5002 |
| ped1Alleles | nprcgenekeepr | Gene-drop alleles example (baboon pedigree) | data.frame | 554 | 6 |
| pedDuplicateIds | nprcgenekeepr | Example studbook with a duplicated record | data.frame | 9 | 5 |
| pedFemaleSireMaleDam | nprcgenekeepr | Example studbook with sex-mismatched parents | data.frame | 8 | 5 |
| pedGood | nprcgenekeepr | Valid example studbook (no QC errors) | data.frame | 8 | 5 |
| pedInvalidDates | nprcgenekeepr | Example studbook with invalid birth dates | data.frame | 8 | 5 |
| pedMissingBirth | nprcgenekeepr | Example studbook missing the birth date column | data.frame | 8 | 4 |
| pedOne | nprcgenekeepr | Raw pedigree-file fragment for testing (5 columns) | data.frame | 8 | 5 |
| pedSameMaleIsSireAndDam | nprcgenekeepr | Example studbook with a male as both sire and dam | data.frame | 8 | 5 |
| pedSix | nprcgenekeepr | Raw pedigree-file fragment for testing (7 columns) | data.frame | 8 | 7 |
| pedWithGenotype | nprcgenekeepr | Pedigree with simulated genotypes (from qcPed) | data.frame | 280 | 12 |
| pedWithGenotypeReport | nprcgenekeepr | Genetic-value report for pedWithGenotype | list | | |
| qcBreeders | nprcgenekeepr | Potential breeder IDs (29 baboons) | character | | |
| qcPed | nprcgenekeepr | Example quality-controlled baboon pedigree | data.frame | 280 | 8 |
| qcPedGvReport | nprcgenekeepr | Genetic-value report for qcPed | list | | |
| rhesusGenotypes | nprcgenekeepr | Rhesus genotypes (two haplotypes per animal) | data.frame | 31 | 3 |
| rhesusPedigree | nprcgenekeepr | Obfuscated rhesus pedigree object | data.frame | 375 | 8 |
| smallPed | nprcgenekeepr | Hypothetical 17-animal pedigree | data.frame | 17 | 6 |
| smallPedTree | nprcgenekeepr | Pedigree tree built from smallPed | list | | |
| speciesGestation | nprcgenekeepr | Per-species reproductive parameters | data.frame | 14 | 4 |
| rao_chr20_25_rep | SpectralTAD | Contact matrix from Rao 2014, chromosome 20, 25kb resolution | data.frame | 2125980 | 3 |
| malware | sboost | Malware System Calls | data.frame | 7597 | 361 |
| mushrooms | sboost | Mushroom Classification | tbl_df | 8124 | 23 |
| blocker | bnma | Beta blockers to prevent mortality after myocardial infarction | list | | |
| cardiovascular | bnma | Trials of low dose and high dose statins for cardiovascular disease vs. placebo | list | | |
| certolizumab | bnma | Trials of certolizumab pegol (CZP) for the treatment of rheumatoid arthritis in patients | list | | |
| parkinsons | bnma | Dopamine agonists as adjunct therapy in Parkinson's disease | list | | |
| parkinsons_contrast | bnma | Dopamine agonists as adjunct therapy in Parkinson's disease | list | | |
| smoking | bnma | Smoking cessation counseling programs | list | | |
| statins | bnma | Trials of statins for cholesterol lowering vs. placebo or usual care | list | | |
| thrombolytic | bnma | Thrombolytic drugs and percutaneous transluminal coronary angioplasty | list | | |
| accidents | bnRep | accidents Bayesian Network | bn.fit | | |
| adhd | bnRep | adhd Bayesian Network | bn.fit | | |
| adversarialbehavior | bnRep | adversarialbehavior Bayesian Network | bn.fit | | |
| aerialvehicles | bnRep | aerialvehicles Bayesian Network | bn.fit | | |
| agropastoral1 | bnRep | agropastoral Bayesian Networks | bn.fit | | |
| agropastoral2 | bnRep | agropastoral Bayesian Networks | bn.fit | | |
| agropastoral3 | bnRep | agropastoral Bayesian Networks | bn.fit | | |
| agropastoral4 | bnRep | agropastoral Bayesian Networks | bn.fit | | |
| agropastoral5 | bnRep | agropastoral Bayesian Networks | bn.fit | | |
| aircrash | bnRep | aircrash Bayesian Network | bn.fit | | |
| airegulation1 | bnRep | airegulation Bayesian Networks | bn.fit | | |
| airegulation2 | bnRep | airegulation Bayesian Networks | bn.fit | | |
| airegulation3 | bnRep | airegulation Bayesian Networks | bn.fit | | |
| algal1 | bnRep | algal Bayesian Networks | bn.fit | | |
| algal2 | bnRep | algal Bayesian Networks | bn.fit | | |
| algalactivity1 | bnRep | algalactivity Bayesian Networks | bn.fit | | |
| algalactivity2 | bnRep | algalactivity Bayesian Networks | bn.fit | | |
| algorithms1 | bnRep | algorithms Bayesian Networks | bn.fit | | |
| algorithms2 | bnRep | algorithms Bayesian Networks | bn.fit | | |
| algorithms3 | bnRep | algorithms Bayesian Networks | bn.fit | | |
| algorithms4 | bnRep | algorithms Bayesian Networks | bn.fit | | |
| algorithms5 | bnRep | algorithms Bayesian Networks | bn.fit | | |
| algorithms6 | bnRep | algorithms Bayesian Networks | bn.fit | | |
| APSsystem | bnRep | APSsystem Bayesian Network | bn.fit | | |
| arcticwaters | bnRep | arcticwaters Bayesian Network | bn.fit | | |
| argument | bnRep | argument Bayesian Network | bn.fit | | |
| asia | bnRep | asia Bayesian Network | bn.fit | | |
| aspergillus | bnRep | aspergillus Bayesian Network | bn.fit | | |
| augmenting | bnRep | augmenting Bayesian Network | bn.fit | | |
| bank | bnRep | bank Bayesian Network | bn.fit | | |
| bankruptcy | bnRep | bankruptcy Bayesian Network | bn.fit | | |
| beam1 | bnRep | beams Bayesian Network | bn.fit | | |
| beam2 | bnRep | beams Bayesian Network | bn.fit | | |
| beatles | bnRep | beatles Bayesian Network | bn.fit | | |
| blacksea | bnRep | blacksea Bayesian Network | bn.fit | | |
| blockchain | bnRep | blockchain Bayesian Network | bn.fit | | |
| bnRep_summary | bnRep | BnRep Summary | data.frame | 224 | 17 |
| BOPfailure1 | bnRep | BOPfailure Bayesian Networks | bn.fit | | |
| BOPfailure2 | bnRep | BOPfailure Bayesian Networks | bn.fit | | |
| BOPfailure3 | bnRep | BOPfailure Bayesian Networks | bn.fit | | |
| building | bnRep | building Bayesian Network | bn.fit | | |
| bullet | bnRep | bullet Bayesian Network | bn.fit | | |
| burglar | bnRep | burglar Bayesian Network | bn.fit | | |
| cachexia1 | bnRep | cachexia Bayesian Networks | bn.fit | | |
| cachexia2 | bnRep | cachexia Bayesian Networks | bn.fit | | |
| cardiovascular | bnRep | cardiovascular Bayesian Network | bn.fit | | |
| case | bnRep | case Bayesian Network | bn.fit | | |
| catchment | bnRep | catchment Bayesian Network | bn.fit | | |
| charleston | bnRep | charleston Bayesian Network | bn.fit | | |
| chds | bnRep | chds Bayesian Network | bn.fit | | |
| cng | bnRep | cng Bayesian Network | bn.fit | | |
| compaction | bnRep | compaction Bayesian Network | bn.fit | | |
| conasense | bnRep | conasense Bayesian Network | bn.fit | | |
| concrete1 | bnRep | concrete Bayesian Networks | bn.fit | | |
| concrete2 | bnRep | concrete Bayesian Networks | bn.fit | | |
| concrete3 | bnRep | concrete Bayesian Networks | bn.fit | | |
| concrete4 | bnRep | concrete Bayesian Networks | bn.fit | | |
| concrete5 | bnRep | concrete Bayesian Networks | bn.fit | | |
| concrete6 | bnRep | concrete Bayesian Networks | bn.fit | | |
| concrete7 | bnRep | concrete Bayesian Networks | bn.fit | | |
| consequenceCovid | bnRep | consequenceCovid Bayesian Network | bn.fit | | |
| constructionproductivity | bnRep | constructionproductivity Bayesian Network | bn.fit | | |
| coral1 | bnRep | coral Bayesian Networks | bn.fit | | |
| coral2 | bnRep | coral Bayesian Networks | bn.fit | | |
| coral3 | bnRep | coral Bayesian Networks | bn.fit | | |
| coral4 | bnRep | coral Bayesian Networks | bn.fit | | |
| coral5 | bnRep | coral Bayesian Networks | bn.fit | | |
| corical | bnRep | corical Bayesian Network | bn.fit | | |
| corrosion | bnRep | corrosion Bayesian Network | bn.fit | | |
| corticosteroid | bnRep | corticosteroid Bayesian Network | bn.fit | | |
| covid1 | bnRep | covid Bayesian Networks | bn.fit | | |
| covid2 | bnRep | covid Bayesian Networks | bn.fit | | |
| covid3 | bnRep | covid Bayesian Networks | bn.fit | | |
| covidfear | bnRep | covidfear Bayesian Network | bn.fit | | |
| covidrisk | bnRep | covidrisk Bayesian Network | bn.fit | | |
| covidtech | bnRep | covidtech Bayesian Network | bn.fit | | |
| covidtest | bnRep | covidtest Bayesian Network | bn.fit | | |
| crimescene | bnRep | crimescene Bayesian Network | bn.fit | | |
| criminal1 | bnRep | criminal Bayesian Networks | bn.fit | | |
| criminal2 | bnRep | criminal Bayesian Networks | bn.fit | | |
| criminal3 | bnRep | criminal Bayesian Networks | bn.fit | | |
| criminal4 | bnRep | criminal Bayesian Networks | bn.fit | | |
| crypto | bnRep | crypto Bayesian Network | bn.fit | | |
| curacao1 | bnRep | curacao Bayesian Networks | bn.fit | | |
| curacao2 | bnRep | curacao Bayesian Networks | bn.fit | | |
| curacao3 | bnRep | curacao Bayesian Networks | bn.fit | | |
| curacao4 | bnRep | curacao Bayesian Networks | bn.fit | | |
| curacao5 | bnRep | curacao Bayesian Networks | bn.fit | | |
| darktriad | bnRep | darktriad Bayesian Network | bn.fit | | |
| diabetes | bnRep | ciabetes Bayesian Network | bn.fit | | |
| diagnosis | bnRep | diagnosis Bayesian Network | bn.fit | | |
| dioxins | bnRep | dioxins Bayesian Network | bn.tan | | |
| disputed1 | bnRep | disputed Bayesian Networks | bn.fit | | |
| disputed2 | bnRep | disputed Bayesian Networks | bn.fit | | |
| disputed3 | bnRep | disputed Bayesian Networks | bn.fit | | |
| disputed4 | bnRep | disputed Bayesian Networks | bn.fit | | |
| dragline | bnRep | dragline Bayesian Network | bn.fit | | |
| drainage | bnRep | drainage Bayesian Network | bn.fit | | |
| dustexplosion | bnRep | dustexplosion Bayesian Network | bn.fit | | |
| earthquake | bnRep | earthquake Bayesian Network | bn.fit | | |
| ecosystem | bnRep | ecosystem Bayesian Network | bn.fit | | |
| electricvehicle | bnRep | electricvehicle Bayesian Network | bn.fit | | |
| electrolysis | bnRep | electrolysis Bayesian Network | bn.fit | | |
| emergency | bnRep | emergency Bayesian Network | bn.fit | | |
| engines | bnRep | engines Bayesian Network | bn.fit | | |
| enrollment | bnRep | enrollment Bayesian Network | bn.fit | | |
| estuary | bnRep | estuary Bayesian Network | bn.fit | | |
| ets | bnRep | ets Bayesian Network | bn.fit | | |
| expenditure | bnRep | expenditure Bayesian Network | bn.fit | | |
| fingermarks1 | bnRep | fingermarks Bayesian Networks | bn.fit | | |
| fingermarks2 | bnRep | fingermarks Bayesian Networks | bn.fit | | |
| fire | bnRep | fire Bayesian Network | bn.fit | | |
| firealarm | bnRep | firealarm Bayesian Network | bn.fit | | |
| firerisk | bnRep | firerisk Bayesian Network | bn.fit | | |
| flood | bnRep | flood Bayesian Network | bn.fit | | |
| fluids1 | bnRep | fluids Bayesian Networks | bn.fit | | |
| fluids2 | bnRep | fluids Bayesian Networks | bn.fit | | |
| fluids3 | bnRep | fluids Bayesian Networks | bn.fit | | |
| foodallergy1 | bnRep | foodallergy Bayesian Networks | bn.fit | | |
| foodallergy2 | bnRep | foodallergy Bayesian Networks | bn.fit | | |
| foodallergy3 | bnRep | foodallergy Bayesian Networks | bn.fit | | |
| foodsecurity | bnRep | foodsecurity Bayesian Network | bn.fit | | |
| forest | bnRep | forest Bayesian Network | bn.fit | | |
| fundraising | bnRep | fundraising Bayesian Network | bn.fit | | |
| gasexplosion | bnRep | gasexplosion Bayesian Network | bn.fit | | |
| gasifier | bnRep | gasifier Bayesian Network | bn.fit | | |
| GDIpathway1 | bnRep | GDIpathway Bayesian Networks | bn.fit | | |
| GDIpathway2 | bnRep | GDIpathway Bayesian Networks | bn.fit | | |
| gonorrhoeae | bnRep | gonorrhoeae Bayesian Network | bn.fit | | |
| greencredit | bnRep | greencredit Bayesian Network | bn.fit | | |
| grounding | bnRep | grounding Bayesian Network | bn.fit | | |
| healthinsurance | bnRep | healthinsurance Bayesian Network | bn.fit | | |
| humanitarian | bnRep | humanitarian Bayesian Network | bn.fit | | |
| hydraulicsystem | bnRep | hydraulicsystem Bayesian Network | bn.fit | | |
| income | bnRep | income Bayesian Network | bn.fit | | |
| intensification | bnRep | intensification Bayesian Network | bn.fit | | |
| intentionalattacks | bnRep | intentionalattacks Bayesian Network | bn.fit | | |
| inverters | bnRep | inverters Bayesian Network | bn.fit | | |
| knowledge | bnRep | knowledge Bayesian Network | bn.fit | | |
| kosterhavet | bnRep | kosterhavet Bayesian Network | bn.fit | | |
| lawschool | bnRep | lawschool Bayesian Network | bn.fit | | |
| lexical | bnRep | lexical Bayesian Network | bn.fit | | |
| lidar | bnRep | lidar Bayesian Network | bn.fit | | |
| liquefaction | bnRep | liquefaction Bayesian Network | bn.fit | | |
| liquidity | bnRep | liquidity Bayesian Network | bn.fit | | |
| lithium | bnRep | lithium Bayesian Network | bn.fit | | |
| macrophytes | bnRep | macrophytes Bayesian Network | bn.fit | | |
| medicaltest | bnRep | medicaltest Bayesian Network | bn.fit | | |
| megacities | bnRep | megacities Bayesian Network | bn.fit | | |
| metal | bnRep | metal Bayesian Network | bn.fit | | |
| moodstate | bnRep | moodstate Bayesian Network | bn.fit | | |
| mountaingoat | bnRep | mountaingoat Bayesian Network | bn.fit | | |
| nanomaterials1 | bnRep | nanomaterial Bayesian Networks | bn.fit | | |
| nanomaterials2 | bnRep | nanomaterial Bayesian Networks | bn.fit | | |
| navigation | bnRep | navigation Bayesian Network | bn.fit | | |
| nuclearwaste | bnRep | nuclearwaste Bayesian Network | bn.fit | | |
| nuisancegrowth | bnRep | nuisancegrowth Bayesian Network | bn.fit | | |
| oildepot | bnRep | oildepot Bayesian Network | bn.fit | | |
| onlinerisk | bnRep | onlinerisk Bayesian Network | bn.fit | | |
| orbital | bnRep | orbital Bayesian Network | bn.fit | | |
| oxygen | bnRep | oxygen Bayesian Network | bn.fit | | |
| parkinson | bnRep | parkinson Bayesian Network | bn.fit | | |
| perioperative | bnRep | perioperative Bayesian Network | bn.fit | | |
| permaBN | bnRep | permaBN Bayesian Network | bn.fit | | |
| phdarticles | bnRep | phdarticles Bayesian Network | bn.fit | | |
| pilot | bnRep | pilot Bayesian Network | bn.fit | | |
| pneumonia | bnRep | pneumonia Bayesian Network | bn.fit | | |
| polymorphic | bnRep | polymorphic Bayesian Network | bn.fit | | |
| poultry | bnRep | poultry Bayesian Network | bn.fit | | |
| project | bnRep | project Bayesian Network | bn.fit | | |
| projectmanagement | bnRep | projectmanagement Bayesian Network | bn.fit | | |
| propellant | bnRep | propellant Bayesian Network | bn.fit | | |
| rainstorm | bnRep | rainstorm Bayesian Network | bn.fit | | |
| rainwater | bnRep | rainwater Bayesian Network | bn.fit | | |
| realestate1 | bnRep | realestate Bayesian Networks | bn.fit | | |
| realestate2 | bnRep | realestate Bayesian Networks | bn.fit | | |
| realestate3 | bnRep | realestate Bayesian Networks | bn.fit | | |
| redmeat | bnRep | redmeat Bayesian Network | bn.fit | | |
| resilience | bnRep | resilience Bayesian Network | bn.fit | | |
| ricci | bnRep | ricci Bayesian Network | bn.fit | | |
| rockburst | bnRep | rockburst Bayesian Network | bn.fit | | |
| rockquality | bnRep | rockquality Bayesian Network | bn.fit | | |
| ropesegment | bnRep | ropesegment Bayesian Network | bn.fit | | |
| safespeeds | bnRep | safespeeds Bayesian Network | bn.fit | | |
| sallyclark | bnRep | sallyclark Bayesian Network | bn.fit | | |
| salmonella1 | bnRep | salmonella Bayesian Networks | bn.fit | | |
| salmonella2 | bnRep | salmonella Bayesian Networks | bn.fit | | |
| seismic | bnRep | seismic Bayesian Network | bn.fit | | |
| shipping | bnRep | shipping Bayesian Network | bn.fit | | |
| simulation | bnRep | simulation Bayesian Network | bn.fit | | |
| softwarelogs1 | bnRep | softwarelogs Bayesian Networks | bn.fit | | |
| softwarelogs2 | bnRep | softwarelogs Bayesian Networks | bn.fit | | |
| softwarelogs3 | bnRep | softwarelogs Bayesian Networks | bn.fit | | |
| softwarelogs4 | bnRep | softwarelogs Bayesian Networks | bn.fit | | |
| soil | bnRep | soil Bayesian Network | bn.fit | | |
| soillead | bnRep | soillead Bayesian Network | bn.fit | | |
| soilliquefaction1 | bnRep | soilliquefaction Bayesian Networks | bn.fit | | |
| soilliquefaction2 | bnRep | soilliquefaction Bayesian Networks | bn.fit | | |
| soilliquefaction3 | bnRep | soilliquefaction Bayesian Networks | bn.fit | | |
| soilliquefaction4 | bnRep | soilliquefaction Bayesian Networks | bn.fit | | |
| stocks | bnRep | stocks Bayesian Network | bn.fit | | |
| student1 | bnRep | student Bayesian Networks | bn.fit | | |
| student2 | bnRep | student Bayesian Networks | bn.fit | | |
| suffocation | bnRep | suffocation Bayesian Network | bn.fit | | |
| tastingtea | bnRep | tastingtea Bayesian Network | bn.fit | | |
| tbm | bnRep | tbm Bayesian Network | bn.fit | | |
| theft1 | bnRep | theft Bayesian Networks | bn.fit | | |
| theft2 | bnRep | theft Bayesian Networks | bn.fit | | |
| titanic | bnRep | titanic Bayesian Network | bn.fit | | |
| trajectories | bnRep | trajectories Bayesian Network | bn.fit | | |
| transport | bnRep | transport Bayesian Network | bn.fit | | |
| tubercolosis | bnRep | tubercolosis Bayesian Network | bn.fit | | |
| turbine1 | bnRep | turbine Bayesian Networks | bn.fit | | |
| turbine2 | bnRep | turbine Bayesian Networks | bn.fit | | |
| twinframework | bnRep | twinframework Bayesian Network | bn.fit | | |
| urinary | bnRep | urinary Bayesian Network | bn.fit | | |
| vaccine | bnRep | vaccine Bayesian Network | bn.fit | | |
| vessel1 | bnRep | vessel Bayesian Networks | bn.fit | | |
| vessel2 | bnRep | vessel Bayesian Networks | bn.fit | | |
| volleyball | bnRep | volleyball Bayesian Networks | bn.fit | | |
| waterlead | bnRep | waterlead Bayesian Network | bn.tan | | |
| wheat | bnRep | wheat Bayesian Network | bn.fit | | |
| windturbine | bnRep | windturbine Bayesian Network | bn.fit | | |
| witness | bnRep | witness Bayesian Network | bn.fit | | |
| yangtze | bnRep | yangtze Bayesian Network | bn.fit | | |
| example_prices | finlabR | Example synthetic price dataset | data.frame | 1000 | 7 |
| breakpoints | animint2 | The breakpointError of simulated signals | list | | |
| change | animint2 | Graphical model structure change | list | | |
| ChromHMMiterations | animint2 | ChromHMM iterations | list | | |
| climate | animint2 | Climate data in central America from 1995 to 2000 | data.frame | 41472 | 16 |
| compare | animint2 | Testing rank and compare SVM on simulated patterns | list | | |
| diamonds | animint2 | Prices of 50,000 round cut diamonds | tbl_df | 53940 | 10 |
| economics | animint2 | US economic time series. | tbl_df | 574 | 6 |
| economics_long | animint2 | US economic time series. | data.frame | 2870 | 4 |
| faithfuld | animint2 | 2d density estimate of Old Faithful data | tbl_df | 5625 | 3 |
| FluView | animint2 | FluView | list | | |
| FunctionalPruning | animint2 | Functional Pruning Algorithm | list | | |
| generation.loci | animint2 | Evolution simulation | data.frame | 120000 | 6 |
| intreg | animint2 | Interval regression | list | | |
| luv_colours | animint2 | 'colors()' in Luv space. | data.frame | 657 | 4 |
| malaria | animint2 | Malaria parasite genome variants | list | | |
| midwest | animint2 | Midwest demographics. | tbl_df | 437 | 28 |
| mixtureKNN | animint2 | K-Nearest-Neighbors model of the mixture example data | list | | |
| montreal.bikes | animint2 | Montreal bikes | list | | |
| mpg | animint2 | Fuel economy data from 1999 and 2008 for 38 popular models of car | tbl_df | 234 | 11 |
| msleep | animint2 | An updated and expanded version of the mammals sleep dataset. | tbl_df | 83 | 11 |
| parallelPeaks | animint2 | | data.table | 584 | 12 |
| PeakConsistency | animint2 | Consistency of segmentation models in simulated Poisson peaks | list | | |
| pirates | animint2 | Worldwide pirate attacks from 1978 to 2013 | data.frame | 6636 | 14 |
| presidential | animint2 | Terms of 11 presidents from Eisenhower to Obama. | tbl_df | 11 | 4 |
| prior | animint2 | Class prior change | list | | |
| prostateLasso | animint2 | Lasso model of the prostate cancer data set | list | | |
| seals | animint2 | Vector field of seal movements. | tbl_df | 1155 | 4 |
| TestROC | animint2 | Test ROC curves | list | | |
| txhousing | animint2 | Housing sales in TX. | tbl_df | 8602 | 9 |
| UStornadoes | animint2 | Tornadoes in the United States from 1950 to 2012 | data.frame | 41620 | 32 |
| VariantModels | animint2 | Error rates of supervised learning methods for variant calling | list | | |
| vervet | animint2 | Vervet monkey intestinal microbiome | list | | |
| WorldBank | animint2 | Demographics by country from 1960 to 2012 | data.frame | 11342 | 15 |
| worldPop | animint2 | World population by subcontinent | data.frame | 294 | 4 |
| ipa_symbols | phonetisr | List of IPA symbols | tbl_df | 188 | 15 |
| kl_swadesh | phonetisr | Klingon Swadesh list | tbl_df | 195 | 4 |
| lidar | VarReg | lidar dataset. | data.frame | 221 | 2 |
| mcycle | VarReg | mcycle dataset. | data.frame | 133 | 2 |
| vcf | VarReg | vcf dataset. | data.frame | 100 | 3 |
| unicorn_data | csucistats | Unicorn Data | data.frame | 750 | 15 |
| Acetylene | genridge | Acetylene Data | data.frame | 16 | 4 |
| Detroit | genridge | Detroit Homicide Data for 1961-1973 | data.frame | 13 | 14 |
| diab | genridge | Diabetes Progression | data.frame | 442 | 11 |
| Manpower | genridge | Hospital manpower data | data.frame | 17 | 6 |
| prostate | genridge | Prostate Cancer Data | data.frame | 97 | 10 |
| ebola | SimBIID | Time series counts of ebola cases | data.frame | 192 | 3 |
| smallpox | SimBIID | Time series counts of smallpox cases | data.frame | 23 | 2 |
| df_curve_reach_freq | Robyn | Robyn Dataset: Reach & frequency simulated dataset | data.frame | 300 | 3 |
| dt_prophet_holidays | Robyn | Robyn Dataset: Holidays by Country | tbl_df | 87651 | 4 |
| dt_simulated_weekly | Robyn | Robyn Dataset: MMM Demo Data | tbl_df | 208 | 12 |
| lip | saeeb | Lip Cancer in Scotland | tbl_df | 56 | 5 |
| ipi_c_eu | ggdemetra | Industrial Production Indices in manufacturing in the European Union | mts | 360 | 34 |
| ipi_c_eu_df | ggdemetra | Industrial Production Indices in manufacturing in the European Union | data.frame | 360 | 35 |
| data_prospect5 | ccrtm | refractive index and specific absorption coefficients for PROSPECT 5 | matrix | 2101 | 7 |
| data_prospectd | ccrtm | refractive index and specific absorption coefficients for PROSPECT D | matrix | 2101 | 8 |
| soil | ccrtm | soil reflectance | matrix | 2101 | 2 |
| solar | ccrtm | direct and diffuse light | matrix | 2101 | 2 |
| act_pattern | musclesyneRgies | Single activation pattern example (30 cycles) | data.frame | 6000 | 2 |
| act_patterns | musclesyneRgies | All activation patterns of one synergy example (30 cycles) | musclesyneRgies | | |
| FILT_EMG | musclesyneRgies | Filtered EMG example | list | | |
| RAW_DATA | musclesyneRgies | Raw EMG example | list | | |
| SYNS | musclesyneRgies | Muscle synergies example | list | | |
| WeatherVienna | ShapleyOutlier | Weather data from Vienna | data.frame | 1804 | 25 |
| barleyh20 | pbANOVA | barleyh20 data | data.frame | 112 | 6 |
| fedata | pbANOVA | fedata data | data.frame | 20 | 2 |
| potato | pbANOVA | potato data | data.frame | 75 | 5 |
| icuData | givitiR | Information of SAPS II score and outcome of 1,000 ICU patients. | data.frame | 1000 | 33 |
| design | TcGSA | Simulated Data for TcGSA | data.frame | 50 | 5 |
| expr_1grp | TcGSA | Simulated Data for TcGSA | data.frame | 250 | 50 |
| expr_2grp | TcGSA | Simulated Data for TcGSA | data.frame | 250 | 50 |
| gmt_sim | TcGSA | Simulated Data for TcGSA | GSA.genesets | | |
| exam | inferr | Dummy data set for Cochran's Q test | data.frame | 15 | 3 |
| hsb | inferr | High School and Beyond Data Set | data.frame | 200 | 11 |
| treatment | inferr | Dummy data set for 2 Sample Proportion test | data.frame | 50 | 2 |
| treatment2 | inferr | Dummy data set for 2 Sample Proportion test | data.frame | 200 | 2 |
| stock04 | MRCE | log-returns of 9 stocks from 2004 | matrix | 52 | 9 |
| milkchoc | MultiResponseR | Check-All-That-Apply data | data.frame | 280 | 8 |
| H.2 | propagate | Datasets from the GUM "Guide to the expression of uncertainties in measurement" (2008) | data.frame | 5 | 3 |
| H.3 | propagate | Datasets from the GUM "Guide to the expression of uncertainties in measurement" (2008) | data.frame | 11 | 2 |
| H.4 | propagate | Datasets from the GUM "Guide to the expression of uncertainties in measurement" (2008) | data.frame | 6 | 6 |
| sdata | ILRCM | Simulated Irregular Longitudinal Data | data.frame | 8631 | 3 |
| smocc | ILRCM | SMOCC Data | data.frame | 1942 | 7 |
| toydata | CHMM | Toy example - observations for 5 correlated samples. | matrix | 1000 | 5 |
| toystatus | CHMM | Toy example - status for 5 correlated samples. | matrix | 1000 | 5 |
| PDII | influence.SEM | Industrialization and Democracy indicators. | data.frame | 75 | 11 |
| Q | influence.SEM | Simulated data set. | data.frame | 919 | 10 |
| concentrationcycles | transmem | Lithium concentration results using a membrane | list | | |
| curvelithium | transmem | External standard calibration curve for lithium in water. | data.frame | 8 | 2 |
| planelithium | transmem | Bivariated calibration plane for lithium in prescence of sodium. | data.frame | 40 | 3 |
| reusecycles | transmem | Membrane reuse capability to transport lithium | list | | |
| seawaterLiNaK | transmem | Lithium, sodium and potassium transport profiles across a membrane | list | | |
| community | traitstrap | Community data | tbl_df | 110 | 4 |
| trait | traitstrap | Trait data | tbl_df | 705 | 6 |
| UCI.BCD.Wisconsin | simpleNeural | Breast Cancer Wisconsin (Diagnostic) Data Set | data.frame | 569 | 32 |
| UCI.ISOLET.ABC | simpleNeural | ISOLET Data Set (ABC) | data.frame | 900 | 618 |
| UCI.transfusion | simpleNeural | Blood Transfusion Service Center Data Set | data.frame | 748 | 5 |
| ge_macro_trial01 | beeca | Output from the Ge et al (2011) SAS macro applied to the trial01 dataset | tbl_df | 1 | 6 |
| margins_trial01 | beeca | Output from the Margins SAS macro applied to the trial01 dataset | tbl_df | 1 | 11 |
| trial01 | beeca | Example trial dataset 01 | tbl_df | 268 | 9 |
| trial02_cdisc | beeca | Example CDISC Clinical Trial Dataset in ADaM Format | tbl_df | 254 | 13 |
| psydata | sgr | Data set | data.frame | 744 | 22 |
| smokers | sgr | Data set | data.frame | 96 | 4 |
| creatinine_data | labNorm | Example values of Hemoglobin and Creatinine | data.frame | 1000 | 3 |
| hemoglobin_data | labNorm | Example values of Hemoglobin and Creatinine | data.frame | 1000 | 3 |
| LAB_DETAILS | labNorm | Available lab names | data.frame | 93 | 8 |
| natlongsurv | kutils | Smoking, Happiness, and other survey responses | data.frame | 2867 | 29 |
| data_category | vegawidget | Example dataset: Categorical data | tbl_df | 10 | 2 |
| data_seattle_daily | vegawidget | Example dataset: Seattle daily weather | spec_tbl_df | 1461 | 6 |
| data_seattle_hourly | vegawidget | Example dataset: Seattle hourly temperatures | tbl_df | 8759 | 2 |
| spec_mtcars | vegawidget | Example vegaspec: mtcars scatterplot | vegaspec_unit | | |
| shp_df | ggswissmaps | A list with 8 data frames of swiss territory boundaries, at various levels. | list | | |
| shp_sf | ggswissmaps | A list with 8 'sf' objects of swiss territory boundaries, at various levels. | list | | |
| latesummer | rLakeAnalyzer | Late Summer Profile | data.frame | 881 | 6 |
| crps | extremeIndex | Observations of 6-h rainfall amount with CRPS values of 3 calibrated ensemble forecasts for one lead time across France. | matrix | 112221 | 4 |
| MACE | viscomp | Major Adverse Cardiovascular Event | data.frame | 22 | 13 |
| nmaMACE | viscomp | Network Meta-Analysis of Major Adverse Cardiovascular Event | netmeta | | |
| bodyfat_Aeolus | PLreg | Body Fat of Little Brown Bat | data.frame | 159 | 4 |
| Firm | PLreg | Firm Cost | data.frame | 73 | 7 |
| PeruVotes | PLreg | Peru Blank Votes | data.frame | 194 | 2 |
| default_exif_tags | filenamr | Default EXIF, XMP-dc, and IPTC tags | character | | |
| default_exif_xwalk | filenamr | EXIF data column name crosswalk | list | | |
| miss | mtsdi | Sample Dataset | data.frame | 24 | 5 |
| legumesIds | geneHummus | NCBI taxonomy ids for the legume family | numeric | | |
| my_legumes | geneHummus | ARF proteins per legume specie | list | | |
| IllumBeta | RPMM | DNA Methylation Data for Normal Tissue Types | matrix | 217 | 100 |
| tissue | RPMM | DNA Methylation Data for Normal Tissue Types | factor | | |
| juncus | mixchar | Thermogravimetric data for Juncus amabilis | data.frame | 46080 | 2 |
| marsilea | mixchar | Thermogravimetric data for Marsilea drumondii | data.frame | 46080 | 2 |
| speech_list | komment | speech list data | tbl_df | 7173 | 6 |
| hkagepop19 | hkdatasets | Land-based non-institutional population by District Council district and age group | tbl_df | 18 | 8 |
| hkdc | hkdatasets | Dataset with public domain information on Hong Kong District Councillors (elected 2019). | tbl_df | 452 | 33 |
| hkdistrict_summary | hkdatasets | Dataset summarising the labels and regions of Hong Kong's Districts | tbl_df | 18 | 6 |
| hkstreetnames20 | hkdatasets | Dataset with Hong Kong Street Names as at 2020. | tbl_df | 4603 | 21 |
| chicago | GeneralOaxaca | Labor market and demographic data for employed Hispanic workers in metropolitan Chicago | data.frame | 712 | 9 |
| albertinedisparue | proustr | Marcel Proust's novel "Albertine disparue" | tbl_df | 259 | 4 |
| alombredesjeunesfillesenfleurs | proustr | Marcel Proust's novel "À l’ombre des jeunes filles en fleurs" | tbl_df | 792 | 4 |
| ducotedechezswann | proustr | Marcel Proust's novel "Du côté de chez Swann" | tbl_df | 1004 | 4 |
| laprisonniere | proustr | Marcel Proust's novel "La Prisonnière" | tbl_df | 365 | 4 |
| lecotedeguermantes | proustr | Marcel Proust's novel "Le côté de Guermantes" | tbl_df | 1610 | 4 |
| letempretrouve | proustr | Marcel Proust's novel "Le temps retrouvé" | tbl_df | 248 | 4 |
| proust_char | proustr | Characters from "À la recherche du temps perdu" | tbl_df | 461 | 1 |
| sodomeetgomorrhe | proustr | Marcel Proust's novel "Sodome et Gomorrhe" | tbl_df | 412 | 4 |
| stop_words | proustr | Stopwords | tbl_df | 689 | 1 |
| ca1d.erlotinib | fracprolif | CA1d cells in 16 micromolar erlotinib | data.frame | 163 | 8 |
| ca1d.erlotinib.totals | fracprolif | CA1d cell counts in various concentrations erlotinib | data.frame | 73 | 11 |
| lplex | beadplexr | LEGENDplex example data | list | | |
| simplex | beadplexr | Simulated beadplex data | list | | |
| ts_set | chopper | Tech Stock Time Series Dataset | data.frame | 2095 | 5 |
| input_formats | oncmap | Adherence input format definitions | data.frame | 10 | 10 |
| regimens | oncmap | Adherence regimens definitions | data.frame | 5 | 6 |
| ElkinAB | ARTool | Synthetic 2x2 Within-Subjects Experiment | data.frame | 32 | 4 |
| ElkinABC | ARTool | Synthetic 2x2x2 Within-Subjects Experiment | data.frame | 64 | 5 |
| Higgins1990Table1 | ARTool | Synthetic 3x3 Factorial Randomized Experiment | data.frame | 36 | 4 |
| Higgins1990Table1.art | ARTool | Aligned Rank Transformed Version of Higgins1990Table1 | data.frame | 36 | 10 |
| Higgins1990Table5 | ARTool | Split-plot Experiment Examining Effect of Moisture and Fertilizer on Dry Matter in Peat Pots | data.frame | 48 | 4 |
| Higgins1990Table5.art | ARTool | Aligned Rank Transformed Version of Higgins1990Table5 | data.frame | 48 | 10 |
| HigginsABC | ARTool | Synthetic 2x2x2 Mixed Design Experiment | data.frame | 16 | 5 |
| HigginsABC.art | ARTool | Aligned Rank Transformed Version of HigginsABC | data.frame | 16 | 19 |
| InteractionTestData | ARTool | Synthetic Data Used in the Contrast Test Vignette | data.frame | 300 | 4 |
| admissions | MAPA | Total Non-elective G&A Admissions (FFCEs) | ts | 62 | 1 |
| FISH | shapeR | An example data file | data.frame | 240 | 18 |
| shape | shapeR | An example shapeR instance including 160 images. | shapeR | | |
| shape | shapeR | An example shapeR instance including 160 images. | shapeR | | |
| geocod_base | utilsIPEA | Brazilian address | data.table | 5 | 5 |
| patients | shinybody | Example data set of patients | data.frame | 16 | 5 |
| shinybody_organs | shinybody | Organs available in shinybody | data.frame | 79 | 7 |
| tumors | shinybody | Example data set of tumors | data.frame | 39 | 5 |
| cuperdec_database_ex | cuperdec | Example isolation source database input for cuperdec | spec_tbl_df | 569 | 2 |
| cuperdec_metadata_ex | cuperdec | Example metadata file input for cuperdec | spec_tbl_df | 229 | 33 |
| cuperdec_taxatable_ex | cuperdec | Example taxon table input for cuperdec | spec_tbl_df | 3401 | 230 |
| SSRPexact | BayesRep | Data from the Social Sciences Replication Project | data.frame | 21 | 16 |
| groupS | ed50 | A Real Experiment Dose Data | data.frame | 36 | 2 |
| groupSN | ed50 | A Real Experiment Dose Data | data.frame | 38 | 2 |
| gTableOrigin | ed50 | G Table | tbl_df | 49 | 3 |
| ped.mrode | AGHmatrix | Pedigree Data | data.frame | 6 | 3 |
| ped.sol | AGHmatrix | Pedigree data for autopolyploid examples | data.frame | 1138 | 3 |
| snp.pine | AGHmatrix | Molecular data for diploid examples | matrix | 926 | 4853 |
| snp.sol | AGHmatrix | Molecular data for autopolyploid examples | matrix | 571 | 3895 |
| tehtuner_example | tehtuner | Simulated example data | data.frame | 1000 | 12 |
| pressure_volume_data | pvcurveanalysis | Pressure volume curve data | data.frame | 160 | 8 |
| Data_Maize | vmdTDNN | Monthly International Maize Price Data | ts | 126 | 1 |
| dark2 | campfin | Dark Color Palette | character | | |
| extra_city | campfin | Additional US City Names | character | | |
| invalid_city | campfin | Invalid City Names | character | | |
| rx_phone | campfin | Phone number regex | character | | |
| rx_state | campfin | State regex | glue | | |
| rx_url | campfin | URL regex | character | | |
| rx_zip | campfin | ZIP code regex | character | | |
| usps_city | campfin | USPS City Abbreviations | tbl_df | 83 | 2 |
| usps_state | campfin | USPS State Abbreviations | tbl_df | 62 | 2 |
| usps_street | campfin | USPS Street Abbreviations | tbl_df | 368 | 2 |
| valid_abb | campfin | US State Abbreviations | character | | |
| valid_city | campfin | US City Names | character | | |
| valid_name | campfin | US State Names | character | | |
| valid_state | campfin | US State Abbreviations | character | | |
| valid_zip | campfin | Almost all of the valid USA ZIP Codes | character | | |
| zipcodes | campfin | US City, state, and ZIP | tbl_df | 44336 | 3 |
| SBM_net | CommKern | Simulated functional and structural connectivity with nested hierarchical community structure | spinglass_net | | |
| simasd_array | CommKern | Simulated Array | array | | |
| simasd_comm_df | CommKern | Simulated partitions of nodes to communities from HMS algorithm | data.frame | 80 | 49 |
| simasd_covars | CommKern | Simulated demographics dataset modeled of a subset of the preprocessed ABIDE database | data.frame | 49 | 8 |
| simasd_hamil_df | CommKern | Simulated Hamiltonian values from HMS algorithm | data.frame | 49 | 2 |
| MisLinks | d3Network | A data file of links from Knuth's Les Miserables characters data base. | data.frame | 254 | 3 |
| MisNodes | d3Network | A data file of nodes from Knuth's Les Miserables characters data base. | data.frame | 77 | 2 |
| AdultsNoID | PopulateR | Non-partnered synthetic people | data.frame | 2213 | 5 |
| AllEmployers | PopulateR | Employers and employees, by industry | data.frame | 183 | 7 |
| BadRels | PopulateR | Synthetic people restricted to an age range | data.frame | 7568 | 8 |
| EmployerSet | PopulateR | Synthetic employers and their employee counts | data.frame | 225 | 3 |
| GroupInfo | PopulateR | The proportion of people in a relationship, by age band within sex | data.frame | 14 | 7 |
| InitialDataframe | PopulateR | People in age groups, in the Timaru District | data.frame | 46293 | 6 |
| IntoSchools | PopulateR | Four person households, with a school status for each person | data.frame | 980 | 8 |
| LeftSchool | PopulateR | School leavers | data.frame | 120 | 4 |
| NetworkMatrix | PopulateR | The number of contacts for 5000 person | integer | | |
| Ppl4networks | PopulateR | Synthetic people living in the Timaru District | data.frame | 1000 | 5 |
| RegionalStructure | PopulateR | Sex/Age pyramid for teenagers in the Canterbury Region | data.frame | 14 | 4 |
| SchoolsToUse | PopulateR | Schools and their roll counts | data.frame | 266 | 5 |
| SingleAges | PopulateR | Sex/Age pyramid data for Timaru District | data.frame | 190 | 4 |
| Township | PopulateR | Simulated township | data.frame | 10000 | 5 |
| WorkingAdolescents | PopulateR | Adolescents with a school status and employment hours | data.frame | 478 | 6 |
| data1042 | bayesvl | | data.frame | 1042 | 41 |
| DKAP1061 | bayesvl | | data.frame | 970 | 188 |
| Legends345 | bayesvl | Legends345 data | data.frame | 345 | 27 |
| STEM5000 | bayesvl | | data.frame | 4018 | 43 |
| Chiapas | rareNMtests | Tree abundance in tropical montane forest plots | data.frame | 224 | 376 |
| components | streetmixr | Streetmix components | list | | |
| illustrations | streetmixr | Streetmix CC-BY-SA illustrations | data.frame | 273 | 8 |
| people | streetmixr | Streetmix CC-BY-SA illustrations of people | data.frame | 36 | 5 |
| fuel | bcscr | Speed and Fuel Efficiency (British Ford Escort) | data.frame | 15 | 2 |
| m111survey | bcscr | MAT 111 Survey | data.frame | 71 | 12 |
| NamePhone | bcscr | Names and Phone Numbers | data.frame | 50 | 2 |
| railtrail | bcscr | Volume of Users of a Rail Trail | data.frame | 90 | 9 |
| powcal | etrm | Historical daily closing prices for 11 calendar year power futures contracts | data.frame | 3253 | 12 |
| powfutures130513 | etrm | Closing prices for power futures contracts at trading date 2013-05-13 | data.frame | 32 | 5 |
| powpriors130513 | etrm | Example priors at trading date 2015-05-13 | data.frame | 3885 | 3 |
| asset | CHNCapitalStock | Assets | data.frame | 2157 | 6 |
| covid19 | SEIRfansy | COVID-19 Cases Time Series in India | data.frame | 236 | 7 |
| crack_growth | dhglm | Crack-growth Data | data.frame | 241 | 5 |
| epilepsy | dhglm | Epilepsy Seizures Data | data.frame | 236 | 7 |
| exData | rKOMICS | Example dataset | list | | |
| matrices | rKOMICS | Example cluster matrices | list | | |
| gaussplot_sample_data | gaussplotR | Sample data set | data.frame | 36 | 11 |
| drugs | C443 | Drug consumption data set | data.frame | 1885 | 32 |
| Admission | coreSim | Graduate school admissions data | data.frame | 400 | 4 |
| stackExample | StackImpute | Example data for Louis_Information() | list | | |
| BigCity | BayesSampling | Full Person-level Population Database | data.frame | 150266 | 12 |
| MEPS14 | glmMisrep | MEPS 2014 Full Year Consolidated Data File | data.frame | 13301 | 7 |
| FIalfalfa | simET | A example dataset of alfalfa under flood irrigation | data.frame | 161 | 22 |
| SDIalfalfa | simET | A example dataset of alfalfa under subsurface drip irrigation | data.frame | 161 | 22 |
| ReliabilityData | REPPlab | Reliability Data from an Industrial Context | data.frame | 520 | 55 |
| helios | solaR | Daily irradiation and ambient temperature from the Helios-IES database | data.frame | 355 | 4 |
| Ktlim | solaR | Markov Transition Matrices for the Aguiar etal. procedure | matrix | 2 | |
| Ktm | solaR | Markov Transition Matrices for the Aguiar etal. procedure | numeric | | |
| MTM | solaR | Markov Transition Matrices for the Aguiar etal. procedure | data.frame | 100 | 10 |
| prodEx | solaR | Productivity of a set of PV systems of a PV plant. | zoo | 493 | 22 |
| pumpCoef | solaR | Coefficients of centrifugal pumps. | data.frame | 124 | 13 |
| simulation1 | hosm | Simulation 1 for High Order Spatial Matrix | data.frame | 4 | 5 |
| simulation2 | hosm | Simulation 2 for High Order Spatial Matrix | data.frame | 5 | 6 |
| simulation3 | hosm | Simulation 3 for High Order Spatial Matrix | data.frame | 5 | 6 |
| simulation4 | hosm | Simulation 4 for High Order Spatial Matrix | data.frame | 4 | 5 |
| simulation5 | hosm | Simulation 5 for High Order Spatial Matrix | data.frame | 4 | 5 |
| Leukemia.data | icdGLM | Survival Times of 33 Leukemia Patients | data.frame | 33 | 3 |
| TLI.data | icdGLM | TLI Study of 82 Patients | data.frame | 82 | 4 |
| ECON85 | fPortfolio | Assets Data Sets | data.frame | 304 | 12 |
| ECON85LONG | fPortfolio | Assets Data Sets | data.frame | 304 | 19 |
| GCCINDEX | fPortfolio | Assets Data Sets | timeSeries | 825 | 11 |
| GCCINDEX.RET | fPortfolio | Assets Data Sets | timeSeries | 824 | 11 |
| LPP2005 | fPortfolio | Assets Data Sets | timeSeries | 377 | 9 |
| LPP2005.RET | fPortfolio | Assets Data Sets | timeSeries | 377 | 9 |
| SMALLCAP | fPortfolio | Assets Data Sets | timeSeries | 60 | 22 |
| SMALLCAP.RET | fPortfolio | Assets Data Sets | timeSeries | 60 | 22 |
| SPISECTOR | fPortfolio | Assets Data Sets | timeSeries | 2216 | 10 |
| SPISECTOR.RET | fPortfolio | Assets Data Sets | timeSeries | 2198 | 10 |
| SWX | fPortfolio | Assets Data Sets | timeSeries | 1917 | 6 |
| SWX.RET | fPortfolio | Assets Data Sets | timeSeries | 1916 | 6 |
| ipd | CVThresh | Inductance plethysmography data | rts | | |
| aquaculture | fishstat | Global Aquaculture Production | data.frame | 107419 | 8 |
| area | fishstat | Areas | data.frame | 29 | 5 |
| capture | fishstat | Global Capture Production | data.frame | 1052197 | 7 |
| country | fishstat | Countries | data.frame | 275 | 8 |
| environment | fishstat | Environments | data.frame | 4 | 2 |
| measure | fishstat | Measures | data.frame | 11 | 7 |
| production | fishstat | Global Aquaculture and Capture Production | data.frame | 1159616 | 8 |
| source | fishstat | Sources | data.frame | 4 | 2 |
| species | fishstat | Species | data.frame | 13772 | 10 |
| status | fishstat | Status | data.frame | 16 | 4 |
| Dfexample | regspec | Synthetic Data for Testing Functions in the regspec Package. | ts | | |
| Dpexample2 | regspec | Synthetic Data for Testing Functions in the regspec Package. | numeric | | |
| Dpexample3 | regspec | Synthetic Data for Testing Functions in the regspec Package. | numeric | | |
| retail | regspec | Retail Sales Index (RSI) data | data.frame | 315 | 2 |
| spec.true | regspec | Synthetic Data for Testing Functions in the regspec Package. | matrix | 200 | 2 |
| trav.mly | regspec | Visits abroad by UK residents | matrix | 36 | 3 |
| trav.qly | regspec | Visits abroad by UK residents | matrix | 28 | 3 |
| lizard_areas_connected | urbioconnect | Connected habitat patch areas for Blue-tongued Lizard | patch_size_tbl | 73 | 2 |
| abalone.i | ggInterval | abalone.i data example | data.frame | 24 | 7 |
| blood.i | ggInterval | blood.i data example | tbl_df | 14 | 3 |
| Cardiological | ggInterval | Cardiological data example | symbolic_tbl | 11 | 3 |
| Cardiological2 | ggInterval | Cardiological data example | symbolic_tbl | 15 | 3 |
| Environment | ggInterval | Environment data example | symbolic_tbl | 14 | 17 |
| facedata | ggInterval | Face Data Example | symbolic_tbl | 27 | 6 |
| iris.i | ggInterval | iris.i data example | data.frame | 3 | 4 |
| mtcars.i | ggInterval | mtcars.i data example | symbolic_tbl | 5 | 11 |
| mushroom | ggInterval | mushroom data example | tbl_df | 23 | 3 |
| oils | ggInterval | oils data example | symbolic_tbl | 8 | 4 |
| lisiecki2005 | gsloid | LR04 Global Pliocene-Pleistocene Benthic d18O Stack (5.3-Myr). | data.frame | 2115 | 3 |
| LR04_MISboundaries | gsloid | Marine isotope stages (MIS) boundaries. | data.frame | 232 | 7 |
| spratt2016 | gsloid | Global Sea Level Reconstruction using Stacked Records from 0-800 ka. | data.frame | 799 | 9 |
| alcoholSurv | alcoholSurv | Light Alcohol Consumption and Survival | data.frame | 5650 | 21 |
| beans | beans | Dry beans | tbl_df | 13611 | 17 |
| global_regions | sdmtools | Global regions | long_tibble | 249 | 6 |
| raster_to_terra | sdmtools | 'raster' to 'terra' equivalence table | long_tibble | 42 | 3 |
| worldclim_bio_names | sdmtools | WorldClim '"bio"' names | character | | |
| ArkansasRiver | GeneralizedHyperbolic | Soil Electrical Conductivity | list | | |
| ghypLargeParam | GeneralizedHyperbolic | Parameter Sets for the Generalized Hyperbolic Distribution | matrix | 1440 | |
| ghypLargeShape | GeneralizedHyperbolic | Parameter Sets for the Generalized Hyperbolic Distribution | matrix | 90 | |
| ghypSmallParam | GeneralizedHyperbolic | Parameter Sets for the Generalized Hyperbolic Distribution | matrix | 84 | |
| ghypSmallShape | GeneralizedHyperbolic | Parameter Sets for the Generalized Hyperbolic Distribution | matrix | 21 | |
| gigLargeParam | GeneralizedHyperbolic | Parameter Sets for the Generalized Inverse Gaussian Distribution | matrix | 1100 | |
| gigSmallParam | GeneralizedHyperbolic | Parameter Sets for the Generalized Inverse Gaussian Distribution | matrix | 125 | |
| hyperbLargeParam | GeneralizedHyperbolic | Parameter Sets for the Hyperbolic Distribution | matrix | 240 | |
| hyperbLargeShape | GeneralizedHyperbolic | Parameter Sets for the Hyperbolic Distribution | matrix | 15 | |
| hyperbSmallParam | GeneralizedHyperbolic | Parameter Sets for the Hyperbolic Distribution | matrix | 28 | |
| hyperbSmallShape | GeneralizedHyperbolic | Parameter Sets for the Hyperbolic Distribution | matrix | 7 | |
| mamquam | GeneralizedHyperbolic | Size of Gravels from Mamquam River | data.frame | 16 | 2 |
| nervePulse | GeneralizedHyperbolic | Intervals Between Pulses Along a Nerve Fibre | numeric | | |
| nigLargeParam | GeneralizedHyperbolic | Parameter Sets for the Normal Inverse Gaussian Distribution | matrix | 240 | |
| nigLargeShape | GeneralizedHyperbolic | Parameter Sets for the Normal Inverse Gaussian Distribution | matrix | 15 | |
| nigSmallParam | GeneralizedHyperbolic | Parameter Sets for the Normal Inverse Gaussian Distribution | matrix | 28 | |
| nigSmallShape | GeneralizedHyperbolic | Parameter Sets for the Normal Inverse Gaussian Distribution | matrix | 7 | |
| resistors | GeneralizedHyperbolic | Resistance of One-half-ohm Resistors | data.frame | 28 | 2 |
| SandP500 | GeneralizedHyperbolic | S&P 500 | numeric | | |
| traffic | GeneralizedHyperbolic | Intervals Between Vehicles on a Road | numeric | | |
| ais | SkewHyperbolic | Australian Institute of Sport data | data.frame | 202 | 13 |
| lrdji | SkewHyperbolic | Dow Jones Log Return Data | matrix | 1132 | 1 |
| lrnokeur | SkewHyperbolic | Log Returns of the NOK/EUR Exchange Rate | matrix | 1647 | 1 |
| skewhypLargeParam | SkewHyperbolic | Parameter Sets for the Skew Hyperbolic t-Distribution | data.frame | 400 | 4 |
| skewhypLargeShape | SkewHyperbolic | Parameter Sets for the Skew Hyperbolic t-Distribution | data.frame | 25 | 4 |
| skewhypSmallParam | SkewHyperbolic | Parameter Sets for the Skew Hyperbolic t-Distribution | data.frame | 16 | 4 |
| skewhypSmallShape | SkewHyperbolic | Parameter Sets for the Skew Hyperbolic t-Distribution | data.frame | 4 | 4 |
| sap | metabodecon | The SAP Dataset | spectra | | |
| sim | metabodecon | The Sim Dataset | spectra | | |
| diabetes | dtGAP | Diabetes patient records. | data.frame | 768 | 9 |
| galaxy | dtGAP | Galaxy dataset for regression. | spec_tbl_df | 323 | 5 |
| penguins | dtGAP | Data of three different species of penguins. | data.frame | 344 | 7 |
| Psychosis_Disorder | dtGAP | Psychosis Disorder Data | data.frame | 95 | 51 |
| test_covid | dtGAP | External test dataset. Medical information of Wuhan patients collected between 2020-01-10 and 2020-02-18. | tbl_df | 110 | 4 |
| train_covid | dtGAP | Training dataset. Medical information of Wuhan patients collected between 2020-01-10 and 2020-02-18. Containing NAs. | tbl_df | 351 | 4 |
| wine | dtGAP | Results of a chemical analysis of wines grown in a specific area of Italy. | data.frame | 178 | 14 |
| wine_quality_red | dtGAP | Red variant of the Portuguese "Vinho Verde" wine. | spec_tbl_df | 1599 | 12 |
| kb_experience | katilingban | Katilingban consultants' experience list | tbl_df | 141 | 12 |
| run | trackeR | Training session. | trackeRdata | | |
| runs | trackeR | Training sessions. | trackeRdata | | |
| alpe_d_huez | learningr | Alpe d'Huez | data.frame | 36 | 7 |
| alpe_d_huez2 | learningr | Alpe d'Huez | data.frame | 36 | 7 |
| crab_tag | learningr | Crab tag | list | | |
| deer_endocranial_volume | learningr | Deer Endocranial Volume | data.frame | 33 | 8 |
| english_monarchs | learningr | English Monarchs | data.frame | 259 | 5 |
| gonorrhoea | learningr | Gonorrhoea | data.frame | 600 | 5 |
| hafu | learningr | Hafu | data.frame | 296 | 9 |
| hafu2 | learningr | Hafu | data.frame | 296 | 11 |
| obama_vs_mccain | learningr | Obama vs. McCain | data.frame | 51 | 15 |
| rainfall | extrememix | Monthly Maxima Daily Rainfall in Madrid | numeric | | |
| rainfall_ggpd | extrememix | Rainfall FGGPD Output | ggpd | | |
| rainfall_mgpd | extrememix | Rainfall FMGPD Output | mgpd | | |
| coordDT | SpICE | Data with location information asociated to ice curves values | tbl_df | 1000 | 4 |
| curveDT | SpICE | Data with ICE curve information | grouped_df | 30000 | 5 |
| dataNORM01 | trajeR | Simulated CNORM Dataset (data01) | matrix | 300 | 16 |
| cora | cora | CORA data set | data.frame | 1879 | 16 |
| cora_gold | cora | Cora Gold | data.frame | 64578 | 2 |
| cora_gold_update | cora | Cora Gold Update | data.frame | 1879 | 2 |
| fit_odeGUTS | odeGUTS | Model calibration results for a GUTS-SD theoretical species exposed to a compound. | survFit | | |
| aa | ra4bayesmeta | Auricular acupuncture data | data.frame | 4 | 7 |
| aom | ra4bayesmeta | Acute otitis media data | data.frame | 5 | 7 |
| rti | ra4bayesmeta | Respiratory tract infections data | data.frame | 22 | 7 |
| canyon_wren | dynaSpec | Acoustic recording of a _Catherpes mexicanus_ (canyon wren) song. | Wave | | |
| item5fr | pln | 5-item Test Data Set | data.frame | 67 | 6 |
| item9cat5 | pln | 9 Item Test Data Set | data.frame | 500 | 9 |
| C10E14 | AMCP | The data used in Chapter 10, Exercise 14 | data.frame | 63 | 4 |
| C10E7 | AMCP | The data used in Chapter 10, Exercise 7 | data.frame | 45 | 3 |
| C10E9 | AMCP | The data used in Chapter 10, Exercise 9 | data.frame | 72 | 4 |
| C10T5 | AMCP | The data used in Chapter 10, Table 5 | data.frame | 40 | 3 |
| C10T9 | AMCP | The data used in Chapter 10, Table 9 | data.frame | 24 | 3 |
| C11E17 | AMCP | The data used in Chapter 11, Exercise 17 | data.frame | 14 | 4 |
| C11E18 | AMCP | The data used in Chapter 11, Exercise 18 | data.frame | 12 | 3 |
| C11E19 | AMCP | The data used in Chapter 11, Exercise 19 | data.frame | 14 | 4 |
| C11E21 | AMCP | The data used in Chapter 11, Exercise 21 | data.frame | 42 | 3 |
| C11E22 | AMCP | The data used in Chapter 11, Exercise 22 | data.frame | 19 | 7 |
| C11E23 | AMCP | The data used in Chapter 11, Exercise 23 | data.frame | 183 | 3 |
| C11E24 | AMCP | The data used in Chapter 11, Exercise 24 | data.frame | 90 | 3 |
| C11E3 | AMCP | The data used in Chapter 11, Exercise 3 | data.frame | 5 | 4 |
| C11E5 | AMCP | The data used in Chapter 11, Exercise 5 | data.frame | 5 | 3 |
| C11T1 | AMCP | The data used in Chapter 11, Table 1 | data.frame | 6 | 2 |
| C11T19 | AMCP | The data used in Chapter 11, Table 19 | data.frame | 24 | 3 |
| C11T20 | AMCP | The data used in Chapter 11, Table 20 | data.frame | 15 | 3 |
| C11T4 | AMCP | The data used in Chapter 11, Table 4 | data.frame | 10 | 4 |
| C11T5 | AMCP | The data used in Chapter 11, Table 5 | data.frame | 12 | 4 |
| C12E17 | AMCP | The data used in Chapter 12, Exercise 17 | data.frame | 14 | 5 |
| C12E18 | AMCP | The data used in Chapter 12, Exercise 18 | data.frame | 10 | 3 |
| C12E19 | AMCP | The data used in Chapter 12, Exercise 19 | data.frame | 47 | 6 |
| C12E21 | AMCP | The data used in Chapter 12, Exercise 21 | data.frame | 36 | 4 |
| C12E22 | AMCP | The data used in Chapter 12, Exercise 22 | data.frame | 40 | 8 |
| C12E23 | AMCP | The data used in Chapter 12, Exercise 23 | data.frame | 38 | 10 |
| C12E24 | AMCP | The data used in Chapter 12, Exercise 24 | data.frame | 118 | 25 |
| C12E25 | AMCP | The data used in Chapter 12, Exercise 25 | data.frame | 39 | 3 |
| C12E26 | AMCP | The data used in Chapter 12, Exercise 26 | data.frame | 58 | 6 |
| C12E27 | AMCP | The data used in Chapter 12, Exercise 27 | data.frame | 193 | 6 |
| C12E9 | AMCP | The data used in Chapter 12, Exercise 9 | data.frame | 10 | 4 |
| C12T1 | AMCP | The data used in Chapter 12, Table 1 | data.frame | 10 | 6 |
| C12T11 | AMCP | The data used in Chapter 12, Table 11 | data.frame | 10 | 3 |
| C12T15 | AMCP | The data used in Chapter 12, Table 15 | data.frame | 10 | 3 |
| C12T29 | AMCP | The data used in Chapter 12, Table 29 | data.frame | 18 | 5 |
| C12T7 | AMCP | The data used in Chapter 12, Table 7 | data.frame | 10 | 3 |
| C12T9 | AMCP | The data used in Chapter 12, Table 9 | data.frame | 10 | 2 |
| C13E10 | AMCP | The data used in Chapter 13, Exercise 10 | data.frame | 14 | 4 |
| C13E13 | AMCP | The data used in Chapter 13, Exercise 13 | data.frame | 14 | 4 |
| C13E14 | AMCP | The data used in Chapter 13, Exercise 14 | data.frame | 13 | 3 |
| C13E22 | AMCP | The data used in Chapter 13, Exercise 22 | data.frame | 5 | 3 |
| C13E23 | AMCP | The data used in Chapter 13, Exercise 23 | data.frame | 19 | 7 |
| C13E24 | AMCP | The data used in Chapter 13, Exercise 24 | data.frame | 183 | 3 |
| C13E25 | AMCP | The data used in Chapter 13, Exercise 25 | data.frame | 30 | 3 |
| C13E7 | AMCP | The data used in Chapter 13, Exercise 7 | data.frame | 5 | 4 |
| C13T1 | AMCP | The data used in Chapter 13, Table 1 | data.frame | 5 | 2 |
| C13T10 | AMCP | The data used in Chapter 13, Table 10 | data.frame | 8 | 2 |
| C13T2 | AMCP | The data used in Chapter 13, Table 2 | data.frame | 8 | 3 |
| C13T5 | AMCP | The data used in Chapter 13, Table 5 | data.frame | 14 | 4 |
| C13T6 | AMCP | The data used in Chapter 13, Table 6 | data.frame | 12 | 4 |
| C14E10 | AMCP | The data used in Chapter 14, Exercise 10 | data.frame | 10 | 4 |
| C14E14 | AMCP | The data used in Chapter 14, Exercise 14 | data.frame | 30 | 5 |
| C14E15 | AMCP | The data used in Chapter 14, Exercise 15 | data.frame | 10 | 3 |
| C14E21 | AMCP | The data used in Chapter 14, Exercise 21 | data.frame | 14 | 5 |
| C14E22 | AMCP | The data used in Chapter 14, Exercise 22 | data.frame | 47 | 6 |
| C14T1 | AMCP | The data used in Chapter 14, Table 1 | data.frame | 10 | 4 |
| C14T10 | AMCP | The data used in Chapter 14, Table 10 | data.frame | 20 | 4 |
| C14T13 | AMCP | The data used in Chapter 14, Table 13 | data.frame | 300 | 5 |
| C14T3 | AMCP | The data used in Chapter 14, Table 3 | data.frame | 10 | 3 |
| C14T4 | AMCP | The data used in Chapter 14, Table 4 | data.frame | 10 | 6 |
| C14T5 | AMCP | The data used in Chapter 14, Table 5 | data.frame | 10 | 5 |
| C14T7 | AMCP | The data used in Chapter 14, Table 7 | data.frame | 20 | 3 |
| C14T8 | AMCP | The data used in Chapter 14, Table 8 | data.frame | 20 | 3 |
| C14T9 | AMCP | The data used in Chapter 14, Table 9 | data.frame | 20 | 4 |
| C15E16 | AMCP | The data used in Chapter 15, Exercise 16 | data.frame | 14 | 4 |
| C15E17 | AMCP | The data used in Chapter 15, Exercise 17 | data.frame | 56 | 4 |
| C15E18 | AMCP | The data used in Chapter 15, Exercise 18 | data.frame | 24 | 4 |
| C15E18U | AMCP | The data used in Chapter 15, Exercise 18 (Univariate) | data.frame | 72 | 3 |
| C15E19 | AMCP | The data used in Chapter 15, Exercise 19 | data.frame | 24 | 4 |
| C15E19U | AMCP | The data used in Chapter 15, Exercise 19 (Univariate) | data.frame | 72 | 3 |
| C15T1 | AMCP | The data used in Chapter 15, Table 1 | data.frame | 12 | 4 |
| C16E5 | AMCP | The data used in Chapter 16, Exercise 5 | data.frame | 24 | 3 |
| C16E7 | AMCP | The data used in Chapter 16, Exercise 7 | data.frame | 29 | 6 |
| C16E9 | AMCP | The data used in Chapter 16, Exercise 9 | data.frame | 29 | 6 |
| C16T1 | AMCP | The data used in Chapter 16, Table 1 | data.frame | 24 | 3 |
| C16T4 | AMCP | The data used in Chapter 16, Table 4 | data.frame | 29 | 6 |
| C1E18 | AMCP | The data used in Chapter 1, Exercise 18 | data.frame | 4 | 3 |
| C1E19 | AMCP | The data used in Chapter 1, Exercise 19 | data.frame | 30 | 2 |
| C1E21 | AMCP | The data used in Chapter 1, Exercise 21 | data.frame | 12 | 2 |
| C1E22 | AMCP | The data used in Chapter 1, Exercise 22 | data.frame | 15 | 3 |
| C1E23 | AMCP | The data used in Chapter 1, Exercise 23 | data.frame | 12 | 3 |
| C1T1 | AMCP | The data used in Chapter 1, Table 1 | data.frame | 10 | 3 |
| C3E10 | AMCP | The data used in Chapter 3, Exercise 10 | data.frame | 36 | 3 |
| C3E11 | AMCP | The data used in Chapter 3, Exercise 11 | data.frame | 24 | 2 |
| C3E19 | AMCP | The data used in Chapter 3, Exercise 19 | data.frame | 155 | 3 |
| C3E20 | AMCP | The data used in Chapter 3, Exercise 20 | data.frame | 72 | 2 |
| C3E21 | AMCP | The data used in Chapter 3, Exercise 21 | data.frame | 192 | 2 |
| C3E22 | AMCP | The data used in Chapter 3, Exercise 22 | data.frame | 310 | 5 |
| C3E9 | AMCP | The data used in Chapter 3, Exercise 9 | data.frame | 12 | 2 |
| C3T1 | AMCP | The data used in Chapter 3, Table 1 | data.frame | 6 | 1 |
| C3T3 | AMCP | The data used in Chapter 3, Table 3 | data.frame | 30 | 2 |
| C3T7R | AMCP | The data used for Chapter 3, Table 7 (raw data to produce the summary measures) | data.frame | 88 | 3 |
| C3T9R | AMCP | The data used for Chapter 3, Table 9 (raw data to produce the summary measures) | data.frame | 88 | 3 |
| C4E11 | AMCP | The data used in Chapter 4, Exercise 11 | data.frame | 24 | 2 |
| C4E12 | AMCP | The data used in Chapter 4, Exercise 12 | data.frame | 18 | 2 |
| C4E13 | AMCP | The data used in Chapter 4, Exercise 13 | data.frame | 20 | 2 |
| C4E18 | AMCP | The data used in Chapter 4, Exercise 18 | data.frame | 12 | 2 |
| C4E21 | AMCP | The data used in Chapter 4, Exercise 21 | data.frame | 39 | 2 |
| C4T1 | AMCP | The data used in Chapter 4, Table 1 | data.frame | 20 | 2 |
| C5E10 | AMCP | The data used in Chapter 5, Exercise 10 | data.frame | 24 | 2 |
| C5E16 | AMCP | The data used in Chapter 5, Exercise 16 | data.frame | 18 | 2 |
| C5E5 | AMCP | The data used in Chapter 5, Exercise 5 | data.frame | 20 | 2 |
| C5T4 | AMCP | The data used in Chapter 5, Table 4 | data.frame | 24 | 2 |
| C6E10 | AMCP | The data used in Chapter 6, Exercise 10 | data.frame | 45 | 2 |
| C6E11 | AMCP | The data used in Chapter 6, Exercise 11 | data.frame | 20 | 2 |
| C6E14 | AMCP | The data used in Chapter 6, Exercise 14 | data.frame | 48 | 2 |
| C6E16 | AMCP | The data used in Chapter 6, Exercise 16 | data.frame | 91 | 5 |
| C6T1 | AMCP | The data used in Chapter 6, Table 1 | data.frame | 24 | 2 |
| C7E12 | AMCP | The data used in Chapter 7, Exercise 12 | data.frame | 32 | 3 |
| C7E13 | AMCP | The data used in Chapter 7, Exercise 13 | data.frame | 48 | 3 |
| C7E14 | AMCP | The data used in Chapter 7, Exercise 14 | data.frame | 28 | 3 |
| C7E15 | AMCP | The data used in Chapter 7, Exercise 15 | data.frame | 36 | 3 |
| C7E18 | AMCP | The data used in Chapter 7, Exercise 18 | data.frame | 22 | 3 |
| C7E19 | AMCP | The data used in Chapter 7, Exercise 19 | data.frame | 40 | 3 |
| C7E21 | AMCP | The data used in Chapter 7, Exercise 21 | data.frame | 40 | 3 |
| C7E22 | AMCP | The data used in Chapter 7, Exercise 22 | data.frame | 28 | 4 |
| C7E23 | AMCP | The data used in Chapter 7, Exercise 23 | data.frame | 68 | 4 |
| C7E24 | AMCP | The data used in Chapter 7, Exercise 24 | data.frame | 56 | 4 |
| C7E25 | AMCP | The data used in Chapter 7, Exercise 25 | data.frame | 60 | 4 |
| C7E6 | AMCP | The data used in Chapter 7, Exercise 6 | data.frame | 45 | 3 |
| C7E9 | AMCP | The data used in Chapter 7, Exercise 9 | data.frame | 48 | 3 |
| C7T1 | AMCP | The data used in Chapter 7, Table 1 | data.frame | 20 | 2 |
| C7T11 | AMCP | The data used in Chapter 7, Table 11 | data.frame | 45 | 3 |
| C7T16 | AMCP | The data used in Chapter 7, Table 16 | data.frame | 22 | 3 |
| C7T24 | AMCP | The data used in Chapter 7, Table 24 | data.frame | 45 | 3 |
| C7T5 | AMCP | The data used in Chapter 7, Table 5 | data.frame | 30 | 3 |
| C7T9 | AMCP | The data used in Chapter 7, Table 9 | data.frame | 6 | 3 |
| C8E15 | AMCP | The data used in Chapter 8, Exercise 15 | data.frame | 48 | 4 |
| C8E16 | AMCP | The data used in Chapter 8, Exercise 16 | data.frame | 96 | 4 |
| C8E17 | AMCP | The data used in Chapter 8, Exercise 17 | data.frame | 54 | 4 |
| C8E18 | AMCP | The data used in Chapter 8, Exercise 18 | data.frame | 80 | 5 |
| C8E19 | AMCP | The data used in Chapter 8, Exercise 19 | data.frame | 80 | 5 |
| C8T12 | AMCP | The data used in Chapter 8, Table 12 | data.frame | 72 | 4 |
| C9E14 | AMCP | The data used in Chapter 9, Exercise 14 | data.frame | 155 | 4 |
| C9E15 | AMCP | The data used in Chapter 9, Exercise 15 | data.frame | 310 | 6 |
| C9E16 | AMCP | The data used in Chapter 9, Exercise 16 | data.frame | 310 | 6 |
| C9E4 | AMCP | The data used in Chapter 9, Exercise 4 | data.frame | 10 | 3 |
| C9ExtE1 | AMCP | The data used in Chapter 9 Extension, Exercise 1 | data.frame | 140 | 6 |
| C9ExtE2 | AMCP | The data used in Chapter 9 Extension, Exercise 2 | data.frame | 168 | 6 |
| C9ExtE3 | AMCP | The data used in Chapter 9 Extension, Exercise 3 | data.frame | 310 | 6 |
| C9ExtFigs4and5 | AMCP | The data used in Chapter 9 Extension Figures 4 and 5 | data.frame | 310 | 10 |
| C9ExtT1 | AMCP | The data used in Chapter 9, Extension Table 1 | data.frame | 6 | 3 |
| C9T1 | AMCP | The data used in Chapter 9, Table 1 | data.frame | 6 | 3 |
| C9T12 | AMCP | The data used in Chapter 9, Table 12 | data.frame | 18 | 4 |
| C9T7 | AMCP | The data used in Chapter 9, Table 7 | data.frame | 30 | 3 |
| chapter_1_exercise_18 | AMCP | The data used in Chapter 1, Exercise 18 | data.frame | 4 | 3 |
| chapter_1_exercise_19 | AMCP | The data used in Chapter 1, Exercise 19 | data.frame | 30 | 2 |
| chapter_1_exercise_21 | AMCP | The data used in Chapter 1, Exercise 21 | data.frame | 12 | 2 |
| chapter_1_exercise_22 | AMCP | The data used in Chapter 1, Exercise 22 | data.frame | 15 | 3 |
| chapter_1_exercise_23 | AMCP | The data used in Chapter 1, Exercise 23 | data.frame | 12 | 3 |
| chapter_1_table_1 | AMCP | The data used in Chapter 1, Table 1 | data.frame | 10 | 3 |
| chapter_10_exercise_14 | AMCP | The data used in Chapter 10, Exercise 14 | data.frame | 63 | 4 |
| chapter_10_exercise_7 | AMCP | The data used in Chapter 10, Exercise 7 | data.frame | 45 | 3 |
| chapter_10_exercise_9 | AMCP | The data used in Chapter 10, Exercise 9 | data.frame | 72 | 4 |
| chapter_10_table_5 | AMCP | The data used in Chapter 10, Table 5 | data.frame | 40 | 3 |
| chapter_10_table_9 | AMCP | The data used in Chapter 10, Table 9 | data.frame | 24 | 3 |
| chapter_11_exercise_17 | AMCP | The data used in Chapter 11, Exercise 17 | data.frame | 14 | 4 |
| chapter_11_exercise_18 | AMCP | The data used in Chapter 11, Exercise 18 | data.frame | 12 | 3 |
| chapter_11_exercise_19 | AMCP | The data used in Chapter 11, Exercise 19 | data.frame | 14 | 4 |
| chapter_11_exercise_21 | AMCP | The data used in Chapter 11, Exercise 21 | data.frame | 42 | 3 |
| chapter_11_exercise_22 | AMCP | The data used in Chapter 11, Exercise 22 | data.frame | 19 | 7 |
| chapter_11_exercise_23 | AMCP | The data used in Chapter 11, Exercise 23 | data.frame | 183 | 3 |
| chapter_11_exercise_24 | AMCP | The data used in Chapter 11, Exercise 24 | data.frame | 90 | 3 |
| chapter_11_exercise_3 | AMCP | The data used in Chapter 11, Exercise 3 | data.frame | 5 | 4 |
| chapter_11_exercise_5 | AMCP | The data used in Chapter 11, Exercise 5 | data.frame | 5 | 3 |
| chapter_11_table_1 | AMCP | The data used in Chapter 11, Table 1 | data.frame | 6 | 2 |
| chapter_11_table_19 | AMCP | The data used in Chapter 11, Table 19 | data.frame | 24 | 3 |
| chapter_11_table_20 | AMCP | The data used in Chapter 11, Table 20 | data.frame | 15 | 3 |
| chapter_11_table_4 | AMCP | The data used in Chapter 11, Table 4 | data.frame | 10 | 4 |
| chapter_11_table_5 | AMCP | The data used in Chapter 11, Table 5 | data.frame | 12 | 4 |
| chapter_12_exercise_17 | AMCP | The data used in Chapter 12, Exercise 17 | data.frame | 14 | 5 |
| chapter_12_exercise_18 | AMCP | The data used in Chapter 12, Exercise 18 | data.frame | 10 | 3 |
| chapter_12_exercise_19 | AMCP | The data used in Chapter 12, Exercise 19 | data.frame | 47 | 6 |
| chapter_12_exercise_21 | AMCP | The data used in Chapter 12, Exercise 21 | data.frame | 36 | 4 |
| chapter_12_exercise_22 | AMCP | The data used in Chapter 12, Exercise 22 | data.frame | 40 | 8 |
| chapter_12_exercise_23 | AMCP | The data used in Chapter 12, Exercise 23 | data.frame | 38 | 10 |
| chapter_12_exercise_24 | AMCP | The data used in Chapter 12, Exercise 24 | data.frame | 118 | 25 |
| chapter_12_exercise_25 | AMCP | The data used in Chapter 12, Exercise 25 | data.frame | 39 | 3 |
| chapter_12_exercise_26 | AMCP | The data used in Chapter 12, Exercise 26 | data.frame | 58 | 6 |
| chapter_12_exercise_27 | AMCP | The data used in Chapter 12, Exercise 27 | data.frame | 193 | 6 |
| chapter_12_exercise_9 | AMCP | The data used in Chapter 12, Exercise 9 | data.frame | 10 | 4 |
| chapter_12_table_1 | AMCP | The data used in Chapter 12, Table 1 | data.frame | 10 | 6 |
| chapter_12_table_11 | AMCP | The data used in Chapter 12, Table 11 | data.frame | 10 | 3 |
| chapter_12_table_15 | AMCP | The data used in Chapter 12, Table 15 | data.frame | 10 | 3 |
| chapter_12_table_29 | AMCP | The data used in Chapter 12, Table 29 | data.frame | 18 | 5 |
| chapter_12_table_7 | AMCP | The data used in Chapter 12, Table 7 | data.frame | 10 | 3 |
| chapter_12_table_9 | AMCP | The data used in Chapter 12, Table 9 | data.frame | 10 | 2 |
| chapter_13_exercise_10 | AMCP | The data used in Chapter 13, Exercise 10 | data.frame | 14 | 4 |
| chapter_13_exercise_13 | AMCP | The data used in Chapter 13, Exercise 13 | data.frame | 14 | 4 |
| chapter_13_exercise_14 | AMCP | The data used in Chapter 13, Exercise 14 | data.frame | 13 | 3 |
| chapter_13_exercise_22 | AMCP | The data used in Chapter 13, Exercise 22 | data.frame | 5 | 3 |
| chapter_13_exercise_23 | AMCP | The data used in Chapter 13, Exercise 23 | data.frame | 19 | 7 |
| chapter_13_exercise_24 | AMCP | The data used in Chapter 13, Exercise 24 | data.frame | 183 | 3 |
| chapter_13_exercise_25 | AMCP | The data used in Chapter 13, Exercise 25 | data.frame | 30 | 3 |
| chapter_13_exercise_7 | AMCP | The data used in Chapter 13, Exercise 7 | data.frame | 5 | 4 |
| chapter_13_table_1 | AMCP | The data used in Chapter 13, Table 1 | data.frame | 5 | 2 |
| chapter_13_table_10 | AMCP | The data used in Chapter 13, Table 10 | data.frame | 8 | 2 |
| chapter_13_table_2 | AMCP | The data used in Chapter 13, Table 2 | data.frame | 8 | 3 |
| chapter_13_table_5 | AMCP | The data used in Chapter 13, Table 5 | data.frame | 14 | 4 |
| chapter_13_table_6 | AMCP | The data used in Chapter 13, Table 6 | data.frame | 12 | 4 |
| chapter_14_exercise_10 | AMCP | The data used in Chapter 14, Exercise 10 | data.frame | 10 | 4 |
| chapter_14_exercise_14 | AMCP | The data used in Chapter 14, Exercise 14 | data.frame | 30 | 5 |
| chapter_14_exercise_15 | AMCP | The data used in Chapter 14, Exercise 15 | data.frame | 10 | 3 |
| chapter_14_exercise_21 | AMCP | The data used in Chapter 14, Exercise 21 | data.frame | 14 | 5 |
| chapter_14_exercise_22 | AMCP | The data used in Chapter 14, Exercise 22 | data.frame | 47 | 6 |
| chapter_14_table_1 | AMCP | The data used in Chapter 14, Table 1 | data.frame | 10 | 4 |
| chapter_14_table_10 | AMCP | The data used in Chapter 14, Table 10 | data.frame | 20 | 4 |
| chapter_14_table_13 | AMCP | The data used in Chapter 14, Table 13 | data.frame | 300 | 5 |
| chapter_14_table_3 | AMCP | The data used in Chapter 14, Table 3 | data.frame | 10 | 3 |
| chapter_14_table_4 | AMCP | The data used in Chapter 14, Table 4 | data.frame | 10 | 6 |
| chapter_14_table_5 | AMCP | The data used in Chapter 14, Table 5 | data.frame | 10 | 5 |
| chapter_14_table_7 | AMCP | The data used in Chapter 14, Table 7 | data.frame | 20 | 3 |
| chapter_14_table_8 | AMCP | The data used in Chapter 14, Table 8 | data.frame | 20 | 3 |
| chapter_14_table_9 | AMCP | The data used in Chapter 14, Table 9 | data.frame | 20 | 4 |
| chapter_15_exercise_16 | AMCP | The data used in Chapter 15, Exercise 16 | data.frame | 14 | 4 |
| chapter_15_exercise_17 | AMCP | The data used in Chapter 15, Exercise 17 | data.frame | 56 | 4 |
| chapter_15_exercise_18 | AMCP | The data used in Chapter 15, Exercise 18 | data.frame | 24 | 4 |
| chapter_15_exercise_18_univariate | AMCP | The data used in Chapter 15, Exercise 18 (Univariate) | data.frame | 72 | 3 |
| chapter_15_exercise_19 | AMCP | The data used in Chapter 15, Exercise 19 | data.frame | 24 | 4 |
| chapter_15_exercise_19_univariate | AMCP | The data used in Chapter 15, Exercise 19 (Univariate) | data.frame | 72 | 3 |
| chapter_15_table_1 | AMCP | The data used in Chapter 15, Table 1 | data.frame | 12 | 4 |
| chapter_16_exercise_5 | AMCP | The data used in Chapter 16, Exercise 5 | data.frame | 24 | 3 |
| chapter_16_exercise_7 | AMCP | The data used in Chapter 16, Exercise 7 | data.frame | 29 | 6 |
| chapter_16_exercise_9 | AMCP | The data used in Chapter 16, Exercise 9 | data.frame | 29 | 6 |
| chapter_16_table_1 | AMCP | The data used in Chapter 16, Table 1 | data.frame | 24 | 3 |
| chapter_16_table_4 | AMCP | The data used in Chapter 16, Table 4 | data.frame | 29 | 6 |
| chapter_3_exercise_10 | AMCP | The data used in Chapter 3, Exercise 10 | data.frame | 36 | 3 |
| chapter_3_exercise_11 | AMCP | The data used in Chapter 3, Exercise 11 | data.frame | 24 | 2 |
| chapter_3_exercise_19 | AMCP | The data used in Chapter 3, Exercise 19 | data.frame | 155 | 3 |
| chapter_3_exercise_20 | AMCP | The data used in Chapter 3, Exercise 20 | data.frame | 72 | 2 |
| chapter_3_exercise_21 | AMCP | The data used in Chapter 3, Exercise 21 | data.frame | 192 | 2 |
| chapter_3_exercise_22 | AMCP | The data used in Chapter 3, Exercise 22 | data.frame | 310 | 5 |
| chapter_3_exercise_9 | AMCP | The data used in Chapter 3, Exercise 9 | data.frame | 12 | 2 |
| chapter_3_table_1 | AMCP | The data used in Chapter 3, Table 1 | data.frame | 6 | 1 |
| chapter_3_table_3 | AMCP | The data used in Chapter 3, Table 3 | data.frame | 30 | 2 |
| chapter_3_table_7_raw | AMCP | The data used for Chapter 3, Table 7 (raw data to produce the summary measures) | data.frame | 88 | 3 |
| chapter_3_table_9_raw | AMCP | The data used for Chapter 3, Table 9 (raw data to produce the summary measures) | data.frame | 88 | 3 |
| chapter_4_exercise_11 | AMCP | The data used in Chapter 4, Exercise 11 | data.frame | 24 | 2 |
| chapter_4_exercise_12 | AMCP | The data used in Chapter 4, Exercise 12 | data.frame | 18 | 2 |
| chapter_4_exercise_13 | AMCP | The data used in Chapter 4, Exercise 13 | data.frame | 20 | 2 |
| chapter_4_exercise_18 | AMCP | The data used in Chapter 4, Exercise 18 | data.frame | 12 | 2 |
| chapter_4_exercise_21 | AMCP | The data used in Chapter 4, Exercise 21 | data.frame | 39 | 2 |
| chapter_4_table_1 | AMCP | The data used in Chapter 4, Table 1 | data.frame | 20 | 2 |
| chapter_5_exercise_10 | AMCP | The data used in Chapter 5, Exercise 10 | data.frame | 24 | 2 |
| chapter_5_exercise_16 | AMCP | The data used in Chapter 5, Exercise 16 | data.frame | 18 | 2 |
| chapter_5_exercise_5 | AMCP | The data used in Chapter 5, Exercise 5 | data.frame | 20 | 2 |
| chapter_5_table_4 | AMCP | The data used in Chapter 5, Table 4 | data.frame | 24 | 2 |
| chapter_6_exercise_10 | AMCP | The data used in Chapter 6, Exercise 10 | data.frame | 45 | 2 |
| chapter_6_exercise_11 | AMCP | The data used in Chapter 6, Exercise 11 | data.frame | 20 | 2 |
| chapter_6_exercise_14 | AMCP | The data used in Chapter 6, Exercise 14 | data.frame | 48 | 2 |
| chapter_6_exercise_16 | AMCP | The data used in Chapter 6, Exercise 16 | data.frame | 91 | 5 |
| chapter_6_table_1 | AMCP | The data used in Chapter 6, Table 1 | data.frame | 24 | 2 |
| chapter_7_exercise_12 | AMCP | The data used in Chapter 7, Exercise 12 | data.frame | 32 | 3 |
| chapter_7_exercise_13 | AMCP | The data used in Chapter 7, Exercise 13 | data.frame | 48 | 3 |
| chapter_7_exercise_14 | AMCP | The data used in Chapter 7, Exercise 14 | data.frame | 28 | 3 |
| chapter_7_exercise_15 | AMCP | The data used in Chapter 7, Exercise 15 | data.frame | 36 | 3 |
| chapter_7_exercise_18 | AMCP | The data used in Chapter 7, Exercise 18 | data.frame | 22 | 3 |
| chapter_7_exercise_19 | AMCP | The data used in Chapter 7, Exercise 19 | data.frame | 40 | 3 |
| chapter_7_exercise_21 | AMCP | The data used in Chapter 7, Exercise 21 | data.frame | 40 | 3 |
| chapter_7_exercise_22 | AMCP | The data used in Chapter 7, Exercise 22 | data.frame | 28 | 4 |
| chapter_7_exercise_23 | AMCP | The data used in Chapter 7, Exercise 23 | data.frame | 68 | 4 |
| chapter_7_exercise_24 | AMCP | The data used in Chapter 7, Exercise 24 | data.frame | 56 | 4 |
| chapter_7_exercise_25 | AMCP | The data used in Chapter 7, Exercise 25 | data.frame | 60 | 4 |
| chapter_7_exercise_6 | AMCP | The data used in Chapter 7, Exercise 6 | data.frame | 45 | 3 |
| chapter_7_exercise_9 | AMCP | The data used in Chapter 7, Exercise 9 | data.frame | 48 | 3 |
| chapter_7_table_1 | AMCP | The data used in Chapter 7, Table 1 | data.frame | 20 | 2 |
| chapter_7_table_11 | AMCP | The data used in Chapter 7, Table 11 | data.frame | 45 | 3 |
| chapter_7_table_16 | AMCP | The data used in Chapter 7, Table 16 | data.frame | 22 | 3 |
| chapter_7_table_24 | AMCP | The data used in Chapter 7, Table 24 | data.frame | 45 | 3 |
| chapter_7_table_5 | AMCP | The data used in Chapter 7, Table 5 | data.frame | 30 | 3 |
| chapter_7_table_9 | AMCP | The data used in Chapter 7, Table 9 | data.frame | 6 | 3 |
| chapter_8_exercise_15 | AMCP | The data used in Chapter 8, Exercise 15 | data.frame | 48 | 4 |
| chapter_8_exercise_16 | AMCP | The data used in Chapter 8, Exercise 16 | data.frame | 96 | 4 |
| chapter_8_exercise_17 | AMCP | The data used in Chapter 8, Exercise 17 | data.frame | 54 | 4 |
| chapter_8_exercise_18 | AMCP | The data used in Chapter 8, Exercise 18 | data.frame | 80 | 5 |
| chapter_8_exercise_19 | AMCP | The data used in Chapter 8, Exercise 19 | data.frame | 80 | 5 |
| chapter_8_table_12 | AMCP | The data used in Chapter 8, Table 12 | data.frame | 72 | 4 |
| chapter_9_exercise_14 | AMCP | The data used in Chapter 9, Exercise 14 | data.frame | 155 | 4 |
| chapter_9_exercise_15 | AMCP | The data used in Chapter 9, Exercise 15 | data.frame | 310 | 6 |
| chapter_9_exercise_16 | AMCP | The data used in Chapter 9, Exercise 16 | data.frame | 310 | 6 |
| chapter_9_exercise_4 | AMCP | The data used in Chapter 9, Exercise 4 | data.frame | 10 | 3 |
| chapter_9_extension_exercise_1 | AMCP | The data used in Chapter 9 Extension, Exercise 1 | data.frame | 140 | 6 |
| chapter_9_extension_exercise_2 | AMCP | The data used in Chapter 9 Extension, Exercise 2 | data.frame | 168 | 6 |
| chapter_9_extension_exercise_3 | AMCP | The data used in Chapter 9 Extension, Exercise 3 | data.frame | 310 | 6 |
| chapter_9_extension_figures_4_and_5 | AMCP | The data used in Chapter 9 Extension Figures 4 and 5 | data.frame | 310 | 10 |
| chapter_9_extension_table_1 | AMCP | The data used in Chapter 9, Extension Table 1 | data.frame | 6 | 3 |
| chapter_9_table_1 | AMCP | The data used in Chapter 9, Table 1 | data.frame | 6 | 3 |
| chapter_9_table_12 | AMCP | The data used in Chapter 9, Table 12 | data.frame | 18 | 4 |
| chapter_9_table_7 | AMCP | The data used in Chapter 9, Table 7 | data.frame | 30 | 3 |
| T1T1 | AMCP | The data used in Tutorial 1, Table 1 | data.frame | 103 | 1 |
| T2T1 | AMCP | The data used in Tutorial 2, Table 1 | data.frame | 8 | 2 |
| T2T2 | AMCP | The data used in Tutorial 2, Table 2 | data.frame | 8 | 4 |
| T3AT1 | AMCP | The data used in Tutorial 3A, Table 1 | data.frame | 8 | 2 |
| T3AT2 | AMCP | The data used in Tutorial 3A, Table 2 | data.frame | 8 | 4 |
| T3AT4 | AMCP | The data used in Tutorial 3A, Table 4 | data.frame | 10 | 6 |
| T3AT5 | AMCP | The data used in Tutorial 3A, Table 5 | data.frame | 10 | 6 |
| tutorial_1_table_1 | AMCP | The data used in Tutorial 1, Table 1 | data.frame | 103 | 1 |
| tutorial_2_table_1 | AMCP | The data used in Tutorial 2, Table 1 | data.frame | 8 | 2 |
| tutorial_2_table_2 | AMCP | The data used in Tutorial 2, Table 2 | data.frame | 8 | 4 |
| tutorial_3a_table_1 | AMCP | The data used in Tutorial 3A, Table 1 | data.frame | 8 | 2 |
| tutorial_3a_table_2 | AMCP | The data used in Tutorial 3A, Table 2 | data.frame | 8 | 4 |
| tutorial_3a_table_4 | AMCP | The data used in Tutorial 3A, Table 4 | data.frame | 10 | 6 |
| tutorial_3a_table_5 | AMCP | The data used in Tutorial 3A, Table 5 | data.frame | 10 | 6 |
| dnase | survregVB | Subset of 'rhDNase' from the 'survival' package | data.frame | 645 | 4 |
| lung_cancer | survregVB | Subset of GSE102287: African American (AA) Patients | data.frame | 60 | 7 |
| simulation_frailty | survregVB | Simulated data incorporating shared frailty effects to model clustered time-to-event data. | data.frame | 75 | 7 |
| simulation_nofrailty | survregVB | Simulated data without shared frailty effects to model unclustered time-to-event data. | data.frame | 300 | 8 |
| neuro_data | RobustLPA | Simulated Neuropsychological Dataset for Robust LPA | data.frame | 250 | 7 |
| overdoses | optic | OPTIC Overdoses example data. | tbl_df | 969 | 7 |
| lymphoma | KODAMA | Lymphoma gene-expression data | list | | |
| MetRef | KODAMA | Nuclear Magnetic Resonance spectra of urine samples | list | | |
| USA | KODAMA | State of the Union data | list | | |
| mathTest | equateMultiple | Math Test Data | list | | |
| evapotranspiration | BLA | Evapotranspiration data | data.frame | 691 | 3 |
| soil | BLA | Soil survey data | data.frame | 6110 | 3 |
| SoilP | BLA | Soil Phosphorus data | data.frame | 6020 | 2 |
| SoilpH | BLA | Soil pH data | data.frame | 6047 | 2 |
| MuseumsDk | micEconDistRay | Data on Museums in Denmark | tbl_df | 558 | 19 |
| aggregate_design | idefix | Discrete choice aggregate design. | data.frame | 112 | 9 |
| example_design | idefix | Discrete choice design. | matrix | 16 | 6 |
| example_design2 | idefix | Discrete choice design. | matrix | 24 | 8 |
| nochoice_design | idefix | Discrete choice design with no choice option. | matrix | 24 | 7 |
| exampledata | SDCNway | A subset of the 1992 National Adult Literacy Study (NALS) prison study public-use microdata file. | data.frame | 182 | 19 |
| graphs3 | dga | All Decomposable Graphical Models on Three Lists | list | | |
| graphs4 | dga | All Decomposable Graphical Models on Four Lists | list | | |
| graphs5 | dga | All Decomposable Graphical Models on Five Lists | list | | |
| influenza | EpiStrainDynamics | World Health Organisation Global Influenza Programme for Australia | data.frame | 426 | 7 |
| sarscov2 | EpiStrainDynamics | United Kingdom Health Security Agency SARS-CoV-2 case data | data.frame | 830 | 6 |
| grmtree_data | grmtree | Medical Outcomes Study Social Support Survey (MOS-SS) Test Data | tbl_df | 3500 | 17 |
| grmtree_long_data | grmtree | Synthetic Longitudinal MOS-SS Social Support Survey Data | tbl_df | 1500 | 24 |
| mRNA_data | mobileRNA | mRNA_data: simulated messenger RNA data for biological replicates | data.table | 7153 | 17 |
| sRNA_data | mobileRNA | sRNA_data: simulated data for biological replicates | data.frame | 11985 | 29 |
| starnames | qs2 | Official list of IAU Star Names | data.frame | 336 | 9 |
| J12S5000 | exametrika | J12S5000 | exametrika | | |
| J15S3810 | exametrika | J15S3810 | exametrika | | |
| J15S500 | exametrika | J15S500 | exametrika | | |
| J20S400 | exametrika | J20S400 | exametrika | | |
| J20S600 | exametrika | J20S600 | exametrika | | |
| J21S300 | exametrika | J21S300 | exametrika | | |
| J35S500 | exametrika | J35S500 | exametrika | | |
| J35S5000 | exametrika | J35S5000 | exametrika | | |
| J35S515 | exametrika | J35S515 | exametrika | | |
| J50S100 | exametrika | J50S100 | exametrika | | |
| J5S10 | exametrika | J5S10 | exametrika | | |
| J5S1000 | exametrika | J5S1000 | exametrika | | |
| centrality.measures | influential | Centrality measures dataset | data.frame | 794 | 6 |
| coexpression.adjacency | influential | Adjacency matrix | matrix | 794 | 794 |
| coexpression.data | influential | Co-expression dataset | data.frame | 2410 | 2 |
| benchmark64.data | bit64 | Results of performance measurement on a Core i7 Lenovo T410 8 GB RAM under Windows 7 64bit | matrix | 16 | 6 |
| optimizer64.data | bit64 | Results of performance measurement on a Core i7 Lenovo T410 8 GB RAM under Windows 7 64bit | matrix | 8 | 2 |
| Anthro | bayesQRsurvey | Children anthropometric data | data.frame | 985 | 8 |
| roaches | countSTAR | Data on the efficacy of a pest management system at reducing the number of roaches in urban apartments. | data.frame | 262 | 5 |
| At_Age_TS_Variables | openMSE | At-Age Time Series Variables | data.frame | 6 | 3 |
| TS_Variables | openMSE | Time Series Variables | data.frame | 21 | 3 |
| bcsd_obs | stars | Monthly Gridded Meteorological Observations | stars_proxy | | |
| L7_ETMs | stars | Landsat-7 bands for a selected region around Olinda, BR | stars_proxy | | |
| stars_sentinel2 | stars | Sentinel-2 sample tile | stars_proxy | | |
| pcod | SAMtool | Pacific cod in Area 5ABCD (Hecate Strait and Queen Charlotte Sound), British Columbia, Canada | list | | |
| swordfish | SAMtool | North Atlantic Swordfish dataset | Data | | |
| breweries | mapview | Selected breweries in Franconia | sf | 224 | 9 |
| franconia | mapview | Administrative district borders of Franconia | sf | 37 | 7 |
| trails | mapview | Selected hiking trails in Franconia | sf | 543 | 4 |
| bbs | spdep | Columbus OH spatial analysis data set | matrix | 49 | |
| col.gal.nb | spdep | Columbus OH spatial analysis data set | nb | | |
| COL.nb | spdep | Columbus OH spatial analysis data set - old numbering | nb | | |
| COL.OLD | spdep | Columbus OH spatial analysis data set - old numbering | data.frame | 49 | 22 |
| columbus | spdep | Columbus OH spatial analysis data set | data.frame | 49 | 22 |
| coords | spdep | Columbus OH spatial analysis data set | matrix | 49 | |
| eire.coords.utm | spdep | Eire data sets | data.frame | 26 | 2 |
| eire.df | spdep | Eire data sets | data.frame | 26 | 9 |
| eire.nb | spdep | Eire data sets | nb | | |
| eire.polys.utm | spdep | Eire data sets | polylist | | |
| polys | spdep | Columbus OH spatial analysis data set | polylist | | |
| acti_raw_data | actibase | Example Actigraphy/Activity Raw Data | tbl_df | 240500 | 4 |
| txyz | actibase | Vector of X, Y, Z, and maybe time | character | | |
| xyz | actibase | Vector of X, Y, Z, and maybe time | character | | |
| xyzt | actibase | Vector of X, Y, Z, and maybe time | character | | |
| Chlorid92 | gstat | Südliche Tullnerfeld data set | data.frame | 161 | 37 |
| coalash | gstat | Coal ash samples from a mine in Pennsylvania | data.frame | 208 | 3 |
| DE_RB_2005 | gstat | Spatio-temporal data set with rural background PM10 concentrations in Germany 2005 | STSDF | | |
| demstd | gstat | Spatial Interpolation Comparison 1997 data set: Swiss Rainfall | SpatialGridDataFrame | | |
| fulmar | gstat | Fulmaris glacialis data | data.frame | 1324 | 6 |
| jura.grid | gstat | Jura data set | data.frame | 5957 | 6 |
| jura.pred | gstat | Jura data set | data.frame | 259 | 13 |
| jura.val | gstat | Jura data set | data.frame | 100 | 13 |
| juragrid.dat | gstat | Jura data set | data.frame | 5957 | 4 |
| meuse.all | gstat | Meuse river data set - original, full data set | data.frame | 164 | 17 |
| meuse.alt | gstat | Meuse river altitude data set | data.frame | 768 | 3 |
| ncp.grid | gstat | Grid for the NCP, the Dutch part of the North Sea | data.frame | 2297 | 5 |
| oxford | gstat | Oxford soil samples | data.frame | 126 | 22 |
| pcb | gstat | PCB138 measurements in sediment at the NCP, the Dutch part of the North Sea | data.frame | 216 | 7 |
| prediction.dat | gstat | Jura data set | data.frame | 259 | 11 |
| sic_full | gstat | Spatial Interpolation Comparison 1997 data set: Swiss Rainfall | SpatialPointsDataFrame | | |
| sic_obs | gstat | Spatial Interpolation Comparison 1997 data set: Swiss Rainfall | SpatialPointsDataFrame | | |
| sic.grid | gstat | Spatial Interpolation Comparison 2004 data set: Natural Ambient Radioactivity | data.frame | 9591 | 2 |
| sic.pred | gstat | Spatial Interpolation Comparison 2004 data set: Natural Ambient Radioactivity | data.frame | 808 | 3 |
| sic.test | gstat | Spatial Interpolation Comparison 2004 data set: Natural Ambient Radioactivity | data.frame | 808 | 5 |
| sic.train | gstat | Spatial Interpolation Comparison 2004 data set: Natural Ambient Radioactivity | data.frame | 200 | 13 |
| sic.val | gstat | Spatial Interpolation Comparison 2004 data set: Natural Ambient Radioactivity | data.frame | 200 | 5 |
| transect.dat | gstat | Jura data set | data.frame | 106 | 5 |
| tull36 | gstat | Südliche Tullnerfeld data set | data.frame | 36 | 2 |
| TULLNREG | gstat | Südliche Tullnerfeld data set | data.frame | 485 | 2 |
| validation.dat | gstat | Jura data set | data.frame | 100 | 11 |
| vv | gstat | Precomputed variogram for PM10 in data set air | StVariogram | 66 | 7 |
| walker | gstat | Walker Lake sample and exhaustive data sets | SpatialPointsDataFrame | | |
| walker.exh | gstat | Walker Lake sample and exhaustive data sets | SpatialGridDataFrame | | |
| wind | gstat | Ireland wind data, 1961-1978 | data.frame | 6574 | 15 |
| wind.loc | gstat | Ireland wind data, 1961-1978 | data.frame | 12 | 5 |
| example_data | stockplotr | SS3 Example data | tbl_df | 589809 | 34 |
| AgeStructuredObs | MSEtool | Age-Structured Observation Model | obs | | |
| Albacore | MSEtool | Stock class objects | Stock | | |
| Albacore_TwoFleet | MSEtool | MOM class objects | MOM | | |
| AlbacoreExStock | MSEtool | Albacore Example Stock | stock | | |
| AsympExFleet | MSEtool | Asymptotic Selectivity Example Fleet | fleet | | |
| Atlantic_mackerel | MSEtool | Data class objects | Data | | |
| Blue_shark | MSEtool | Stock class objects | Stock | | |
| Bluefin_tuna | MSEtool | Stock class objects | Stock | | |
| Bluefin_tuna_WAtl | MSEtool | Stock class objects | Stock | | |
| Butterfish | MSEtool | Stock class objects | Stock | | |
| ButterfishExStock | MSEtool | Butterfish Example Stock | stock | | |
| CatchAndSurveyObs | MSEtool | Catch-and-Survey Observation Model | obs | | |
| China_rockfish | MSEtool | Data class objects | Data | | |
| Cobia | MSEtool | Data class objects | Data | | |
| CommercialFleetObs | MSEtool | Commercial-Fleet Observation Model | obs | | |
| ComplexOM | MSEtool | Stock Complex Example Operating Model | om | | |
| DataDescription | MSEtool | DataDescription | data.frame | 94 | 2 |
| DataRichObs | MSEtool | Data-Rich Observation Model | obs | | |
| DataSlots | MSEtool | DataSlots | tbl_df | 101 | 4 |
| DecE_Dom | MSEtool | Fleet class objects | Fleet | | |
| DecE_HDom | MSEtool | Fleet class objects | Fleet | | |
| DecE_NDom | MSEtool | Fleet class objects | Fleet | | |
| DomeExFleet | MSEtool | Dome-Shaped Selectivity Example Fleet | fleet | | |
| Example_datafile | MSEtool | Data class objects | Data | | |
| FlatE_Dom | MSEtool | Fleet class objects | Fleet | | |
| FlatE_HDom | MSEtool | Fleet class objects | Fleet | | |
| FlatE_NDom | MSEtool | Fleet class objects | Fleet | | |
| FleetDescription | MSEtool | FleetDescription | data.frame | 20 | 2 |
| FullComplianceImp | MSEtool | Full-Compliance Implementation Error | imp | | |
| Generic_DecE | MSEtool | Fleet class objects | Fleet | | |
| Generic_FlatE | MSEtool | Fleet class objects | Fleet | | |
| Generic_Fleet | MSEtool | Fleet class objects | Fleet | | |
| Generic_IncE | MSEtool | Fleet class objects | Fleet | | |
| Generic_Obs | MSEtool | Obs class objects | Obs | | |
| Gulf_blue_tilefish | MSEtool | Data class objects | Data | | |
| HermOM | MSEtool | Hermaphroditic Wrasse Example Operating Model | om | | |
| Herring | MSEtool | Stock class objects | Stock | | |
| HistDescription | MSEtool | HistDescription | data.frame | 76 | 2 |
| ImpDescription | MSEtool | ImpDescription | data.frame | 7 | 2 |
| Imprecise_Biased | MSEtool | Obs class objects | Obs | | |
| Imprecise_Unbiased | MSEtool | Obs class objects | Obs | | |
| IncE_HDom | MSEtool | Fleet class objects | Fleet | | |
| IncE_NDom | MSEtool | Fleet class objects | Fleet | | |
| LengthStructuredObs | MSEtool | Length-Structured Observation Model | obs | | |
| LHdatabase | MSEtool | LHdatabase | list | | |
| Low_Effort_Non_Target | MSEtool | Fleet class objects | Fleet | | |
| Mackerel | MSEtool | Stock class objects | Stock | | |
| MSEDescription | MSEtool | MSEDescription | data.frame | 29 | 2 |
| MultiStockOM | MSEtool | Multi-Stock Multi-Fleet Example Operating Model | om | | |
| ObsDescription | MSEtool | ObsDescription | data.frame | 30 | 2 |
| OMDescription | MSEtool | OMDescription | data.frame | 15 | 2 |
| ourReefFish | MSEtool | Data class objects | Data | | |
| OverageImp | MSEtool | Overage Implementation Error | imp | | |
| Overages | MSEtool | Imp class objects | Imp | | |
| PartialSizeComplianceImp | MSEtool | Partial Size-Regulation Compliance Implementation Error | imp | | |
| Perfect_Imp | MSEtool | Imp class objects | Imp | | |
| Perfect_Info | MSEtool | Obs class objects | Obs | | |
| Porgy | MSEtool | Stock class objects | Stock | | |
| Precise_Biased | MSEtool | Obs class objects | Obs | | |
| Precise_Unbiased | MSEtool | Obs class objects | Obs | | |
| Red_snapper | MSEtool | Data class objects | Data | | |
| ReqData | MSEtool | ReqData | data.frame | 123 | 2 |
| Rockfish | MSEtool | Stock class objects | Stock | | |
| SeasonalSpatialExStock | MSEtool | Seasonal Spatial Example Stock | stock | | |
| SeasonalSpatialOM | MSEtool | Seasonal Spatial Example Operating Model | om | | |
| SimulatedData | MSEtool | SimulatedData Data | Data | | |
| Simulation_1 | MSEtool | Data class objects | Data | | |
| SingleStockOM | MSEtool | Single Stock Example Operating Model | om | | |
| Snapper | MSEtool | Stock class objects | Stock | | |
| Sole | MSEtool | Stock class objects | Stock | | |
| StockDescription | MSEtool | StockDescription | data.frame | 27 | 2 |
| Target_All_Fish | MSEtool | Fleet class objects | Fleet | | |
| Targeting_Small_Fish | MSEtool | Fleet class objects | Fleet | | |
| Taxa_Table | MSEtool | Taxa_Table | tbl_df | 34721 | 8 |
| testOM | MSEtool | OM class objects | OM | | |
| Toothfish | MSEtool | Stock class objects | Stock | | |
| TwoFleetOM | MSEtool | Two-Fleet Example Operating Model | om | | |
| UnderageImp | MSEtool | Underage Implementation Error | imp | | |
| WrasseFemaleExStock | MSEtool | Wrasse Example Stocks (Female / Male) | stock | | |
| WrasseMaleExStock | MSEtool | Wrasse Example Stocks (Female / Male) | stock | | |
| met | inti | Swedish cultivar trial data | data.frame | 1069 | 8 |
| potato | inti | Water use efficiency in 15 potato genotypes | data.frame | 150 | 17 |
| affiliation_info | asar | Author affiliations | data.frame | 34 | 8 |
| taxonomy | malaviR | MalAvi host species matched to the clootl (eBird) taxonomy | data.frame | 2339 | 6 |
| BS.chr22 | bsseq | Whole-genome bisulfite sequencing for chromosome 22 from Lister et al. | BSseq | | |
| bixi_flows | mapgl | BIXI Montréal Hourly Bike Sharing Flows (July 1-7, 2019) | data.frame | 6092 | 4 |
| bixi_locations | mapgl | BIXI Montréal Bike Share Stations (2019) | data.frame | 618 | 4 |
| solubility | embed | Compound solubility data | tbl_df | 1267 | 229 |
| circle_example | themis | Synthetic Dataset With a Circle | data.frame | 400 | 4 |
| emoji_samples | textrecipes | Sample sentences with emojis | tbl_df | 4 | 1 |
| toydata | localIV | A Hypothetical Dataset for Illustrative Purpose | data.frame | 10000 | 4 |
| alerce_data | HBV.IANIGLA | Alerce's glacier data for modeling | list | | |
| glacio_hydro_hbv | HBV.IANIGLA | Synthetic glacio-hydrological data for modeling | list | | |
| lumped_hbv | HBV.IANIGLA | Lumped HBV catchment data | data.frame | 5310 | 5 |
| semi_distributed_hbv | HBV.IANIGLA | Semi-distributed HBV model data | list | | |
| tupungato_data | HBV.IANIGLA | Tupungato River basin data | list | | |
| nlmixr2Keywords | nlmixr2est | A list and description of the fields in the nlmxir2 object | data.frame | 58 | 3 |
| tplyr_adae | Tplyr | ADAE Data | tbl_df | 276 | 55 |
| tplyr_adas | Tplyr | ADAS Data | tbl_df | 1040 | 40 |
| tplyr_adlb | Tplyr | ADLB Data | tbl_df | 311 | 46 |
| tplyr_adpe | Tplyr | ADPE Data | tbl_df | 21 | 8 |
| tplyr_adsl | Tplyr | ADSL Data | tbl_df | 254 | 49 |
| correspondence | cansim | The correspondence file for old to new StatCan table numbers is included in the package | tbl_df | 4807 | 2 |
| yeast_data | BBNI | Yeast Cell-Cycle Gene Expression Data | matrix | 14 | 385 |
| primates | corHMM | Example datasets | list | | |
| primates.paint | corHMM | Example datasets | list | | |
| rayDISC.example | corHMM | Example datasets | list | | |
| aba_phenotype_data_df | NeuroDataSets | Allen Brain Atlas Phenotype Data | data.frame | 377 | 4 |
| ability_intelligence_list | NeuroDataSets | Ability and Intelligence Tests | list | | |
| acupuncture_df | NeuroDataSets | Acupuncture Therapy for Chronic Headache | data.frame | 301 | 4 |
| AD_biomarkers_tbl_df | NeuroDataSets | Alzheimer's Biomarkers | tbl_df | 333 | 131 |
| ADHD_df | NeuroDataSets | ADHD Symptom Checklist for Children Aged 6–8 Years | data.frame | 355 | 19 |
| adolescent_mental_health_df | NeuroDataSets | Adolescent Mental Health Study | data.frame | 4344 | 3 |
| alzheimer_smoking_df | NeuroDataSets | Smoking and Alzheimer's Disease | data.frame | 538 | 3 |
| ASD_risks_tbl_df | NeuroDataSets | Autism Spectrum Disorder (ASD) Risk Factors in Children | spec_tbl_df | 1985 | 28 |
| bilingual_brains_df | NeuroDataSets | Brain Structure in Bilingual Humans | data.frame | 22 | 2 |
| blood_brain_barrier_df | NeuroDataSets | Blood-Brain Barrier | data.frame | 34 | 9 |
| brain_litter_mammals_df | NeuroDataSets | Mammal Brain Size and Litter Size Relationship | data.frame | 96 | 2 |
| brain_size_iq_df | NeuroDataSets | Brain Size and IQ Study Data | data.frame | 40 | 7 |
| brain_string_players_df | NeuroDataSets | Brain Activity in String Players | data.frame | 15 | 2 |
| brainexpression_df | NeuroDataSets | Proteolipid Protein 1 Gene Expression in Brain Tissue | data.frame | 45 | 2 |
| brains_cognitive_matrix | NeuroDataSets | BRAiNS Cohort Cognitive States Matrix | matrix | 6240 | 13 |
| brainvolume_df | NeuroDataSets | Meta-Analysis on Human Brain Volume and Intelligence | data.frame | 83 | 8 |
| cerebellar_age_df | NeuroDataSets | Repeated Measurements of Age and Cerebellar Volume | data.frame | 144 | 4 |
| chimpbrains_df | NeuroDataSets | Brodmann's Area 44 Asymmetry in Chimpanzees | data.frame | 20 | 3 |
| cocaine_dopamine_df | NeuroDataSets | Effects of Cocaine on Dopamine Receptors | data.frame | 34 | 2 |
| DA_schizophrenia_tbl_df | NeuroDataSets | DBH in Schizophrenia | tbl_df | 25 | 2 |
| dementia_df | NeuroDataSets | Dementia Scores Dataset | data.frame | 1000 | 4 |
| encephalitis_df | NeuroDataSets | Cases of Herpes Encephalitis in Bavaria and Saxony | data.frame | 26 | 3 |
| epilepsy_drug_qol_df | NeuroDataSets | SANAD Epilepsy Drug Treatment Quality of Life Study | data.frame | 1852 | 9 |
| epilepsy_drug_trial_df | NeuroDataSets | Epileptic Seizures Clinical Drug Trial | data.frame | 295 | 6 |
| epilepsy_RCT_tbl_df | NeuroDataSets | Epilepsy Treatment Randomized Controlled Trial | tbl_df | 59 | 8 |
| gm_expected_patterns_tbl_df | NeuroDataSets | Patterns of Gray Matter in Schizophrenia | tbl_df | 33 | 16 |
| guineapig_neuro_df | NeuroDataSets | Guinea Pig Neural Data | data.frame | 346 | 1 |
| hippocampus_lesions_df | NeuroDataSets | Memory and the Hippocampus | data.frame | 57 | 2 |
| iq_country_tbl_df | NeuroDataSets | Average Global IQ per Country | spec_tbl_df | 193 | 10 |
| mammals_brain_body_df | NeuroDataSets | Mammal Brain and Body Size | data.frame | 96 | 5 |
| markers_brain_df | NeuroDataSets | Cross-Species Brain Cell Marker Genes | data.frame | 6000 | 2 |
| markers_human_brain_df | NeuroDataSets | Human Brain Cell Marker Genes | data.frame | 5500 | 2 |
| markers_mouse_brain_df | NeuroDataSets | Mouse Brain Cell Marker Genes | data.frame | 5430 | 2 |
| migraine_treatment_df | NeuroDataSets | Migraine Headache Treatment | data.frame | 4152 | 9 |
| migraines_df | NeuroDataSets | Effects of Transcranial Magnetic Stimulation on Migraine Headaches | data.frame | 2 | 4 |
| migrane_dose_df | NeuroDataSets | Migraine Dose–Response Trial Data | data.frame | 8 | 3 |
| neanderthal_brains_df | NeuroDataSets | Cranial Capacity in Neanderthals and Modern Humans | data.frame | 39 | 3 |
| neuro_pointprocess_matrix | NeuroDataSets | Neurophysiological Point Process Data | matrix | 469 | |
| neurodeg_dose_df | NeuroDataSets | Simulated Neurodegenerative Disease Dose Data | data.frame | 1250 | 4 |
| nfl_concussions_tbl_df | NeuroDataSets | Concussions in the National Football League (2012–2014) | spec_tbl_df | 392 | 18 |
| OASIS_cross_tbl_df | NeuroDataSets | Cross-sectional Brain MRI Data Across Adult Lifespan | spec_tbl_df | 436 | 12 |
| OASIS_long_tbl_df | NeuroDataSets | Longitudinal MRI Data in Nondemented and Demented Older Adults | spec_tbl_df | 373 | 15 |
| parkinsons_dopamine_list | NeuroDataSets | Dopamine Agonists as Adjunct Therapy in Parkinson’s | list | | |
| pediatric_glioma_tbl_df | NeuroDataSets | Pediatric High-Grade Glioma Clinical Dataset | spec_tbl_df | 57 | 22 |
| psych_neurocog_df | NeuroDataSets | Psychotic Cognition | data.frame | 242 | 10 |
| SAHemorrhage_df | NeuroDataSets | Subarachnoid Hemorrhage Clinical and Laboratory Data | data.frame | 113 | 7 |
| sleep_deprivation_tbl_df | NeuroDataSets | Sleep Deprivation and Cognitive Performance Data | spec_tbl_df | 60 | 14 |
| sleep_disorder_df | NeuroDataSets | Transient Sleep Disorder Polysomnography Scoring Data | data.frame | 82 | 3 |
| sleep_performance_df | NeuroDataSets | Sleep and Learning Performance | data.frame | 10 | 2 |
| subcortical_patterns_tbl_df | NeuroDataSets | Patterns of Subcortical Structures | tbl_df | 8 | 16 |
| TBI_age_tbl_df | NeuroDataSets | Traumatic Brain Injury (TBI) Cases by Age Group and Injury Mechanism | spec_tbl_df | 231 | 5 |
| TBI_military_tbl_df | NeuroDataSets | Traumatic Brain Injury (TBI) in U.S. Military Personnel | spec_tbl_df | 438 | 5 |
| TBI_steroids_df | NeuroDataSets | Corticosteroids in Acute Traumatic Brain Injury | data.frame | 17 | 5 |
| tourette_ADHD_df | NeuroDataSets | Attentional Dysfunction in Adults With Tourette’s Syndrome | data.frame | 51 | 2 |
| WMpatterns_tbl_df | NeuroDataSets | White Matter Patterns | tbl_df | 24 | 15 |
| DOSPERT | EFAtools | DOSPERT | list | | |
| DOSPERT_raw | EFAtools | DOSPERT_raw | data.frame | 3123 | 30 |
| GRiPS_raw | EFAtools | GRiPS_raw | data.frame | 810 | 8 |
| IDS2_R | EFAtools | Intelligence subtests from the Intelligence and Development Scales-2 | matrix | 14 | 14 |
| population_models | EFAtools | population_models | list | | |
| RiskDimensions | EFAtools | RiskDimensions | list | | |
| SPSS_23 | EFAtools | Various outputs from SPSS (version 23) FACTOR | list | | |
| SPSS_27 | EFAtools | Various outputs from SPSS (version 27) FACTOR | list | | |
| test_models | EFAtools | Four test models used in Grieder and Steiner (2022) | list | | |
| UPPS_raw | EFAtools | UPPS_raw | data.frame | 645 | 45 |
| WJIV_ages_14_19 | EFAtools | Woodcock Johnson IV: ages 14 to 19 | list | | |
| WJIV_ages_20_39 | EFAtools | Woodcock Johnson IV: ages 20 to 39 | list | | |
| WJIV_ages_3_5 | EFAtools | Woodcock Johnson IV: ages 3 to 5 | list | | |
| WJIV_ages_40_90 | EFAtools | Woodcock Johnson IV: ages 40 to 90 plus | list | | |
| WJIV_ages_6_8 | EFAtools | Woodcock Johnson IV: ages 6 to 8 | list | | |
| WJIV_ages_9_13 | EFAtools | Woodcock Johnson IV: ages 9 to 13 | list | | |
| animal_steps | ggcircular | Simulated animal movement steps | tbl_df | 600 | 8 |
| axial_orientations | ggcircular | Simulated axial orientations | tbl_df | 300 | 3 |
| hourly_activity | ggcircular | Simulated hourly activity | tbl_df | 240 | 5 |
| wind_directions | ggcircular | Simulated wind directions | tbl_df | 500 | 4 |
| BriggsEx47 | rdecision | Probabilistic results of HIV model | data.frame | 1000 | 7 |
| cognition | galamm | Simulated Data with Measurements of Cognitive Abilities | data.frame | 14400 | 7 |
| diet | galamm | Diet Data | data.frame | 742 | 8 |
| epilep | galamm | Epilepsy Data | data.frame | 236 | 7 |
| hsced | galamm | Example Data with Heteroscedastic Residuals | data.frame | 1200 | 5 |
| latent_covariates | galamm | Simulated Data with Latent and Observed Covariates Interaction | data.frame | 600 | 5 |
| latent_covariates_long | galamm | Simulated Longitudinal Data with Latent and Observed Covariates Interaction | data.frame | 800 | 5 |
| lifespan | galamm | Simulated Dataset with Lifespan Trajectories of Three Cognitive Domains | data.frame | 54457 | 10 |
| mresp | galamm | Simulated Mixed Response Data | data.frame | 4000 | 4 |
| mresp_hsced | galamm | Simulated Mixed Response Data with Heteroscedastic Residuals | data.frame | 4000 | 6 |
| anger | ggm | Anger data | matrix | 4 | 4 |
| derived | ggm | Data on blood pressure body mass and age | list | | |
| glucose | ggm | Glucose control | data.frame | 68 | 8 |
| marks | ggm | Mathematics marks | data.frame | 88 | 5 |
| stress | ggm | Stress data | matrix | 4 | 4 |
| surdata | ggm | A simulated data set | data.frame | 600 | 4 |
| ECB_press_conferences | sentopics | Corpus of press conferences from the European Central Bank | corpus | | |
| ECB_press_conferences_tokens | sentopics | Tokenized press conferences | tokens | | |
| LoughranMcDonald | sentopics | Loughran-McDonald lexicon | dictionary2 | | |
| PicaultRenault | sentopics | Picault-Renault lexicon | data.table | 68826 | 9 |
| PicaultRenault_data | sentopics | Regression dataset based on Picault & Renault (2017) | xts | 238 | 7 |
| amlodipine | meta | Amlodipine for Work Capacity | data.frame | 8 | 7 |
| caffeine | meta | Caffeine for daytime drowsiness | data.frame | 8 | 12 |
| cisapride | meta | Cisapride in Non-Ulcer Dispepsia | data.frame | 13 | 5 |
| Fleiss1993bin | meta | Aspirin after Myocardial Infarction | data.frame | 7 | 6 |
| Fleiss1993cont | meta | Mental Health Treatment | data.frame | 5 | 8 |
| Fleiss93 | meta | Aspirin after Myocardial Infarction | data.frame | 7 | 6 |
| Fleiss93cont | meta | Mental Health Treatment | data.frame | 5 | 8 |
| lungcancer | meta | Smoking example | data.frame | 7 | 6 |
| Olkin1995 | meta | Thrombolytic Therapy after Acute Myocardial Infarction | data.frame | 70 | 6 |
| Olkin95 | meta | Thrombolytic Therapy after Acute Myocardial Infarction | data.frame | 70 | 6 |
| Pagliaro1992 | meta | Meta-analysis on Prevention of First Bleeding in Cirrhosis | data.frame | 28 | 8 |
| smoking | meta | Smoking example | data.frame | 7 | 6 |
| woodyplants | meta | Elevated CO_2 and total biomass of woody plants | data.frame | 102 | 10 |
| airm | riemtan | Pre-configured Riemannian metrics for SPD matrices | rmetric | | |
| bures_wasserstein | riemtan | Pre-configured Riemannian metrics for SPD matrices | rmetric | | |
| euclidean | riemtan | Pre-configured Riemannian metrics for SPD matrices | rmetric | | |
| log_cholesky | riemtan | Pre-configured Riemannian metrics for SPD matrices | rmetric | | |
| log_euclidean | riemtan | Pre-configured Riemannian metrics for SPD matrices | rmetric | | |
| i10_map_frame | icdpicr2 | Table i10_map_frame | spec_tbl_df | 20499 | 5 |
| i10_map_iciss | icdpicr2 | Table i10_map_iciss | spec_tbl_df | 20551 | 12 |
| i10_map_mech | icdpicr2 | Table i10_map_mech | spec_tbl_df | 8117 | 5 |
| i10_map_sev | icdpicr2 | Table i10_map_sev | spec_tbl_df | 20551 | 8 |
| testdata | icdpicr2 | Test Data | spec_tbl_df | 20000 | 13 |
| namcs2019sv | surveytable | Selected variables from the National Ambulatory Medical Care Survey (NAMCS) 2019 Public Use File (PUF) | survey.design2 | | |
| namcs2019sv_df | surveytable | Selected variables from the National Ambulatory Medical Care Survey (NAMCS) 2019 Public Use File (PUF) | data.frame | 8250 | 33 |
| nhis2024a | surveytable | National Health Interview Survey (NHIS) 2024 Public Use File (PUF) | survey.design2 | | |
| rccsu2018 | surveytable | National Study of Long-Term Care Providers (NSLTCP) Residential Care Community (RCC) Services User (SU) 2018 Public Use File (PUF) | survey.design2 | | |
| uspop_example | surveytable | US Population for use in examples | list | | |
| COelev | evgam | Colorado daily precipitation accumulations | list | | |
| COprcp | evgam | Colorado daily precipitation accumulations | data.frame | 404326 | 3 |
| COprcp_meta | evgam | Colorado daily precipitation accumulations | data.frame | 64 | 5 |
| FCtmax | evgam | Fort Collins, Colorado, US daily max. temperatures | data.frame | 18156 | 2 |
| fremantle | evgam | Annual Maximum Sea Levels at Fremantle, Western Australia | data.frame | 86 | 3 |
| alfalfa_soil | agridatasets | Alfalfa Soil Requirement for Land Evaluation | data.frame | 12 | 8 |
| apple_canker | agridatasets | Apple Canker Infestation of Apple Shoots | data.frame | 36 | 5 |
| apple_uniformity | agridatasets | Uniformity Trial in Apple | data.frame | 198 | 3 |
| arabica_soil | agridatasets | Arabica Coffee Soil Requirement for Land Evaluation | data.frame | 11 | 8 |
| arabica_temp | agridatasets | Arabica Coffee Temp Requirement for Land Evaluation | data.frame | 3 | 8 |
| arabica_terrain | agridatasets | Arabica Coffee Terrain Requirement for Land Evaluation | data.frame | 6 | 8 |
| arabica_water | agridatasets | Arabica Coffee Water Requirement for Land Evaluation | data.frame | 4 | 8 |
| avocado_us_sale | agridatasets | Hass Avocado Weekly US Sales | tbl_df | 810 | 11 |
| bamboo_growth | agridatasets | Bamboo Growth by Geographic Location | data.frame | 595 | 5 |
| biological_control | agridatasets | Biological Pest Control | data.frame | 15 | 2 |
| bird_grazing | agridatasets | Bird Abundance in Grazing Areas | data.frame | 62 | 3 |
| black_duck_survival | agridatasets | Survival of Black Ducks | data.frame | 50 | 6 |
| blackgrass_herbicide | agridatasets | Herbicide Efficacy | tbl_df | 135 | 7 |
| broiler_growth | agridatasets | Broiler Chicken Daily Measurements | data.frame | 59 | 6 |
| budworm_pyrethroid | agridatasets | Insecticide Doses and Tobacco Budworm | data.frame | 12 | 4 |
| carrot_fly_infestation | agridatasets | Carrot Fly Infestation of Carrots | data.frame | 96 | 5 |
| carrot_insecticide | agridatasets | Insecticide Treatments for Carrot Fly Larvae | data.frame | 36 | 6 |
| cattle_butterfat | agridatasets | Butterfat and Dairy Cattle | data.frame | 100 | 3 |
| cauliflower_growth | agridatasets | Leaves for Cauliflower Plants at Different Times | data.frame | 14 | 3 |
| coffee_composition | agridatasets | Arabica and Robusta Chemical Composition Samples | data.frame | 43 | 14 |
| coffee_production | agridatasets | World Coffee Production 2016 - 2017 | tbl_df | 47 | 3 |
| cork_tree_direction | agridatasets | Cork Samples Weight on Four Sides of Trees | data.frame | 112 | 3 |
| corn_hybrid_density | agridatasets | Factorial Experiment on Corn Hybrids | data.frame | 24 | 3 |
| cotton_pesticide | agridatasets | Cotton Response to Herbicide and Insecticide | tbl_df | 25 | 4 |
| cowpea_maize_yield | agridatasets | Intercropping Experiment of Maize and Cowpea | data.frame | 72 | 6 |
| cows_insemination | agridatasets | Crossbreeding of Cows | data.frame | 10 | 7 |
| earthworm_crop_soils | agridatasets | Earthworms in Cultivated Soils | data.frame | 12 | 5 |
| earthworm_population | agridatasets | Population Dynamics of Earthworms | data.frame | 46 | 3 |
| eelworm_fumigation | agridatasets | Eelworm Fumigant Treatments | data.frame | 48 | 10 |
| egg_weight_daily | agridatasets | Sample of Egg Weights on 24 Consecutive Days | data.frame | 240 | 2 |
| eucalyptus_progenies | agridatasets | #' Eucalyptus Grandis Progenies Height | tbl_df | 72 | 4 |
| fish_feeding | agridatasets | Food Consumption for Fish | data.frame | 33 | 6 |
| fungicide_latin_square | agridatasets | Latin Square Experiment for Testing Fungicide | data.frame | 25 | 4 |
| grape_uniformity | agridatasets | Uniformity Trial of Grape | data.frame | 155 | 3 |
| guinea_pig_sleep | agridatasets | Sleep Times for Guinea Pigs | data.frame | 30 | 2 |
| hawaii_plant_size | agridatasets | Maximum Plant Size in the Hawaiian Archipelago | data.frame | 58 | 6 |
| hawaii_tree_growth | agridatasets | Hawaii Tree Diameter Growth | data.frame | 63 | 8 |
| idn_rice_farms | agridatasets | Rice Production in Indonesia | data.frame | 1026 | 20 |
| kiwi_crop_design | agridatasets | Experimental Design of Kiwifruit Cover Crops | spec_tbl_df | 12 | 2 |
| ladybird_fungus | agridatasets | Ladybird Transmission of Fungus | tbl_df | 72 | 8 |
| lamb_births | agridatasets | Number of Lambs Born to 3 Breeds on 3 Farms | data.frame | 36 | 4 |
| nitrofen_toxicity | agridatasets | Toxicity of Nitrofen in Aquatic Systems | data.frame | 50 | 5 |
| orange_rootstocks | agridatasets | Orange Plant Growth Under Rootstocks | tbl_df | 27 | 3 |
| peach_uniformity | agridatasets | Uniformity Trial of Peach | data.frame | 144 | 3 |
| pig_weight_gain | agridatasets | Weight Gain in Pigs for Different Treatments | data.frame | 30 | 10 |
| plant_growth_regulator | agridatasets | Plant Heights in Glasshouse | tbl_df | 24 | 5 |
| pollen_removal | agridatasets | Pollen Removal | data.frame | 47 | 3 |
| potato_scab_sulfur | agridatasets | Potato Scab Infection with Sulfur Treatments | data.frame | 32 | 4 |
| rabbit_body_mass | agridatasets | Body Weight in Rabbits | data.frame | 141 | 2 |
| red_wine_quality | agridatasets | Red Wine Characteristics and Quality | tbl_df | 1599 | 12 |
| rice_wheat_production | agridatasets | Rice and Wheat Production | data.frame | 106 | 6 |
| river_deforestation | agridatasets | River Deforestation Temperatures | tbl_df | 56 | 3 |
| robusta_soil | agridatasets | Robusta Coffee Soil Requirement for Land Evaluation | data.frame | 10 | 8 |
| robusta_temp | agridatasets | Robusta Coffee Temp Requirement for Land Evaluation | data.frame | 3 | 8 |
| robusta_terrain | agridatasets | Robusta Coffee Terrain Requirement for Land Evaluation | data.frame | 5 | 8 |
| robusta_water | agridatasets | Robusta Coffee Water Requirement for Land Evaluation | data.frame | 4 | 8 |
| seed_germination | agridatasets | Seed Germination with Different Temperatures | data.frame | 64 | 5 |
| soil_munsell_colors | agridatasets | Traditional Soil Color Names | data.frame | 482 | 2 |
| soil_munsell_minerals | agridatasets | Common Soil Minerals Munsell Colors | data.frame | 20 | 5 |
| soybean_cultivars | agridatasets | Soybean Cultivars Grain Yield | data.frame | 40 | 3 |
| strawberry_cross_disease | agridatasets | Ordered Disease Ratings of Strawberry Crosses | data.frame | 144 | 5 |
| strawberry_yield | agridatasets | RCB Experiment of Strawberry | data.frame | 32 | 5 |
| timber_genetics | agridatasets | Timber Tree Genetic Characteristics | data.frame | 224 | 10 |
| tomato_insecticides | agridatasets | Commercial Insecticides Used in Tomato Pest Control | spec_tbl_df | 15 | 4 |
| tomato_uniformity | agridatasets | Uniformity Trial of Tomato | data.frame | 180 | 3 |
| toxin_lethal_dose | agridatasets | Crop Protection Product Lethal Dose | data.frame | 6 | 3 |
| turnip_density | agridatasets | Density and Spacing Experiment for Turnips | data.frame | 60 | 4 |
| us_state_soils | agridatasets | US State Soils | data.frame | 52 | 3 |
| wheat_bunt | agridatasets | Fungus Infection in Varieties of Wheat | data.frame | 400 | 4 |
| wheat_splitsplit | agridatasets | Split-Split-Plot Experiment of Wheat | data.frame | 160 | 9 |
| willow_cutting_yield | agridatasets | Effect of Type and Size of Cutting on Willow Yield | tbl_df | 25 | 6 |
| SGPstateData | SGP | State assessment program data from large scale state assessments for use with SGP package | environment | | |
| adult | rebmix | Adult Dataset | data.frame | 48842 | 16 |
| bearings | rebmix | Bearings Faults Detection Data | data.frame | 1906 | 14 |
| galaxy | rebmix | Galaxy Dataset | data.frame | 82 | 1 |
| iris | rebmix | Iris Data Set | data.frame | 150 | 5 |
| sensorlessdrive | rebmix | Sensorless Drive Faults Detection Data | data.frame | 58509 | 4 |
| steelplates | rebmix | Steel Plates Faults Recognition Data | data.frame | 1941 | 28 |
| truck | rebmix | Truck Dataset | data.frame | 31665 | 2 |
| weibull | rebmix | Weibull Dataset 8.1 | data.frame | 50 | 1 |
| weibullnormal | rebmix | Weibull-normal Simulated Dataset | data.frame | 10000 | 2 |
| wine | rebmix | Wine Recognition Data | data.frame | 178 | 14 |
| b3_real_estate | realestatebr | Real Estate Players listed on B3 | tbl_df | 39 | 5 |
| bcb_metadata | realestatebr | Brazilian Central Bank Series Metadata | tbl_df | 140 | 11 |
| dim_city | realestatebr | Brazilian city identifier table | tbl_df | 5570 | 9 |
| fsaverage | neuroatlas | Surface geometry for the fsaverage6 atlas | list | | |
| olsen_mtl | neuroatlas | Olsen Medial Temporal Lobe Atlas | olsen_mtl | | |
| bra23 | lapop | bra23: Single-country Single-year Dataset | data.frame | 1526 | 14 |
| cm23 | lapop | cm23: Single-country Multi-year Dataset | data.frame | 15764 | 10 |
| world | lapop | bra23: Single-country Single-year Dataset | sf | 241 | 3 |
| ym23 | lapop | ym23: Multi-country Single-year Dataset | tbl_df | 65566 | 15 |
| floods_rs | climateBR | Rainfall during the 2024 Rio Grande do Sul floods | tbl_df | 565 | 4 |
| inmet_stations | climateBR | INMET rainfall monitoring stations | grouped_df | 700 | 6 |
| mun_stations | climateBR | Nearest INMET Weather Stations for Brazilian Municipalities | tbl_df | 27850 | 5 |
| mun_stations_distance | climateBR | Distance Between Brazilian Municipal Centroids and INMET Rainfall Stations | data.frame | 501390 | 5 |
| municipality | climateBR | Municipality Database | data.frame | 5570 | 6 |
| rain_stations | climateBR | INMET rainfall monitoring stations by year | tbl_df | 9459 | 11 |
| DLBCL | ipred | Diffuse Large B-Cell Lymphoma | data.frame | 40 | 15 |
| dystrophy | ipred | Detection of muscular dystrophy carriers. | data.frame | 209 | 10 |
| GlaucomaMVF | ipred | Glaucoma Database | data.frame | 170 | 67 |
| Smoking | ipred | Smoking Styles | data.frame | 55 | 9 |
| Atom | FCPS | Atom introduced in [Ultsch, 2004]. | list | | |
| Chainlink | FCPS | Chainlink introduced in [Ultsch et al., 1994; Ultsch, 1995]. | list | | |
| dietary_survey_IBS | FCPS | Dietary survey IBS [Hayes et al., 2013] | list | | |
| EngyTime | FCPS | EngyTime introduced in [Baggenstoss, 2002]. | list | | |
| GolfBall | FCPS | GolfBall introduced in [Ultsch, 2005] | list | | |
| Hepta | FCPS | Hepta introduced in [Ultsch, 2003] | list | | |
| Leukemia | FCPS | Leukemia distance matrix and classificiation used in [Thrun, 2018] | list | | |
| Lsun | FCPS | | list | | |
| Lsun3D | FCPS | Lsun3D inspired by FCPS introduced in [Thrun, 2018] | list | | |
| Target | FCPS | Target introduced in [Ultsch, 2005]. | list | | |
| Tetra | FCPS | Tetra introduced in [Ultsch, 1993] | list | | |
| TwoDiamonds | FCPS | TwoDiamonds introduced in [Ultsch, 2003a, 2003b] | list | | |
| WingNut | FCPS | WingNut introduced in [Ultsch, 2005] | list | | |
| brfss | postlink | Behavioral Risk Factor Surveillance System (BRFSS) 2013 Subsample | data.frame | 2000 | 8 |
| LD1000 | postlink | Linked Dataset (n = 1000) for a Simple Illustration | tbl_df | 1000 | 10 |
| lifem | postlink | Longitudinal Intergenerational Family Electronic Micro-Database | data.frame | 3238 | 6 |
| cafes_tg | ggtaichi | Synthetic café orders: espresso vs. matcha | data.frame | 96 | 4 |
| pitts_emojis | ggtaichi | Popular Emojis | character | | |
| pitts_tg | ggtaichi | Pittsburgh COVID-related Google & Twitter incidence rates | tbl_df | 270 | 6 |
| states_tg | ggtaichi | States' COVID-related Google & Twitter incidence rates | tbl_df | 1116 | 6 |
| test_data | ambiR | AMBI test dataset | tbl_df | 53 | 4 |
| chile_election_2021 | fastei | Chilean 2021 First Round Presidential Election | data.frame | 46606 | 23 |
| Cornejo2025 | NPLStoolbox | Cornejo2025 longitudinal dataset measured in transgender persons | list | | |
| Jakobsen2025 | NPLStoolbox | Jakobsen2025 longitudinal dataset measured in mother-infant dyads | list | | |
| Georgiou2025 | CMTFtoolbox | Georgiou2025 Apical Periodontitis data | list | | |
| beetle | gllvm | ground beetle assemblages | list | | |
| eSpider | gllvm | Hunting spider data | list | | |
| fungi | gllvm | Wood-decaying fungi data | list | | |
| kelpforest | gllvm | Kelp Forest community Dynamics: Cover of sessile organisms, Uniform Point Contact | list | | |
| microbialdata | gllvm | Microbial community data | list | | |
| Skabbholmen | gllvm | Skabbholmen island data | list | | |
| mc_data_example_agg | myClim | Example data in Agg-format. | myClimList | | |
| mc_data_example_clean | myClim | Example cleaned data in Raw-format. | myClimList | | |
| mc_data_example_raw | myClim | Example data in Raw-format | myClimList | | |
| mc_data_formats | myClim | Formats of source data files | environment | | |
| mc_data_heights | myClim | Default heights of sensors | data.frame | 21 | 4 |
| mc_data_physical | myClim | Physical quantities definition | environment | | |
| mc_data_sensors | myClim | Sensors definition. | environment | | |
| mc_data_vwc_parameters | myClim | Volumetric water content parameters | data.frame | 13 | 9 |
| DEstd_dataset | FESta | DEstd_dataset | data.frame | 32 | 5 |
| GAMstd_dataset | FESta | GAMstd_dataset | data.frame | 4200 | 6 |
| GLMMstd_dataset | FESta | GLMMstd_dataset | data.frame | 1000 | 5 |
| GLMstd_dataset | FESta | GLMstd_dataset | data.frame | 1000 | 4 |
| MGMSstd_dataset | FESta | MGMSstd_dataset | data.frame | 375 | 4 |
| ORQGLMstd_dataset | FESta | ORQGLMstd_dataset | data.frame | 1000 | 4 |
| REstd_dataset | FESta | REstd_dataset | data.frame | 30 | 5 |
| StdEffort_dataset | FESta | StdEffort_dataset | list | | |
| SVstd_dataset | FESta | SVstd_dataset | data.frame | 1000 | 4 |
| antibiotics | AMR | Data Sets with 625 Antimicrobial Drugs | deprecated_amr_dataset | 505 | 14 |
| antimicrobials | AMR | Data Sets with 625 Antimicrobial Drugs | tbl_df | 505 | 14 |
| antivirals | AMR | Data Sets with 625 Antimicrobial Drugs | tbl_df | 120 | 11 |
| clinical_breakpoints | AMR | Data Set with Clinical Breakpoints for SIR Interpretation | tbl_df | 45735 | 14 |
| dosage | AMR | Data Set with Treatment Dosages as Defined by EUCAST | tbl_df | 759 | 9 |
| esbl_isolates | AMR | Data Set with 500 ESBL Isolates | tbl_df | 500 | 19 |
| example_isolates | AMR | Data Set with 2 000 Example Isolates | tbl_df | 2000 | 46 |
| example_isolates_unclean | AMR | Data Set with Unclean Data | tbl_df | 3000 | 8 |
| intrinsic_resistant | AMR | Data Set Denoting Bacterial Intrinsic Resistance | tbl_df | 294079 | 2 |
| microorganisms | AMR | Data Set with 96 982 Taxonomic Records of Microorganisms | tbl_df | 96982 | 28 |
| microorganisms.codes | AMR | Data Set with 6 029 Common Microorganism Codes | tbl_df | 6029 | 2 |
| microorganisms.groups | AMR | Data Set with 530 Microorganisms In Species Groups | tbl_df | 530 | 4 |
| WHONET | AMR | Data Set with 500 Isolates - WHONET Example | tbl_df | 500 | 53 |
| jordan | modsem | Jordan subset of PISA 2006 data | data.frame | 6038 | 15 |
| oneInt | modsem | oneInt | data.frame | 2000 | 9 |
| TPB | modsem | TPB | data.frame | 2000 | 15 |
| TPB_1SO | modsem | TPB_1SO | data.frame | 2000 | 16 |
| TPB_2SO | modsem | TPB_2SO | data.frame | 2000 | 14 |
| TPB_UK | modsem | TPB_UK | data.frame | 1169 | 20 |
| oneIntOrdered | plssem | oneIntOrdered | data.frame | 2000 | 9 |
| randomIntercepts | plssem | randomIntercepts | data.frame | 10000 | 9 |
| randomInterceptsOrdered | plssem | randomInterceptsOrdered | data.frame | 10000 | 9 |
| randomSlopes | plssem | randomSlopes | data.frame | 5000 | 13 |
| randomSlopesOrdered | plssem | randomSlopesOrdered | data.frame | 5000 | 13 |
| titanic | plssem | Titanic Passenger Survival Data Set. | data.frame | 1309 | 13 |
| TPB_Ordered | plssem | TPB_Ordered | data.frame | 2000 | 15 |
| x | Hiiragi2013 | Normalized Microarray Data | ExpressionSet | | |
| xq | Hiiragi2013 | qPCR Gene Expression | ExpressionSet | | |
| xql | Hiiragi2013 | Position-dependent Gene Expression | ExpressionSet | | |
| data_test_medmod | betaselectr | Test Dataset with Moderator and Mediator | data.frame | 200 | 6 |
| data_test_mod_cat | betaselectr | Test Dataset with Moderator and Categorical Variables | data.frame | 500 | 5 |
| data_test_mod_cat_binary | betaselectr | Test Dataset with a Binary Outcome Variable | data.frame | 300 | 5 |
| data_test_mod_cat2 | betaselectr | Test Dataset with Moderator and Categorical Variables (Version 2) | data.frame | 300 | 5 |
| g2g_data | vivainsights | Sample Group-to-Group dataset | spec_tbl_df | 150 | 11 |
| mt_data | vivainsights | Sample Meeting Query dataset | spec_tbl_df | 612 | 41 |
| p2p_data | vivainsights | Sample person-to-person dataset | tbl_df | 11550 | 13 |
| pq_data | vivainsights | Sample Person Query dataset | tbl_df | 6900 | 73 |
| halfsib | KLINK | Dataset for a case involving a putative half sibling. | list | | |
| paternity | KLINK | Dataset for a paternity case | list | | |
| sibship | KLINK | Dataset for a full vs half sibship case | list | | |
| covid19_sa | epichains | COVID-19 Confirmed Cases Data for South Africa | tbl_df | 14 | 2 |
| cm2001 | forecastdom | US Unemployment and Inflation (Clark & McCracken, 2001 setup) | data.frame | 937 | 3 |
| gw2006 | forecastdom | SPF Mean CPI Inflation Forecasts (Giacomini & White, 2006 setup) | data.frame | 180 | 10 |
| hl2005 | forecastdom | IBM Volatility Forecasts and Realized-Variance Proxies (Hansen & Lunde, 2005) | list | | |
| llq2022 | forecastdom | S&P 500 Realized-Variance Forecasts (Li, Liao & Quaedvlieg, 2022) | data.frame | 3071 | 9 |
| llq2022_jnj | forecastdom | Johnson & Johnson Realized-Variance Forecasts (Li, Liao & Quaedvlieg, 2022) | data.frame | 3179 | 9 |
| llq2022_uv_cspa | forecastdom | Pairwise CSPA Rejection Counts Across 28 Stocks | list | | |
| nrtz2014 | forecastdom | Equity Premium and Technical Indicators (Neely, Rapach, Tu, & Zhou, 2014) | data.frame | 733 | 16 |
| quaedvlieg2021 | forecastdom | Loss-Differential Path Forecasts from Quaedvlieg (2021) | list | | |
| rossi2006 | forecastdom | Bilateral Nominal Exchange Rates (Rossi, 2006) | data.frame | 1550 | 3 |
| rrz2016 | forecastdom | Equity Premium and Short Interest Index (Rapach, Ringgenberg & Zhou, 2016) | data.frame | 504 | 3 |
| rz2013 | forecastdom | Equity Premium and Macro Predictors (Rapach & Zhou, 2013) | data.frame | 1009 | 16 |
| wg2008 | forecastdom | Welch & Goyal (2008) Annual Equity-Premium Dataset | data.frame | 134 | 13 |
| blood_transfusion | SSLfmm | Semi-Synthetic Blood Transfusion Data | data.frame | 748 | 7 |
| fish | parameters | Sample data set | data.frame | 250 | 9 |
| qol_cancer | parameters | Sample data set | data.frame | 564 | 7 |
| efc | datawizard | Sample dataset from the EFC Survey | data.frame | 100 | 5 |
| nhanes_sample | datawizard | Sample dataset from the National Health and Nutrition Examination Survey | tbl_df | 2992 | 7 |
| disgust | bayestestR | Moral Disgust Judgment | data.frame | 150 | 2 |
| food_class | effectsize | Classification of Foods | table | 2 | 3 |
| hardlyworking | effectsize | Workers' Salary and Other Information | data.frame | 500 | 6 |
| Music_preferences | effectsize | Music Preference by College Major | table | 3 | 4 |
| Music_preferences2 | effectsize | Music Preference by College Major | table | 3 | 4 |
| preferences2025 | effectsize | Preferences of Poop vs Chocolate | data.frame | 489 | 3 |
| RCT_table | effectsize | Fictional Results from a Workers' Randomized Control Trial | table | 2 | 2 |
| rouder2016 | effectsize | Jeff Rouder's Example Dataset for Repeated Measures | data.frame | 2500 | 3 |
| screening_test | effectsize | Results from 2 Screening Tests | data.frame | 1600 | 3 |
| Smoking_FASD | effectsize | Frequency of FASD for Smoking Mothers | table | | |
| derappp | derappp | Data for environmental risk assessment of plant protection products | dm | | |
| derappp_bib | derappp | List of references | BibEntry | | |
| derappp_chents | derappp | List of chemical entities with additional information | derappp_chents | | |
| efc_insight | insight | Sample dataset from the EFC Survey | data.frame | 908 | 28 |
| fish | insight | Sample data set for count models | data.frame | 250 | 9 |
| EUR_ld.blocks19 | RapidoPGS | LD block architecture for European populations (hg19). | GRanges | | |
| EUR_ld.blocks38 | RapidoPGS | LD block architecture for European populations (hg38). | GRanges | | |
| michailidou19 | RapidoPGS | Subset of Michailidou BRCA GWAS sumstat dataset. | data.table | 100000 | 10 |
| michailidou38 | RapidoPGS | Subset of Michailidou BRCA GWAS sumstat dataset. | data.table | 100000 | 10 |
| dictionary_afltables | fitzRoy | AFL Tables Data Dictionary: AFL Tables Column Formats | data.frame | 81 | 2 |
| mapping_afltables | fitzRoy | AFL Tables Mapping: AFL Tables Naming Convention Mapping | character | | |
| BeijingAir | HDTSA | Beijing multi-site air pollution tensor data | array | | |
| FamaFrench | HDTSA | Fama-French 10*10 return series | data.frame | 696 | 102 |
| IPindices | HDTSA | U.S. Industrial Production indices | data.frame | 924 | 8 |
| QWIdata | HDTSA | The national QWI hires data | list | | |
| lipid_screen | SVEMnet | Lipid formulation screening data | data.frame | 23 | 13 |
| grunfeld | sdim | Grunfeld (1958) investment dataset | data.frame | 220 | 5 |
| he2023_dacheng202 | sdim | Dacheng 202-portfolio value-weighted returns from He, Huang, Li, Zhou (2023) | data.frame | 552 | 203 |
| he2023_factors | sdim | Factor proxies from He, Huang, Li, Zhou (2023) | data.frame | 516 | 71 |
| he2023_ff17vw | sdim | Fama-French 17-industry value-weighted portfolios from He, Huang, Li, Zhou (2023) | data.frame | 528 | 18 |
| he2023_ff30vw | sdim | Fama-French 30-industry value-weighted portfolios from He, Huang, Li, Zhou (2023) | data.frame | 528 | 31 |
| he2023_ff48ew | sdim | Fama-French 48-industry equal-weighted portfolios from He, Huang, Li, Zhou (2023) | data.frame | 528 | 49 |
| he2023_ff48vw | sdim | Fama-French 48-industry value-weighted portfolios from He, Huang, Li, Zhou (2023) | data.frame | 528 | 49 |
| he2023_ff5 | sdim | Fama-French 5-factor data from He, Huang, Li, Zhou (2023) | data.frame | 652 | 9 |
| huang2022_ip | sdim | Industrial production growth from Huang, Jiang, Li, Tong, Zhou (2022) | numeric | | |
| huang2022_macro | sdim | FRED-MD macro predictors from Huang, Jiang, Li, Tong, Zhou (2022) | matrix | 720 | 123 |
| Alcohol_data | fitODBOD | Alcohol data | data.frame | 8 | 3 |
| Chromosome_data | fitODBOD | Chromosome Data | data.frame | 4 | 2 |
| Course_data | fitODBOD | Course Data | data.frame | 9 | 2 |
| Epidemic_Cold | fitODBOD | Family Epidemics | data.frame | 5 | 6 |
| Exam_data | fitODBOD | Exam Data | data.frame | 10 | 2 |
| Male_Children | fitODBOD | Male children data | data.frame | 13 | 2 |
| Plant_DiseaseData | fitODBOD | Plant Disease Incidence data | data.frame | 10 | 2 |
| Terror_data_ARG | fitODBOD | Terror Data ARG | data.frame | 7 | 2 |
| Terror_data_USA | fitODBOD | Terror Data USA | data.frame | 6 | 2 |
| data | PEAXAI | Simulated efficiency dataset (100 DMUs) | data.frame | 100 | 3 |
| data_example | PEAXAI | Simulated efficiency dataset (100 DMUs) | data.frame | 100 | 3 |
| data_SABI | PEAXAI | Spanish Food Industry Firms Dataset | data.frame | 1282 | 4 |
| firms | PEAXAI | Spanish Food Industry Firms Dataset | data.frame | 917 | 6 |
| brewer | TreeTools | Brewer palettes | list | | |
| Lobo.data | TreeTools | Data from Zhang et al. 2016 | list | | |
| Lobo.phy | TreeTools | Data from Zhang et al. 2016 | phyDat | | |
| nRootedShapes | TreeTools | Number of rooted / unrooted tree shapes | integer64 | | |
| nUnrootedShapes | TreeTools | Number of rooted / unrooted tree shapes | integer64 | | |
| unrootedKeys | TreeTools | Integer representing shape of a tree | list | | |
| pigfarms | pgt | Pig-finishing farms from Rodseth (2025), Table 1 | data.frame | 5 | 9 |
| steeldemo | pgt | Synthetic steel plant panel | data.frame | 180 | 9 |
| uscoal | pgt | US coal-fired power plants with a measured SO2 account, 2022 | data.frame | 212 | 11 |
| bmd | lava | Longitudinal Bone Mineral Density Data (Wide format) | data.frame | 112 | 7 |
| bmidata | lava | Data | data.frame | 552 | 10 |
| brisa | lava | Simulated data | data.frame | 500 | 21 |
| calcium | lava | Longitudinal Bone Mineral Density Data | data.frame | 501 | 6 |
| deprdiag | lava | 50 patients from Monash Medical Centre, Melbourne | data.frame | 7 | 1 |
| hubble | lava | Hubble data | data.frame | 36 | 3 |
| hubble2 | lava | Hubble data | data.frame | 24 | 3 |
| indoorenv | lava | Data | data.frame | 200 | 13 |
| missingdata | lava | Missing data example | list | | |
| nldata | lava | Example data (nonlinear model) | data.frame | 50 | 3 |
| nsem | lava | Example SEM data (nonlinear) | data.frame | 500 | 7 |
| semdata | lava | Example SEM data | data.frame | 500 | 21 |
| serotonin | lava | Serotonin data | data.frame | 250 | 20 |
| twindata | lava | Twin menarche data | data.frame | 4000 | 7 |
| Alps | envar | Alpine region | sf | 1 | 27 |
| Apollo | envar | Apollo butterfly occurrences | data.frame | 2648 | 2 |
| Europe | envar | Europe | sf | 1 | 53 |
| ela1 | tirt | Mixed-Format English Language Arts (ELA) Assessment Data (Form 1) | data.frame | 52417 | 47 |
| ela2 | tirt | Mixed-Format English Language Arts (ELA) Assessment Data (Form 2) | data.frame | 2348 | 10 |
| ela3 | tirt | Large-Scale Mixed-Format English Language Arts (ELA) Assessment Data (Form 3) | data.frame | 2434185 | 5 |
| ela3_testmap | tirt | Large-Scale Mixed-Format English Language Arts (ELA) Assessment Data Testmap (Form 3 Testmap) | data.frame | 328 | 5 |
| events_SISe3 | SimInf | Example event data for the SISe3 model with cattle herds | data.frame | 783773 | 8 |
| nodes | SimInf | Example data with spatial distribution of nodes | data.frame | 1600 | 2 |
| u0_SISe3 | SimInf | Example initial population data for the SISe3 model | data.frame | 1600 | 6 |
| data_by_type | RIFanalysis | Example RIF datasets with a type variable | list | | |
| data_no_type | RIFanalysis | Example RIF datasets without a type variable | list | | |
| badData | Pmetrics | Pmetrics data file with errors | PM_data | | |
| cdc_bmi | Pmetrics | CDC Pediatric and Adolescent BMI Table | spec_tbl_df | 438 | 15 |
| dataEx | Pmetrics | Pmetrics data file | PM_data | | |
| ger_bmi | Pmetrics | CDC Pediatric and Adolescent BMI Table | spec_tbl_df | 74 | 16 |
| growth | Pmetrics | CDC Pediatric and Adolescent Growth Data Table | data.frame | 308 | 9 |
| locales | Pmetrics | Pmetrics locales | spec_tbl_df | 183 | 3 |
| mic1 | Pmetrics | Example MIC data | spec_tbl_df | 19 | 2 |
| modEx | Pmetrics | Pmetrics model object | PM_model | | |
| NPex | Pmetrics | Example NPAG Output with validation | PM_result | | |
| simEx | Pmetrics | Example simulator output | PM_sim | | |
| data_cr | mvord | Simulated credit ratings | data.frame | 690 | 10 |
| data_cr_panel | mvord | Simulated panel of credit ratings | data.frame | 11320 | 9 |
| data_mvord | mvord | Simulated credit ratings | data.frame | 3000 | 9 |
| data_mvord_panel | mvord | Simulated panel of credit ratings | data.frame | 10000 | 9 |
| data_mvord_toy | mvord | Data set toy example | data.frame | 100 | 6 |
| data_mvord2 | mvord | Simulated credit ratings | data.frame | 1000 | 10 |
| essay_data | mvord | Essay data | data.frame | 198 | 6 |
| dataConstr | DVHmetrics | Constraint data frame | data.frame | 6 | 3 |
| dataMZ | DVHmetrics | DVH data from 3 patients | DVHLstLst | | |
| akan | cofad | Data from Akan et al. (2018), experiment 2B | tbl_df | 270 | 3 |
| furr_p4 | cofad | Empathy data set by Furr (2004) | data.frame | 20 | 2 |
| haans_within1by4 | cofad | Haans within data example | tbl_df | 20 | 3 |
| maraver | cofad | Data from Maraver et al. (2021) | tbl_df | 120 | 3 |
| rosenthal_chap5_q2 | cofad | Complexity data set by Rosenthal and Rosnow (2000) | data.frame | 18 | 4 |
| rosenthal_p141 | cofad | Data set by Rosenthal and Rosnow (2000) | data.frame | 14 | 4 |
| rosenthal_tbl31 | cofad | Data set by Rosenthal and Rosnow (2000) | data.frame | 20 | 2 |
| rosenthal_tbl53 | cofad | Children data set by Rosenthal and Rosnow (2000) | data.frame | 36 | 4 |
| rosenthal_tbl54 | cofad | Response-profile data by Rosenthal et al. (2000) | data.frame | 8 | 3 |
| rosenthal_tbl59 | cofad | Therapy data set by Rosenthal and Rosnow (2000) | data.frame | 12 | 4 |
| rosenthal_tbl68 | cofad | Data set by Rosenthal and Rosnow (2000) | data.frame | 32 | 4 |
| rosenthal_tbl68_mixed | cofad | Corrected mixed-design Table 6.8 data by Rosenthal et al. (2000) | data.frame | 32 | 4 |
| schwoebel | cofad | Data from Schwoebel et al. (2018) | tbl_df | 64 | 2 |
| sedlmeier_p525 | cofad | Problem solving data set by Sedlmeier & Renkewitz (2018) | data.frame | 15 | 3 |
| sedlmeier_p537 | cofad | Music data set by Sedlmeier & Renkewitz (2018) | data.frame | 32 | 3 |
| testing_effect | cofad | Testing Effect data | data.frame | 60 | 3 |
| bundData | NMOF | German Government Bond Data | list | | |
| fundData | NMOF | Mutual Fund Returns | matrix | 500 | |
| optionData | NMOF | Option Data | list | | |
| nice | ulrb | V4-V5 16S rRNA gene amplicons, clean OTU table (N-ICE, 2015) | data.frame | 524 | 17 |
| nice_env | ulrb | Metadata of samples from OTU tables (N-ICE, 2015) | data.frame | 9 | 8 |
| nice_raw | ulrb | V4-V5 16S rRNA gene amplicons, raw OTU table (N-ICE, 2015) | data.frame | 1003 | 19 |
| nice_tidy | ulrb | V4-V5 16S rRNA gene amplicons, clean OTU table in tidy/long format (N-ICE, 2015) | tbl_df | 4716 | 10 |
| emudata | remulator | emudata | magpie | | |
| luqueries | luscale | luqueries | list | | |
| pest_thresholds | TrackTrap | Pest Developmental Thresholds | data.frame | 90 | 5 |
| admiral_adrs | admiralonco | Response Analysis Dataset | tbl_df | 3694 | 75 |
| GTS2012 | palaeoverse | Geological Timescale 2012 | data.frame | 186 | 10 |
| GTS2020 | palaeoverse | Geological Timescale 2020 | data.frame | 189 | 10 |
| interval_key | palaeoverse | Example dataset: Interval key for the look_up function | data.frame | 1323 | 3 |
| reefs | palaeoverse | Example dataset: Phanerozoic reefs from the PaleoReefs Database | data.frame | 4363 | 14 |
| tetrapods | palaeoverse | Example dataset: Early tetrapod data from the Paleobiology Database | data.frame | 5270 | 32 |
| acaule | dismo | Solanum acaule data | data.frame | 1366 | 25 |
| Anguilla_grids | dismo | Anguilla australis distribution data | RasterBrick | | |
| Anguilla_test | dismo | Anguilla australis distribution data | data.frame | 500 | 13 |
| Anguilla_train | dismo | Anguilla australis distribution data | data.frame | 1000 | 14 |
| ECOcrops | dismo | Ecocrop model | data.frame | 1710 | 35 |
| Anderson | rcompanion | Hypothetical data for Alexander Anderson | matrix | 4 | 2 |
| AndersonBias | rcompanion | Hypothetical data for Alexander Anderson with gender bias | data.frame | 16 | 4 |
| AndersonRainBarrel | rcompanion | Hypothetical data for Alexander Anderson on rain barrel installation | matrix | 2 | 2 |
| AndersonRainGarden | rcompanion | Hypothetical data for Alexander Anderson on rain garden installation | matrix | 3 | 3 |
| BobBelcher | rcompanion | Hypothetical data for ratings of instructors in unreplicated CBD | data.frame | 40 | 3 |
| Breakfast | rcompanion | Hypothetical data for students' breakfast habits and travel to school | table | 3 | 5 |
| BrendonSmall | rcompanion | Hypothetical data for Brendon Small and company | data.frame | 45 | 6 |
| BullyHill | rcompanion | Hypothetical data for proportion of students passing a certification | data.frame | 12 | 5 |
| Catbus | rcompanion | Hypothetical data for Catbus and company | data.frame | 26 | 5 |
| HayleySmith | rcompanion | Hypothetical data for responses about adopting lawn care practices | data.frame | 56 | 3 |
| Monarchs | rcompanion | Hypothetical data for monarch butterflies in gardens | data.frame | 24 | 2 |
| Nurseries | rcompanion | Data for proportion of good practices followed by plant nuseries | data.frame | 38 | 2 |
| Pennsylvania18 | rcompanion | Votes for the Democratic candidate in Pennsylvania 18 in 2016 and 2018 | matrix | 2 | 2 |
| Pooh | rcompanion | Hypothetical data for paired ratings of Pooh Bear | data.frame | 20 | 4 |
| PoohPiglet | rcompanion | Hypothetical data for ratings of Pooh, Piglet, and Tigger | data.frame | 30 | 2 |
| Religion | rcompanion | Hypothetical data for change in religion after a caucusing event | matrix | 4 | 4 |
| site_sp_birds | fundiversity | Site-species matrix of birds along a Tropical Gradient | matrix | 8 | 217 |
| site_sp_plants | fundiversity | Site-species matrix of plants along a Tropical Gradient | matrix | 10 | 392 |
| traits_birds | fundiversity | Functional Traits of Frugivorous Birds along a Tropical Gradient | matrix | 217 | 4 |
| traits_plants | fundiversity | Functional Traits of Fleshy-fruit plants along a Tropical Gradient | matrix | 392 | 4 |
| pasilla | tidySummarizedExperiment | Read counts of RNA-seq samples of Pasilla knock-down by Brooks et al. | SummarizedExperiment | | |
| se | tidySummarizedExperiment | Read counts of RNA-seq samples derived from Pasilla knock-down by Brooks et al. | RangedSummarizedExperiment | | |
| biosemi128 | diegr | Coordinates of the spherical BioSemi system with 128 electrodes | list | | |
| biosemi256 | diegr | Coordinates of the spherical BioSemi system with 256 electrodes | list | | |
| epochdata | diegr | Example high-density (HD-EEG) epoched data | data.frame | 295800 | 5 |
| HCGSN256 | diegr | Coordinates of 256-channel HCGSN sensors | list | | |
| rtdata | diegr | Example response time data | data.frame | 29 | 3 |
| system1005 | diegr | Coordinates of the spherical 10-05 system sensors | list | | |
| berkeley | bsitar | Berkeley Child Guidance Study Data | tbl_df | 2883 | 10 |
| berkeley_exdata | bsitar | Berkeley Child Guidance Study Data for Females | data.frame | 770 | 3 |
| berkeley_exfit | bsitar | Model Fit to the Berkeley Child Guidance Study Data for Females | brmsfit | | |
| Anime | cSEM | Data: Anime | data.frame | 183 | 13 |
| Benitezetal2020 | cSEM | Data: Benitezetal2020 | data.frame | 300 | 22 |
| BergamiBagozzi2000 | cSEM | Data: BergamiBagozzi2000 | data.frame | 305 | 22 |
| corp_rep_data | cSEM | Data: corp_rep_data | data.frame | 344 | 41 |
| dgp_2ndorder_cf_of_c | cSEM | Data: Second order common factor of composites | matrix | 500 | 20 |
| ITFlex | cSEM | Data: ITFlex | data.frame | 100 | 16 |
| LancelotMiltgenetal2016 | cSEM | Data: LancelotMiltgenetal2016 | data.frame | 1090 | 11 |
| LeDang2022 | cSEM | Data: LeDang2022 | data.frame | 773 | 107 |
| PoliticalDemocracy | cSEM | Data: political democracy | data.frame | 75 | 11 |
| Russett | cSEM | Data: Russett | data.frame | 47 | 10 |
| satisfaction | cSEM | Data: satisfaction | data.frame | 250 | 27 |
| satisfaction_gender | cSEM | Data: satisfaction including gender | data.frame | 250 | 28 |
| Sigma_Summers_composites | cSEM | Data: Summers | matrix | 18 | 18 |
| SQ | cSEM | Data: SQ | data.frame | 411 | 23 |
| Switching | cSEM | Data: Switching | data.frame | 767 | 26 |
| threecommonfactors | cSEM | Data: threecommonfactors | matrix | 500 | 9 |
| Yooetal2000 | cSEM | Data: Yooetal2000 | data.frame | 569 | 34 |
| adirondack_sites | lakefetch | Adirondack Lake Sampling Sites | data.frame | 12 | 5 |
| example_lake | lakefetch | Example Circular Lake Polygon | sf | 1 | 4 |
| wisconsin_lakes | lakefetch | Wisconsin Lake Sampling Sites | data.frame | 8 | 4 |
| gabriel1971 | bpca | Percentages of households having various facilities and appliances in East Jerusalem Arab areas, by quarters of the town | matrix | 8 | 9 |
| gge2003 | bpca | A didactic matrix of genotypes (rows) and environments (columns) | matrix | 4 | 3 |
| marina | bpca | Films shown at five festivals in Brazil from 2007 to 2011 | data.frame | 25 | 6 |
| ontario | bpca | Ontario winter wheat (1993) | data.frame | 18 | 9 |
| intcal20 | nimbleCarbon | IntCal20 radiocarbon age calibration curve for the Northern hemisphere. | data.frame | 9501 | 5 |
| marine20 | nimbleCarbon | Marine20 radiocarbon age calibration curve. | data.frame | 5501 | 5 |
| shcal20 | nimbleCarbon | IntCal20 radiocarbon age calibration curve for the Southern hemisphere. | data.frame | 9501 | 5 |
| Eng | smoppix | Spatial transcriptomics data of mouse fibroblast cells | data.frame | 100000 | 5 |
| EngRois | smoppix | Spatial transcriptomics data of mouse fibroblast cells | list | | |
| Yang | smoppix | Spatial transcriptomics data of Selaginella moellendorffii roots | data.frame | 150432 | 6 |
| cdata | FastJM | Simulated competing risks data correlated with ydata | data.frame | 1000 | 7 |
| cdatah | FastJM | Simulated competing risks data where event hazards depend on within-subject variance | data.frame | 200 | 6 |
| mvcdata | FastJM | Simulated competing risks data correlated with mvydata | data.frame | 800 | 5 |
| mvydata | FastJM | Simulated bivariate longitudinal data | data.frame | 5645 | 6 |
| ydata | FastJM | Simulated longitudinal data | data.frame | 3067 | 6 |
| ydatah | FastJM | Simulated longitudinal data with within-subject variance | data.frame | 1353 | 6 |
| data1 | multimediate | Toy dataset 1 | data.frame | 1000 | 8 |
| data2 | multimediate | Toy dataset 2 | data.frame | 1000 | 8 |
| data3 | multimediate | Toy dataset 3 | data.frame | 1000 | 8 |
| data4 | multimediate | Toy dataset 4 | data.frame | 1000 | 8 |
| data5 | multimediate | Toy dataset 5 | data.frame | 3000 | 6 |
| decay_data | RNAdecay | Normalized RNA abundance RNA decay timecourse | data.frame | 15104 | 5 |
| models | RNAdecay | Example double exponential decay modeling results | list | | |
| results | RNAdecay | Example double exponential decay modeling results | data.frame | 118 | 21 |
| RPMs | RNAdecay | RNA abundance reads per million over RNA decay timecourse | data.frame | 118 | 128 |
| obj_toy | CellMentor | Tiny prebuilt CSFNMF object for accessors (optional) | csfnmf | | |
| qry_matrix_toy | CellMentor | Tiny toy matrices and labels for runnable examples | matrix | 50 | 8 |
| ref_celltype_toy | CellMentor | Tiny toy matrices and labels for runnable examples | character | | |
| ref_matrix_toy | CellMentor | Tiny toy matrices and labels for runnable examples | matrix | 50 | 12 |
| beverage | BayesRTMB | Beverage Preference Data | data.frame | 60 | 7 |
| BigFive | BayesRTMB | Big Five Personality Traits Data | data.frame | 170 | 20 |
| debate | BayesRTMB | Debate Simulation Data | data.frame | 300 | 6 |
| training | BayesRTMB | Social Skills Training Data | data.frame | 12 | 8 |
| mvb_teams | ncaavolleyballr | NCAA Men's Volleyball Teams 2020-2024 | data.frame | 1071 | 6 |
| ncaa_conferences | ncaavolleyballr | NCAA Conference Names | character | | |
| ncaa_sports | ncaavolleyballr | NCAA Sports and Sport Codes | data.frame | 100 | 2 |
| ncaa_teams | ncaavolleyballr | NCAA Team Names | character | | |
| wvb_teams | ncaavolleyballr | NCAA Women's Volleyball Teams 2020-2024 | data.frame | 6344 | 6 |
| CHOL_DEGs | easybio | Example DEGs data from Limma-Voom workflow for TCGA-CHOL project | data.table | 21938 | 23 |
| pbmc.markers | easybio | Example marker data from Seurat::FindAllMarkers() | data.frame | 11629 | 7 |
| emotions | transforEmotion | Emotions Data | data.frame | 175592 | 9 |
| neo_ipip_extraversion | transforEmotion | NEO-PI-R IPIP Extraversion Item Descriptions | list | | |
| stop_words | transforEmotion | Stop Words from the _tm_ Package | character | | |
| tinytrolls | transforEmotion | Russian Trolls Data - Small Version | data.frame | 22143 | 6 |
| strokeCTdensity | WRI | Stroke data: clinical, radiological scalar variables and density curves of the hematoma of 393 stroke patients | list | | |
| math_example | LVGP | Dataset for the example in function 'LVGP_fit' | list | | |
| au_player_data | provolleyballr | Athletes Unlimited Individual Player Statistics | tbl_df | 220 | 25 |
| lovb_player_data | provolleyballr | League One Volleyball Individual Player Statistics | data.frame | 94 | 27 |
| lovb_team_data | provolleyballr | League One Volleyball Team Match Statistics | data.frame | 96 | 24 |
| lovb_teams | provolleyballr | League One Volleyball Teams | data.frame | 7 | 3 |
| mlv_player_data | provolleyballr | Major League Volleyball Individual Player Statistics | tbl_df | 262 | 23 |
| mlv_team_data | provolleyballr | Major League Volleyball Team Match Statistics | tbl_df | 392 | 15 |
| mlv_teams | provolleyballr | Major League Volleyball Teams | data.frame | 9 | 5 |
| bushfire | modi | Bushfire scars. | data.frame | 38 | 5 |
| bushfire.weights | modi | Weights for Bushfire scars. | numeric | | |
| bushfirem | modi | Bushfire scars with missing data. | data.frame | 38 | 5 |
| lival | modi | Living Standards Measurement Survey Albania 2012 | data.frame | 6671 | 26 |
| sepe | modi | Sample Environment Protection Expenditure Survey. | data.frame | 675 | 23 |
| atp_2019 | welo | ATP matches in 2019 | data.frame | 2610 | 36 |
| wta_2019 | welo | WTA matches in 2019 | data.frame | 2472 | 32 |
| example_scan | bioRad | Scan ('scan') example | scan | | |
| example_vp | bioRad | Vertical profile ('vp') example | vp | | |
| example_vpts | bioRad | Time series of vertical profiles ('vpts') example | vpts | | |
| vpts_schema | bioRad | ENRAM-defined VPTS schema | list | | |
| ibmspko | MTS | Monthly simple returns of the stocks of International Business Machines (IBM) and Coca Cola (KO) and the S&P Composite index (SP) | data.frame | 612 | 4 |
| qgdp | MTS | Quarterly real gross domestic products of United Kingdom, Canada, and the United States | data.frame | 126 | 5 |
| tenstocks | MTS | Monthly simple returns of ten U.S. stocks | data.frame | 132 | 11 |
| BigLucyT0T1 | samplesize4surveys | Some Business Population Database for two periods of time | data.frame | 170592 | 16 |
| memory_exp | RMediation | Memory Experiment Data Description from MacKinnon et al., 2018 | tbl_df | 369 | 5 |
| master_est | baRulho | Extended selection table of master acoustic data | extended_selection_table | 7 | 7 |
| test_sounds_est | baRulho | Extended selection table with re-recorded playbacks | extended_selection_table | 25 | 9 |
| bacteria | qqtest | Bacteria from Delaware River water entering the Torresdale Filter of the Philadelphia water supply 1913. | data.frame | 22 | 2 |
| penicillin | qqtest | 31 contrast sums from a 32 run 2^(5-0) factorial experiment on penicillin production. | data.frame | 31 | 1 |
| primer | qqtest | Automobile primer paint thickness quality control measurements. | data.frame | 20 | 14 |
| pullstrength | qqtest | Strength of pull for 519 males aged 23-26. | data.frame | 7 | 5 |
| sittingHeights | qqtest | Sitting height in inches of female adults (aged 23-50). | data.frame | 9 | 8 |
| stacklossDistances | qqtest | Mahalanobis squared distances of Brownlee's stack loss plant operation data based only on the explanatory variates (air flow, water temperature, and acid concentration). | data.frame | 21 | 2 |
| WachusettReservoir | qqtest | Storage, in millions of gallons daily per square mile of net land area, at the Wachusett Reservoir in Massacusetts - storage computed for each of several rates of draft (draft being a determined maintainable flow in 1,000s of gallons per square mile daily). | data.frame | 15 | 6 |
| california_housing | ppforest2 | California Housing Dataset | data.frame | 20433 | 9 |
| crab | ppforest2 | Australian Crabs Dataset | data.frame | 200 | 6 |
| crabs | ppforest2 | Crabs Dataset | data.frame | 200 | 7 |
| fishcatch | ppforest2 | Fish Catch Dataset | data.frame | 159 | 7 |
| glass | ppforest2 | Glass Dataset | data.frame | 214 | 10 |
| image | ppforest2 | Image Dataset | data.frame | 2310 | 19 |
| leukemia | ppforest2 | Leukemia Dataset | data.frame | 72 | 41 |
| lymphoma | ppforest2 | Lymphoma Dataset | data.frame | 80 | 51 |
| NCI60 | ppforest2 | NCI60 Dataset | data.frame | 61 | 31 |
| olive | ppforest2 | Olive Dataset | data.frame | 572 | 9 |
| parkinson | ppforest2 | Parkinson Dataset | data.frame | 195 | 23 |
| wine | ppforest2 | Wine Dataset | data.frame | 178 | 14 |
| production | paneldesc | Simulated Unbalanced Panel Data for Cobb-Douglas Production Function Analysis | data.frame | 180 | 8 |
| bikes | fmeffects | Regression data of the usage of rental bikes in Washington D.C., USA | data.frame | 731 | 10 |
| BigCity | TeachingSampling | Full Person-level Population Database | data.frame | 150266 | 12 |
| BigLucy | TeachingSampling | Full Business Population Database (Extended) | data.frame | 85296 | 11 |
| Lucy | TeachingSampling | Full Business Population Database | data.frame | 2396 | 8 |
| sampleObj | GeneNMF | Sample dataset to test GeneNMF installation | Seurat | | |
| clipboard_sample | screenshot | Sample data of clipboard bitmap array | raw | | |
| Arizona | twoway | Mean monthly temperatures in Arizona | matrix | 7 | 3 |
| drugs | twoway | Scores for 5 subjects after being given each of 4 drugs | data.frame | 5 | 4 |
| hstart | twoway | Number of U.S. housing starts by month for the years 1965 - 1973 | matrix | 9 | 12 |
| insectCounts | twoway | Counts of an insect for the combinations of 4 treatments and 6 areas of a field | matrix | 4 | 6 |
| Rubber | twoway | Compressibility of Rubber | matrix | 4 | 6 |
| sentRT | twoway | Reaction times for T/F judgments | matrix | 3 | 3 |
| taskRT | twoway | Data on reaction times for various tasks and topics | matrix | 3 | 4 |
| VermontPop | twoway | Vermont country populations from the US Census, 1900-1990 | data.frame | 14 | 10 |
| coastal_flooding | profExtrema | Coastal flooding as function of offshore forcing conditions | data.frame | 200 | 6 |
| eLTER_data_reporting_format | ReLTER | eLTER Data Reporting Format (DRF) | tbl_df | 122 | 4 |
| Hearingloss_N109 | BIDistances | Hearingloss data | list | | |
| Hepta | BIDistances | Hepta introduced in [Ultsch, 2003] | list | | |
| SquaredSquare | ResistorArray | A Squared square | matrix | 13 | |
| ecoffs | MIC | ECOFF data | tbl_df | 85 | 25 |
| example_mics | MIC | Example MIC data | data.frame | 300 | 4 |
| glm_data | RobinCar2 | Example Trial Data for GLMs with Permute-Block Randomization | tbl_df | 600 | 7 |
| surv_data | RobinCar2 | Survival Example Data | data.frame | 228 | 12 |
| eg_burden_template | vimcheck | Example of VIMC burden template provided to modellers | tbl_df | 20402 | 11 |
| eg_coverage | vimcheck | Example of scenario coverage data | spec_tbl_df | 11 | 19 |
| eg_fvps | vimcheck | Example of FVP estimate data | spec_tbl_df | 11 | 24 |
| eg_fvps_2 | vimcheck | A second FVP data example | tbl_df | 4 | 7 |
| eg_impact | vimcheck | Example of impact data | tbl_df | 5396 | 9 |
| eg_impact_2 | vimcheck | Second example of impact data | tbl_df | 4 | 6 |
| eg_wpp | vimcheck | Example of UN-WPP time-series data | tbl_df | 65448 | 5 |
| who_subregions | vimcheck | Data on countries in WHO subregions | spec_tbl_df | 249 | 9 |
| acoustic | sparseFLMM | Phonetics acoustic data (complete) | data.table | 24830 | 11 |
| acoustic_subset | sparseFLMM | Phonetics acoustic data (subset) | data.table | 656 | 11 |
| EWMaleData | StMoMo | England and Wales male mortality data | StMoMoData | | |
| distance_results | STDistance | distance_results | data.frame | 18 | 5 |
| metadata | STDistance | Metadata | data.frame | 293 | 9 |
| posi | STDistance | posi | data.frame | 293 | 18 |
| tissue_posi | STDistance | tissue_posi | data.frame | 9984 | 9 |
| tissue_posi_normalized | STDistance | tissue_posi_normalized | tbl_df | 9984 | 9 |
| interval_table | sepkoski | Interval table for linking and standardising intervals | data.frame | 302 | 4 |
| sepkoski | sepkoski | Sepkoski's marine animal genera compendium | data.frame | 35826 | 9 |
| sepkoski_raw | sepkoski | Sepkoski's marine animal genera compendium (raw) | data.frame | 35826 | 8 |
| m4_data | echos | Deprecated name for 'm4_monthly_subset' | tbl_ts | 1152 | 4 |
| m4_monthly_subset | echos | Monthly subset of the M4 competition data | tbl_ts | 1152 | 4 |
| synthetic_data | echos | Synthetic time series data | tbl_df | 2000 | 3 |
| metrics | miQC | Basic scRNA-seq QC metrics from an ovarian tumor | DFrame | | |
| colqFA | dti | FA map color scheme | character | | |
| icosa0 | dti | Polyeders derived from the Icosahedron (icosa0) by sequential triangulation of surface triangles | list | | |
| icosa1 | dti | Polyeders derived from the Icosahedron (icosa0) by sequential triangulation of surface triangles | list | | |
| icosa2 | dti | Polyeders derived from the Icosahedron (icosa0) by sequential triangulation of surface triangles | list | | |
| icosa3 | dti | Polyeders derived from the Icosahedron (icosa0) by sequential triangulation of surface triangles | list | | |
| icosa4 | dti | Polyeders derived from the Icosahedron (icosa0) by sequential triangulation of surface triangles | list | | |
| optgrad | dti | Optimal gradient directions | list | | |
| fishdf | bioregion | Spatial distribution of fish in Europe (data.frame) | data.frame | 2703 | 3 |
| fishmat | bioregion | Spatial distribution of fish in Europe (co-occurrence matrix) | matrix | 338 | 195 |
| fishsf | bioregion | Spatial distribution of fish in Europe | sf | 338 | 2 |
| vegedf | bioregion | Spatial distribution of Mediterranean vegetation (data.frame) | data.frame | 460878 | 3 |
| vegemat | bioregion | Spatial distribution of Mediterranean vegetation (co-occurrence matrix) | matrix | 715 | 3697 |
| vegesf | bioregion | Spatial distribution of Mediterranean vegetation (sf data.frame) | sf | 728 | 2 |
| airtemp | smbdata | Air temperature | tbl_df | 100 | 4 |
| aphids | smbdata | Pea aphids survey | tbl_df | 45 | 4 |
| beetles | smbdata | Beetle mating | tbl_df | 40 | 5 |
| biomassc | smbdata | Measuring soil microbial biomass | tbl_df | 48 | 5 |
| brassica | smbdata | Sreening for pathogenicity | tbl_df | 39 | 9 |
| calcium | smbdata | Calcium pot trial | tbl_df | 20 | 3 |
| calcium2 | smbdata | Calcium pot trial with alternative representation | tbl_df | 20 | 7 |
| calibrate | smbdata | ELISA calibration | tbl_df | 24 | 4 |
| competition | smbdata | Weed competition experiment | tbl_df | 32 | 7 |
| conidia | smbdata | Conidial release experiment | tbl_df | 28 | 7 |
| cotton | smbdata | Cotton response to herbicide and insecticide | tbl_df | 25 | 4 |
| cross | smbdata | Genetics of root growth | tbl_df | 30 | 4 |
| cuttings | smbdata | Effect of type and size of cutting on willow yield | tbl_df | 25 | 6 |
| demethylation | smbdata | Demethylation experiment | tbl_df | 24 | 4 |
| elisa | smbdata | Elisa absorbance readings | tbl_df | 8 | 3 |
| examine | smbdata | Aphid catch | tbl_df | 50 | 23 |
| forage | smbdata | Forage maize yields | tbl_df | 12 | 5 |
| forest | smbdata | Stand density of mixed nothofagus forest plots | tbl_df | 41 | 4 |
| forest2 | smbdata | Stand density of mixed nothofagus forest plots alternative representation | tbl_df | 41 | 7 |
| heights | smbdata | Plant heights in glasshouse | tbl_df | 24 | 5 |
| herbicide | smbdata | Herbicide efficacy | tbl_df | 135 | 7 |
| ladybird | smbdata | Ladybird transmission of fungus | tbl_df | 72 | 8 |
| latinsquare1 | smbdata | Independent Latin squares | tbl_df | 50 | 4 |
| latinsquare2 | smbdata | Linked Latin squares | tbl_df | 50 | 4 |
| lupin | smbdata | Lupin trial | tbl_df | 16 | 7 |
| lupintrial | smbdata | Lupin variety trial | tbl_df | 42 | 6 |
| phosphorus | smbdata | Olsen Phosphorus | tbl_df | 20 | 3 |
| potato | smbdata | Potato yields | tbl_df | 20 | 6 |
| potatorow | smbdata | Potato yields with row data | tbl_df | 80 | 6 |
| prey | smbdata | Ladybird predation | tbl_df | 60 | 7 |
| sosr | smbdata | Weed abundance | tbl_df | 118 | 8 |
| temperature | smbdata | Rothamsted monthly mean temperature | tbl_df | 12 | 3 |
| tgw | smbdata | Thousand grain weights | tbl_df | 24 | 6 |
| transect | smbdata | Crop transect beetle counts | tbl_df | 24 | 4 |
| triticum | smbdata | Diploid wheat | tbl_df | 190 | 6 |
| voltage | smbdata | Voltage response | tbl_df | 18 | 5 |
| weedseed | smbdata | Weed seed abundance | tbl_df | 87 | 5 |
| wheat | smbdata | Wheat yields | tbl_df | 14 | 3 |
| willow | smbdata | Willow beetle measurements | tbl_df | 50 | 3 |
| isotopes | IsoCor | isotopes. | data.frame | 308 | 5 |
| testdata | IsoCor | testdata. | list | | |
| testdata_IDMS | IsoCor | testdata_IDMS. | list | | |
| sub_R25 | omXplore | Feature example data | MultiAssayExperiment | | |
| vdata | omXplore | Feature example data | MultiAssayExperiment | | |
| beads | biopixR | Image of microbeads | cimg | | |
| beads_large1 | biopixR | Image of microbeads | cimg | | |
| beads_large2 | biopixR | Image of microbeads | cimg | | |
| droplet_beads | biopixR | Image of microbeads in luminescence channel | cimg | | |
| droplets | biopixR | Droplets containing microbeads | cimg | | |
| Android_gpl | palettesForR | Palette Android_gpl | character | | |
| Bears_gpl | palettesForR | Palette Bears_gpl | character | | |
| Bgold_gpl | palettesForR | Palette Bgold_gpl | character | | |
| Blues_gpl | palettesForR | Palette Blues_gpl | character | | |
| Borders_gpl | palettesForR | Palette Borders_gpl | character | | |
| Browns_gpl | palettesForR | Palette Browns_gpl | character | | |
| Caramel_gpl | palettesForR | Palette Caramel_gpl | character | | |
| Cascade_gpl | palettesForR | Palette Cascade_gpl | character | | |
| China_gpl | palettesForR | Palette China_gpl | character | | |
| Coldfire_gpl | palettesForR | Palette Coldfire_gpl | character | | |
| Cool_gpl | palettesForR | Palette Cool_gpl | character | | |
| Cranes_gpl | palettesForR | Palette Cranes_gpl | character | | |
| Dark_gpl | palettesForR | Palette Dark_gpl | character | | |
| Default_gpl | palettesForR | Palette Default_gpl | character | | |
| Echo_gpl | palettesForR | Palette Echo_gpl | character | | |
| Ega_gpl | palettesForR | Palette Ega_gpl | character | | |
| Firecode_gpl | palettesForR | Palette Firecode_gpl | character | | |
| Gold_gpl | palettesForR | Palette Gold_gpl | character | | |
| Gray_gpl | palettesForR | Palette Gray_gpl | character | | |
| Grayblue_gpl | palettesForR | Palette Grayblue_gpl | character | | |
| Grays_gpl | palettesForR | Palette Grays_gpl | character | | |
| GrayViolet_gpl | palettesForR | Palette GrayViolet_gpl | character | | |
| Greens_gpl | palettesForR | Palette Greens_gpl | character | | |
| Hilite_gpl | palettesForR | Palette Hilite_gpl | character | | |
| Inkscape_gpl | palettesForR | Palette Inkscape_gpl | character | | |
| Khaki_gpl | palettesForR | Palette Khaki_gpl | character | | |
| LaTeX_gpl | palettesForR | Palette LaTeX_gpl | character | | |
| Lights_gpl | palettesForR | Palette Lights_gpl | character | | |
| MATLAB_gpl | palettesForR | Palette MATLAB_gpl | character | | |
| Muted_gpl | palettesForR | Palette Muted_gpl | character | | |
| Named_gpl | palettesForR | Palette Named_gpl | character | | |
| News3_gpl | palettesForR | Palette News3_gpl | character | | |
| Op2_gpl | palettesForR | Palette Op2_gpl | character | | |
| Paintjet_gpl | palettesForR | Palette Paintjet_gpl | character | | |
| Pastels_gpl | palettesForR | Palette Pastels_gpl | character | | |
| Plasma_gpl | palettesForR | Palette Plasma_gpl | character | | |
| Reds_gpl | palettesForR | Palette Reds_gpl | character | | |
| Royal_gpl | palettesForR | Palette Royal_gpl | character | | |
| SVG_gpl | palettesForR | Palette SVG_gpl | character | | |
| Tango_gpl | palettesForR | Palette Tango_gpl | character | | |
| Topographic_gpl | palettesForR | Palette Topographic_gpl | character | | |
| Visibone_gpl | palettesForR | Palette Visibone_gpl | character | | |
| Volcano_gpl | palettesForR | Palette Volcano_gpl | character | | |
| Warm_gpl | palettesForR | Palette Warm_gpl | character | | |
| Web_gpl | palettesForR | Palette Web_gpl | character | | |
| WebHex_gpl | palettesForR | Palette WebHex_gpl | character | | |
| WebSafe22_gpl | palettesForR | Palette WebSafe22_gpl | character | | |
| Windows_gpl | palettesForR | Palette Windows_gpl | character | | |
| os_alias | repo.data | Base R OS specific alias | matrix | 33 | 5 |
| toy_curves | fda.vi | Toy Simulated Functional Dataset | list | | |
| config_csv | motherduck | DuckDB CSV read configuration (config_csv) | tbl_df | 41 | 4 |
| config_db | motherduck | DuckDB runtime database configuration (config_db) | list | | |
| config_excel | motherduck | DuckDB Excel read configuration (config_excel) | tbl_df | 7 | 4 |
| config_parquet | motherduck | DuckDB Parquet read configuration (config_parquet) | tbl_df | 6 | 4 |
| brazil | politicsR | Brazilian Lower Chamber Electoral Results | tbl_df | 11205 | 8 |
| denmark | politicsR | Danish Lower Chamber Electoral Results | tbl_df | 7532 | 9 |
| spain | politicsR | Spanish Lower Chamber Electoral Results | tbl_df | 8179 | 8 |
| gdp_2014_admin_districts | leafdown | GPD for administrative districts of Germany for 2014. | data.frame | 402 | 2 |
| gdp_2014_federal_states | leafdown | GPD for federal states of Germany for 2014. | data.frame | 16 | 2 |
| us_election_counties | leafdown | Results of the 2016 US Presidential Election - County Level | tbl_df | 3143 | 17 |
| us_election_states | leafdown | Results of the 2016 US Presidential Election - State Level | tbl_df | 51 | 15 |
| finratKZ | AFR | finratKZ dataset | data.frame | 400 | 30 |
| macroKZ | AFR | macroKZ dataset | mts | 61 | 60 |
| clock_iso_weekdays | clock | Integer codes | environment | | |
| clock_months | clock | Integer codes | environment | | |
| clock_weekdays | clock | Integer codes | environment | | |
| motion_example | SelectBoost.FDA | Smooth Trajectory Functional Example | list | | |
| spectra_example | SelectBoost.FDA | Spectroscopy-Style Functional Example | list | | |
| terror | bizicount | Nigeria Terrorism Data | data.frame | 312 | 6 |
| mockDatasets | omock | Available mock OMOP CDM Synthetic Datasets | tbl_df | 25 | 9 |
| languages | unstruwwel | Language Information | tbl_df | 4 | 5 |
| midas | unstruwwel | MIDAS Standardization Examples | character | | |
| schemes | unstruwwel | Language-Specific Scheme Variants | tbl_df | 3583 | 3 |
| fortmax | eva | | data.frame | 100 | 11 |
| lowestoft | eva | | matrix | 51 | 10 |
| WYcond | FIESTA | FIA data. Condition-level data from FIA public database. | data.frame | 3224 | 26 |
| WYp2veg_subp_structure | FIESTA | FIA data. P2 vegetation structure data from FIA public database. | data.frame | 57200 | 6 |
| WYp2veg_subplot_spp | FIESTA | FIA data. P2 vegetation species data from FIA public database. | data.frame | 8265 | 9 |
| WYplt | FIESTA | FIA data. Plot-level data from FIA public database. | data.frame | 3047 | 20 |
| WYpltassgn | FIESTA | FIA data. Plot assignment data from FIA public database. | data.frame | 3047 | 24 |
| WYseed | FIESTA | FIA data. Seedling data from FIA public database. | data.frame | 1607 | 10 |
| WYstratalut | FIESTA | FIA data. Post-stratification data from FIA public database. | data.frame | 35 | 7 |
| WYsubp_cond | FIESTA | FIA data. Subplot condition data from FIA public database. | data.frame | 12214 | 6 |
| WYsubplot | FIESTA | FIA data. Subplot data from FIA public database. | data.frame | 12188 | 6 |
| WYtree | FIESTA | FIA data. Tree-level data from FIA public database. | data.frame | 18574 | 19 |
| WYunitarea | FIESTA | FIA data. Acres data from FIA public database. | data.frame | 23 | 4 |
| WYunitzonal | FIESTA | Zonal data. Zonal means for auxiliary data in counties in Wyoming. | data.frame | 23 | 9 |
| Catania | netseg | Pattern of sexual contacts in AMEN study | table | 4 | 4 |
| Classroom | netseg | Classroom network | igraph | | |
| EF3 | netseg | Example data from Figure III of Echenique and Fryer (2006) | igraph | | |
| WhiteKinship | netseg | White's data on Effective Kinship Networks | igraph | | |
| constants_table | drawsample | Fleishman's Power Method Transformation Constants | tbl_df | 5292 | 5 |
| example_data | drawsample | Example Data | tbl_df | 5000 | 3 |
| likert_example | drawsample | Likert Example Data | tbl_df | 6669 | 7 |
| headlines | rwig | NYT headlines by economic policy uncertainty | data.frame | 18507 | 2 |
| Data_Maize | eemdARIMA | Monthly International Maize Price Data | ts | 128 | 1 |
| gcTheoretical | ngsReports | Theoretical GC content | TheoreticalGC | | |
| indsim | BrainCon | Simulation time series data for individual | matrix | 50 | |
| popsimA | BrainCon | Simulation time series data for population A | array | | |
| popsimB | BrainCon | Simulation time series data for population B | array | | |
| Bechtoldt | psych | Seven data sets showing a bifactor solution. | matrix | 17 | 17 |
| Bechtoldt.1 | psych | Seven data sets showing a bifactor solution. | matrix | 17 | 17 |
| Bechtoldt.2 | psych | Seven data sets showing a bifactor solution. | matrix | 17 | 17 |
| bfi | psych | 25 Personality items representing 5 factors | data.frame | 2800 | 28 |
| bfi.dictionary | psych | 25 Personality items representing 5 factors | data.frame | 28 | 7 |
| bfi.keys | psych | 25 Personality items representing 5 factors | list | | |
| bock.table | psych | Bock and Liberman (1970) data set of 1000 observations of the LSAT | data.frame | 32 | 8 |
| cattell | psych | 12 cognitive variables from Cattell (1963) | matrix | 12 | 12 |
| Chen | psych | 12 variables created by Schmid and Leiman to show the Schmid-Leiman Transformation | matrix | 18 | 18 |
| Dwyer | psych | 8 cognitive variables used by Dwyer for an example. | matrix | 8 | 8 |
| Garcia | psych | Data from the sexism (protest) study of Garcia, Schmitt, Branscome, and Ellemers (2010) | data.frame | 129 | 6 |
| Gleser | psych | Example data from Gleser, Cronbach and Rajaratnam (1965) to show basic principles of generalizability theory. | data.frame | 12 | 12 |
| Gorsuch | psych | Example data set from Gorsuch (1997) for an example factor extension. | matrix | 10 | 10 |
| Harman_5 | psych | Six data sets from Harman (1967). 9 cognitive variables from Holzinger and 8 emotional variables from Burt | matrix | 5 | 5 |
| Harman.5 | psych | Six data sets from Harman (1967). 9 cognitive variables from Holzinger and 8 emotional variables from Burt | matrix | 12 | 5 |
| Harman.8 | psych | Six data sets from Harman (1967). 9 cognitive variables from Holzinger and 8 emotional variables from Burt | matrix | 8 | 8 |
| Harman.Burt | psych | Six data sets from Harman (1967). 9 cognitive variables from Holzinger and 8 emotional variables from Burt | matrix | 8 | 8 |
| Harman.Holzinger | psych | Six data sets from Harman (1967). 9 cognitive variables from Holzinger and 8 emotional variables from Burt | matrix | 9 | 9 |
| Harman.political | psych | Six data sets from Harman (1967). 9 cognitive variables from Holzinger and 8 emotional variables from Burt | matrix | 8 | 8 |
| Holzinger | psych | Seven data sets showing a bifactor solution. | matrix | 14 | 14 |
| Holzinger.9 | psych | Seven data sets showing a bifactor solution. | matrix | 9 | 9 |
| lsat6 | psych | Bock and Liberman (1970) data set of 1000 observations of the LSAT | matrix | 1000 | 5 |
| lsat7 | psych | Bock and Liberman (1970) data set of 1000 observations of the LSAT | matrix | 1000 | 5 |
| Reise | psych | Seven data sets showing a bifactor solution. | matrix | 16 | 16 |
| sat.act | psych | 3 Measures of ability: SATV, SATQ, ACT | data.frame | 700 | 6 |
| Schmid | psych | 12 variables created by Schmid and Leiman to show the Schmid-Leiman Transformation | matrix | 12 | 12 |
| schmid.leiman | psych | 12 variables created by Schmid and Leiman to show the Schmid-Leiman Transformation | matrix | 12 | 12 |
| small.msq | psych | A small example data set taken from a larger data set | data.frame | 200 | 14 |
| Tal_Or | psych | Data set testing causal direction in presumed media influence | data.frame | 123 | 6 |
| Tal.Or | psych | Data set testing causal direction in presumed media influence | data.frame | 123 | 6 |
| Thurstone | psych | Seven data sets showing a bifactor solution. | matrix | 9 | 9 |
| Thurstone.33 | psych | Seven data sets showing a bifactor solution. | matrix | 9 | 9 |
| Thurstone.33G | psych | Seven data sets showing a bifactor solution. | matrix | 9 | |
| Thurstone.9 | psych | Seven data sets showing a bifactor solution. | matrix | 9 | 9 |
| Tucker | psych | 9 Cognitive variables discussed by Tucker and Lewis (1973) | data.frame | 9 | 9 |
| West | psych | 12 variables created by Schmid and Leiman to show the Schmid-Leiman Transformation | matrix | 16 | 16 |
| withinBetween | psych | An example of the distinction between within group and between group correlations | data.frame | 16 | 10 |
| fitPoly_data | fitPoly | Small fitPoly input datasets for testing and examples | list | | |
| scores | fitPoly | A data set with dosage scores generated by fitPoly | data.frame | 17013 | 12 |
| XYdat | fitPoly | A data set containing SNP array data | data.frame | 17808 | 6 |
| injury | icdpicr | ICD10CM injury codes | spec_tbl_df | 100477 | 11 |
| us | dirmult | Allele counts for six US subpopulations. | list | | |
| jones2020.tracks | pawscore | Jones et al. (2020) paw trajectory data | list | | |
| GDT_NAMES | FIESTAutils | Reference tables - gdal data types. | character | | |
| kindcd3old | FIESTAutils | Reference table - List of RMRS plots that have fallen out of inventory because they were not found or they were in the wrong place. | data.frame | 38 | 8 |
| ref_codes | FIESTAutils | Reference tables - Code definitions. | data.frame | 730 | 7 |
| ref_cond | FIESTAutils | Reference table - Metadata for cond default variables output from DBgetPlots() | data.frame | 97 | 3 |
| ref_conversion | FIESTAutils | Reference table - for conversion factors. | data.frame | 7 | 6 |
| ref_diacl2in | FIESTAutils | Reference table - diameter 2-inch class codes (DIA). | data.frame | 40 | 3 |
| ref_domain | FIESTAutils | Reference table - for generating tables. | data.frame | 33 | 3 |
| ref_estimators | FIESTAutils | Reference table - FIESTA estimators. | data.frame | 8 | 9 |
| ref_estvar | FIESTAutils | Reference table - for generating estimates | data.frame | 227 | 18 |
| ref_evaltyp | FIESTAutils | Reference table - for generating tables. | data.frame | 14 | 3 |
| ref_plt | FIESTAutils | Reference table - Metadata for plt default variables output from DBgetPlots() | data.frame | 59 | 3 |
| ref_popType | FIESTAutils | Reference table - popType codes. | data.frame | 15 | 2 |
| ref_shp | FIESTAutils | Reference table - Metadata for shp_* default variables output from DBgetPlots() | data.frame | 63 | 4 |
| ref_species | FIESTAutils | Reference table - Code definitions. | data.frame | 2677 | 20 |
| ref_statecd | FIESTAutils | Reference table - state codes (STATECD). | data.frame | 59 | 7 |
| ref_titles | FIESTAutils | Reference table - Variable titles. | data.frame | 71 | 2 |
| ref_tree | FIESTAutils | Reference table - Metadata for tree default variables output from DBgetPlots() | data.frame | 117 | 3 |
| ref_units | FIESTAutils | Reference table - for variable units. | data.frame | 47 | 5 |
| stunitco | FIESTAutils | SpatialPolygonsDataFrame with FIA state, unit, county codes and names | sf | 3233 | 8 |
| asr_sae | SwissASR | Demonstration data set | data.frame | 20 | 22 |
| pois_logistic | waspr | pois_logistic | array | | |
| betas.toy | approximator | Toy datasets for approximator package | matrix | 4 | 5 |
| D1.genie | approximator | Genie datasets for approximator package | matrix | 36 | 4 |
| D1.toy | approximator | Toy datasets for approximator package | matrix | 20 | 3 |
| hpa.genie.optimal | approximator | Genie datasets for approximator package | list | | |
| hpa.genie.start | approximator | Genie datasets for approximator package | list | | |
| hpa.toy | approximator | Toy datasets for approximator package | list | | |
| subsets.genie | approximator | Genie datasets for approximator package | list | | |
| subsets.toy | approximator | Toy datasets for approximator package | list | | |
| z.genie | approximator | Genie datasets for approximator package | list | | |
| z.toy | approximator | Toy datasets for approximator package | list | | |
| us_deaths | offsetreg | United States Deaths 2011-2020 | tbl_df | 140 | 6 |
| atrazine | NADA2 | Atrazine concentrations in Nebraska ground water | data.frame | 24 | 4 |
| Brumbaugh | NADA2 | Brumbaugh | data.frame | 133 | 14 |
| Cadmium | NADA2 | Cadmium | data.frame | 19 | 3 |
| CuZn | NADA2 | CuZn | data.frame | 118 | 5 |
| Example1 | NADA2 | Example1 | data.frame | 21 | 3 |
| Example2 | NADA2 | Example2 | data.frame | 31 | 5 |
| Example3 | NADA2 | Example3 | data.frame | 14 | 4 |
| Gales_Creek | NADA2 | Gales_Creek | tbl_df | 63 | 11 |
| Markers | NADA2 | Markers | data.frame | 30 | 15 |
| PbHeron | NADA2 | PbHeron | data.frame | 27 | 15 |
| Pollen_Thia | NADA2 | Thiamethoxam concentrations in pollen | data.frame | 204 | 4 |
| ReconLogistic | NADA2 | ReconLogistic | data.frame | 423 | 8 |
| ShePyrene | NADA2 | ShePyrene | data.frame | 56 | 2 |
| TCE2 | NADA2 | TCE2 | data.frame | 222 | 6 |
| TCEReg | NADA2 | TCE Ground Waters of Long Island — with Explanatory Variables | data.frame | 247 | 7 |
| adls_timevarying_region_data | EpiForsk | Simulated Time-Varying Residence Data | tbl_df | 546 | 7 |
| andh_forest_data | EpiForsk | Example Data for Husby's Forest Plot Vignette | spec_tbl_df | 18 | 12 |
| ar9 | validann | Data generated by autoregressive AR9 model. | data.frame | 1000 | 16 |
| chr6 | GenWin | Maize Fst Data | data.frame | 85388 | 5 |
| spiders | cotram | Bavarian Forest Spider Data | data.frame | 190 | 9 |
| examples | webseq | Examples | list | | |
| cross.beds1 | NPCirc | Cross-beds azimuths (I) | data.frame | 580 | 1 |
| cross.beds2 | NPCirc | Cross-beds (II) | data.frame | 104 | 1 |
| cycle.changes | NPCirc | Cycle changes | data.frame | 350 | 2 |
| dragonfly | NPCirc | Orientations of dragonflies | data.frame | 214 | 1 |
| flywheels | NPCirc | Flywheel measurements | data.frame | 60 | 3 |
| HumanMotorResonance | NPCirc | Human motor resonance data | data.frame | 70 | 2 |
| inclusion.trails | NPCirc | Porphyroblast inclusion trails dataset | data.frame | 2827 | 6 |
| periwinkles | NPCirc | Orientations of dragonflies | data.frame | 31 | 2 |
| pm10 | NPCirc | Pm10 particles in Pontevedra, Spain | data.frame | 1156 | 3 |
| sandhoppers | NPCirc | Behavioral plasticity of Talitrus saltator and Talorchestia brito | data.frame | 1828 | 12 |
| speed.wind | NPCirc | Wind speed and wind direction data | data.frame | 19488 | 6 |
| speed.wind2 | NPCirc | Wind speed and wind direction data | data.frame | 200 | 6 |
| spikes | NPCirc | Neuronal spikes in a macaque monkey | data.frame | 68 | 2 |
| temp.wind | NPCirc | Temperature and wind direction data | data.frame | 3648 | 4 |
| wind | NPCirc | Wind direction data | data.frame | 1752 | 3 |
| zebrafish | NPCirc | Zebrafish | data.frame | 502 | 2 |
| package_rss | newscatcheR | RSS table from python package newscatcher | spec_tbl_df | 4505 | 7 |
| QuadRules | mvQuad | nodes and weights for 1D - Gauss-Quadrature | list | | |
| birds | TH.data | Habitat Suitability for Breeding Bird Communities | data.frame | 258 | 10 |
| bodyfat | TH.data | Prediction of Body Fat by Skinfold Thickness, Circumferences, and Bone Breadths | data.frame | 71 | 10 |
| GBSG2 | TH.data | German Breast Cancer Study Group 2 | data.frame | 686 | 10 |
| geyser | TH.data | Old Faithful Geyser Data | data.frame | 299 | 2 |
| GlaucomaM | TH.data | Glaucoma Database | data.frame | 196 | 63 |
| mammoexp | TH.data | Mammography Experience Study | data.frame | 412 | 6 |
| mn6.9 | TH.data | I.Q. and attitude towards science | data.frame | 2982 | 5 |
| sphase | TH.data | S-phase Fraction of Tumor Cells | data.frame | 109 | 3 |
| Westbc | TH.data | Breast Cancer Gene Expression | list | | |
| wpbc | TH.data | Wisconsin Prognostic Breast Cancer Data | data.frame | 198 | 34 |
| starmine | backtest | StarMine Rankings, 1995 | data.frame | 53328 | 23 |
| data | Jdmbs | correlation coefficients between all pair companies | data.frame | 6 | 6 |
| elcf4r_elmas_toy | elcf4R | Toy subset of ELMAS hourly cluster profiles | tbl_df | 5040 | 3 |
| elcf4r_iflex_benchmark_index | elcf4R | iFlex benchmark index of complete participant-days | data.table | 563150 | 11 |
| elcf4r_iflex_benchmark_results | elcf4R | iFlex benchmark results for shipped forecasting methods | data.frame | 525 | 20 |
| elcf4r_iflex_example | elcf4R | iFlex example panel for package examples | data.frame | 1008 | 16 |
| elcf4r_lcl_benchmark_results | elcf4R | Low Carbon London benchmark results for shipped forecasting methods | data.frame | 35 | 20 |
| elcf4r_lcl_example | elcf4R | Low Carbon London example panel for package examples | data.frame | 1344 | 10 |
| elcf4r_refit_benchmark_results | elcf4R | REFIT benchmark results for shipped forecasting methods | data.frame | 70 | 20 |
| elcf4r_refit_example | elcf4R | REFIT example panel for package examples | data.frame | 2688 | 14 |
| elcf4r_storenet_benchmark_results | elcf4R | StoreNet benchmark results for shipped forecasting methods | data.frame | 8 | 20 |
| elcf4r_storenet_example | elcf4R | StoreNet example panel for package examples | data.frame | 8640 | 15 |
| dat.3drug | OofAExp | Three-drug combination data | matrix | 28 | 9 |
| dat.3drug.adapted | OofAExp | Adapted three-drug combination data | matrix | 6 | 6 |
| dat.4drug | OofAExp | Four-drug combination data | matrix | 25 | 8 |
| dat.4paint | OofAExp | Four-ingredient paint viscosity data | matrix | 24 | 7 |
| dat.5drug | OofAExp | Five-drug combination data | matrix | 60 | 11 |
| dat.5paint | OofAExp | Five-ingredient paint viscosity data | matrix | 15 | 8 |
| dat.6paint | OofAExp | Six-ingredient paint viscosity data | matrix | 24 | 9 |
| mallard | fido | Data from Silverman et al. (2018) Microbiome | list | | |
| mallard_family | fido | Data from Silverman et al. (2018) Microbiome | list | | |
| metadata | fido | Data from Silverman et al. (2019) bioRxiv | data.frame | 99 | 4 |
| RISK_CCFA_otu | fido | Data from Gevers et al. (2014) | matrix | 9511 | 1359 |
| RISK_CCFA_sam | fido | Data from Gevers et al. (2014) | data.frame | 1359 | 73 |
| RISK_CCFA_tax | fido | Data from Gevers et al. (2014) | matrix | 9511 | 7 |
| Y | fido | Data from Silverman et al. (2019) bioRxiv | matrix | 10 | 99 |
| sim_fcs_init | DGP4LCF | Initials values. | list | | |
| sim_fcs_results_irregular_6_8 | DGP4LCF | Results when people have irregularly observed time points (some 6 while others 8). | list | | |
| sim_fcs_results_regular_8 | DGP4LCF | Results when people are observed at common 8 time points. | list | | |
| sim_fcs_truth | DGP4LCF | Truth of simulated data. | list | | |
| corn_data | maize | Synthetic Corn Dataset for Corny Example | tbl_df | 300 | 3 |
| artset1987 | GTDL | Artset1987 data | numeric | | |
| tumor | GTDL | Tumor data | data.frame | 35 | 3 |
| name_key | InteractionPoweR | Name key for plotting | data.frame | 25 | 2 |
| factorialdata | doebioresearch | Data of Factorial Experiment | data.frame | 24 | 6 |
| lsddata | doebioresearch | Data for Latin Square Design | data.frame | 25 | 5 |
| splitdata | doebioresearch | Data for Split plot Design | data.frame | 36 | 5 |
| hmdb | pmd | A dataframe containing HMDB with unique accurate mass pmd with three digits frequency larger than 1 and accuracy percentage larger than 0.9. | data.frame | 114824 | 9 |
| keggrall | pmd | A dataframe containing reaction related accurate mass pmd and related reaction formula with KEGG ID | data.frame | 22336 | 14 |
| MaConDa | pmd | mass spectrometry contaminants database for PMD check | data.frame | 308 | 5 |
| omics | pmd | A dataframe containing multiple reaction database ID and their related accurate mass pmd and related reactions | data.frame | 58050 | 12 |
| sda | pmd | A dataset containing common Paired mass distances of substructure, ions replacements, and reaction | spec_tbl_df | 146 | 4 |
| spmeinvivo | pmd | A peaks list dataset containing 9 samples from 3 fish with triplicates samples for each fish from LC-MS. | list | | |
| tanf | whatifbandit | Public TANF Recipient Data From Washington D.C | tbl_df | 3517 | 21 |
| episodes | alone | Episodes | tbl_df | 172 | 14 |
| loadouts | alone | Loadouts | tbl_df | 1240 | 7 |
| seasons | alone | Seasons | tbl_df | 21 | 10 |
| survivalists | alone | Survivalists | tbl_df | 160 | 20 |
| SimData | rtmpt | Data simulated from the restricted 2HTM | data.frame | 2400 | 5 |
| justice | mitml | Example data set on employees' justice perceptions and satisfaction | data.frame | 1400 | 5 |
| leadership | mitml | Example data set on leadership style and job satisfaction | data.frame | 750 | 5 |
| studentratings | mitml | Example data set on student ratings and achievement | data.frame | 750 | 10 |
| data_list | label.switching | Simulated MCMC sample and related information | list | | |
| lamb | label.switching | Fetal lamb dataset | integer | | |
| texts | tipitaka.critical | Full Text of the Pali Canon (Critical Edition) | data.frame | 5777 | 6 |
| bim_example | htaBIM | Example budget impact model inputs: Disease X | list | | |
| app1 | ANSM5 | Data in Appendix 1 | list | | |
| ch10 | ANSM5 | Data used in Chapter 10 | list | | |
| ch11 | ANSM5 | Data used in Chapter 11 | list | | |
| ch12 | ANSM5 | Data used in Chapter 12 | list | | |
| ch13 | ANSM5 | Data used in Chapter 13 | list | | |
| ch14 | ANSM5 | Data used in Chapter 14 | list | | |
| ch15 | ANSM5 | Data used in Chapter 15 | list | | |
| ch3 | ANSM5 | Data used in Chapter 3 | list | | |
| ch4 | ANSM5 | Data used in Chapter 4 | list | | |
| ch5 | ANSM5 | Data used in Chapter 5 | list | | |
| ch6 | ANSM5 | Data used in Chapter 6 | list | | |
| ch7 | ANSM5 | Data used in Chapter 7 | list | | |
| ch8 | ANSM5 | Data used in Chapter 8 | list | | |
| ch9 | ANSM5 | Data used in Chapter 9 | list | | |
| bandicoot.gl | dartR.data | A dartR object created via the read.dart functions | dartR | | |
| EYR | dartR.data | Eastern yellow robin (EYR) DArT data in a genlight object | dartR | | |
| LBP | dartR.data | Laedbeater's possum (LBP) DArT data in a genlight object | dartR | | |
| platypus.gl | dartR.data | A genlight object created via the gl.read.dart function | dartR | | |
| possums.gl | dartR.data | A simulated dartR object created to run a landscape genetic example | dartR | | |
| testset.gl | dartR.data | A genlight object created via the gl.read.dart function | dartR | | |
| testset.gs | dartR.data | A genlight object created via the gl.read.silicodart function | dartR | | |
| YTH | dartR.data | Yellow-tufted honeyeater (YTH) DArT data in a genlight object | dartR | | |
| children | questionr | A fertility survey - "children" table | tbl_df | 1584 | 6 |
| enfants | questionr | A fertility survey - "enfants" table | tbl_df | 1584 | 6 |
| femmes | questionr | A fertility survey - "femmes" table | tbl_df | 2000 | 17 |
| happy | questionr | Data related to happiness from the General Social Survey, 1972-2006. | data.frame | 51020 | 10 |
| hdv2003 | questionr | Histoire de vie 2003 | data.frame | 2000 | 20 |
| households | questionr | A fertility survey - "households" table | tbl_df | 1814 | 5 |
| menages | questionr | A fertility survey - "menages" table | tbl_df | 1814 | 5 |
| rp2012 | questionr | 2012 French Census - French cities of more than 2000 inhabitants | tbl_df | 5170 | 60 |
| rp2018 | questionr | 2018 French Census - French cities of more than 2000 inhabitants | tbl_df | 5417 | 62 |
| women | questionr | A fertility survey - "women" table | tbl_df | 2000 | 17 |
| cancer | fChange | Breast Cancer | data.frame | 19 | 72 |
| electricity | fChange | Spanish Spot Electricity Data | dfts | | |
| rates | fChange | US Yield Curves | dfts | | |
| SPYUS500 | fChange | S&P 500 Index Data | dfts | | |
| temperature | fChange | Australian Temperature Data | dfts | | |
| flint | quantileCI | Water Monitoring Sample from Flint, Michigan, 2015 | data.frame | 71 | 2 |
| Australia | SDLfilter | A map of Australia | data.frame | 17138 | 7 |
| bathymodel | SDLfilter | Bathymetry model for Sandy Strait, Australia | stars | | |
| flatback | SDLfilter | Flatback turtle tracking data | data.frame | 1020 | 4 |
| SandyStrait | SDLfilter | A map of Sandy Strait, Australia | data.frame | 3847 | 7 |
| tidalplane | SDLfilter | Tidal plane table for Sandy Strait, Australia | data.frame | 2 | 6 |
| tidedata | SDLfilter | Tidal data for Sandy Strait, Australia | data.frame | 26351 | 3 |
| turtle | SDLfilter | Green turtle tracking data | data.frame | 429 | 5 |
| turtle2 | SDLfilter | Green turtle tracking data 2 | data.frame | 276 | 5 |
| ud_matrix | SDLfilter | A matrix containing probability distributions of flatback turtles | matrix | 15 | |
| ud_raster | SDLfilter | A list of raster data containing probability distributions of flatback turtles | list | | |
| data_all | fusedMGM | An example of 2-group mixed data | data.frame | 500 | 100 |
| data_mini | fusedMGM | A toy example of 2-group mixed data | data.frame | 500 | 10 |
| ind_disc | fusedMGM | An example of 2-group mixed data | integer | | |
| ind_disc_mini | fusedMGM | A toy example of 2-group mixed data | integer | | |
| BatchData | AMARETTO | BatchData | data.frame | 23263 | 3 |
| Driver_Genes | AMARETTO | Driver_Genes | list | | |
| MsigdbMapping | AMARETTO | MsigdbMapping | data.frame | 17810 | 3 |
| ProcessedDataLIHC | AMARETTO | ProcessedDataLIHC | list | | |
| coriell | bcp | Array CGH data set of Coriell cell lines | data.frame | 2271 | 5 |
| lombard | bcp | Milling machine indentation data | numeric | | |
| NewHavenHousing | bcp | New Haven housing data | data.frame | 244 | 18 |
| QuebecRivers | bcp | Quebec river streamflow data | matrix | 23 | 5 |
| RealInt | bcp | US Ex-post Real Interest Rate data, 1961(1):1986(3) | ts | 103 | |
| PPA | SynthTools | Characteristics of 1000 People in Pennsylvania. | data.frame | 1000 | 7 |
| PPAm5 | SynthTools | A list containing 5 partially synthetic data sets. | list | | |
| PPAps1 | SynthTools | Characteristics of 1000 People in Pennsylvania, partially synthetic (set 1). | data.frame | 1000 | 7 |
| PPAps2 | SynthTools | Characteristics of 1000 People in Pennsylvania, partially synthetic (set 2). | data.frame | 1000 | 7 |
| PPAps3 | SynthTools | Characteristics of 1000 People in Pennsylvania, partially synthetic (set 3). | data.frame | 1000 | 7 |
| PPAps4 | SynthTools | Characteristics of 1000 People in Pennsylvania, partially synthetic (set 4). | data.frame | 1000 | 7 |
| PPAps5 | SynthTools | Characteristics of 1000 People in Pennsylvania, partially synthetic (set 5). | data.frame | 1000 | 7 |
| data.sim | CRTgeeDR | The data.sim Dataset. | data.frame | 10000 | 11 |
| destinations_york | simodels | Example destinations dataset: schools in York | sf | 68 | 5 |
| od_aus | simodels | Example OD dataset: flows between regions in Australia | data.frame | 225 | 13 |
| si_centroids | simodels | Example zones and centroids | sf | 107 | 14 |
| si_oa_wpz | simodels | Origin-Destination Data for Leeds | spec_tbl_df | 18153 | 3 |
| si_oa_wpz_d | simodels | Destination Data for Leeds | sf | 273 | 3 |
| si_oa_wpz_o | simodels | Origin Data for Leeds | sf | 453 | 3 |
| si_od_census | simodels | Example OD dataset | spec_tbl_df | 10536 | 18 |
| si_pubs | simodels | Example destinations dataset: pubs in Leeds | sf | 265 | 8 |
| si_zones | simodels | Example zones and centroids | sf | 107 | 14 |
| zones_aus | simodels | Example zones dataset: regions of Australia | sf | 15 | 7 |
| zones_york | simodels | Example zones dataset: administrative zones of York | sf | 121 | 14 |
| studyStatus | prismadiagramR | Prices of 50,000 round cut diamonds. | data.frame | 100 | 3 |
| dem_twi_srb | meteo | Digital Elevation Model (DEM) and Topographic Wetness Index (TWI) for Serbia | PackedSpatRaster | | |
| dprec | meteo | Daily precipitation amount in mm for July 2011 | data.frame | 37613 | 3 |
| dslp | meteo | Mean sea level pressure in hPa for July 2011 | data.frame | 31208 | 3 |
| dsndp | meteo | Daily snow depth in cm for July 2011 | data.frame | 33770 | 3 |
| dtemp_maxc | meteo | Maximum daily temperature in degrees Celsius for July 2011 | data.frame | 136264 | 3 |
| dtemp_minc | meteo | Minimum daily temperature in degrees Celsius for July 2011 | data.frame | 136384 | 3 |
| dtempc | meteo | Mean daily temperature in degrees Celsius for July 2011 | data.frame | 21664 | 12 |
| dtempc | meteo | Mean daily temperature in degrees Celsius for July 2011 | data.frame | 133372 | 3 |
| dwdsp | meteo | Daily mean wind speed in m/s for July 2011 | data.frame | 31226 | 3 |
| nlmodis20110704 | meteo | MODIS LST 8 day images image for the Netherlands ('2011-07-04') | SpatialGridDataFrame | | |
| nlmodis20110712 | meteo | MODIS LST 8 day images image for the Netherlands ('2011-07-12') | SpatialGridDataFrame | | |
| NLpol | meteo | The Netherlands border polygon from WCAB | SpatialPolygonsDataFrame | | |
| regdata | meteo | Dynamic and static covariates for spatio-temporal regression kriging | STFDF | | |
| stations | meteo | Data frame containing stations' information | data.frame | 61 | 7 |
| stations | meteo | Data frame containing stations' information | data.frame | 13297 | 6 |
| tregcoef | meteo | Multiple linear regression coefficients for global and local daily air temperatures | list | | |
| tvgms | meteo | Spatio-temporal variogram models for global and local daily air temperatures | list | | |
| area_unit_options | papersize | Area units (vector) | character | | |
| card_sizes | papersize | Standard card sizes | tbl_df | 7 | 5 |
| dist_unit_options | papersize | Distance units (vector) | character | | |
| dist_units | papersize | Distance units (data frame) | tbl_df | 33 | 12 |
| grid_units | papersize | Grid units (vector) | character | | |
| page_extras | papersize | Extra reference data for page layouts | list | | |
| paper_sizes | papersize | Standard paper and image sizes | tbl_df | 125 | 9 |
| standard_scales | papersize | Standard map, architectural, and engineering scales | tbl_df | 36 | 16 |
| dict_acs_geoheader_2005_1year | totalcensus | List of geographic headers used in 2005 ACS 1 year survey | data.table | 35 | 4 |
| dict_acs_geoheader_2006_2008_1year | totalcensus | List of geographic headers used in 2006 - 2008 ACS 1 year survey | data.table | 51 | 4 |
| dict_acs_geoheader_2009_1year | totalcensus | List of geographic headers in 2009 ACS 1 year survey | data.table | 50 | 4 |
| dict_acs_geoheader_2009_5year | totalcensus | List of geographic headers used in ACS 5 year survey ending 2009 | data.table | 51 | 4 |
| dict_acs_geoheader_2010 | totalcensus | List of geographic headers used in 2010 ACS 1 and 5 year surveys | data.table | 53 | 4 |
| dict_acs_geoheader_2011_now | totalcensus | List of geographic headers used in American Community Survey since 2011 | data.table | 53 | 4 |
| dict_acs1_geocomponent | totalcensus | List of geographic components used in ACS 1 year surverys | data.table | 28 | 9 |
| dict_acs1_summarylevel | totalcensus | List of summary levels used in ACS 1 year surverys | data.table | 23 | 5 |
| dict_acs1_table | totalcensus | List of summary levels used in ACS 1 year surverys | data.table | 1818 | 18 |
| dict_acs5_geocomponent | totalcensus | List of geographic components used in ACS 5 year surverys | data.table | 19 | 4 |
| dict_acs5_summarylevel | totalcensus | List of summary levels used in ACS 5 year surveys | data.table | 87 | 8 |
| dict_acs5_table | totalcensus | List of summary levels used in ACS 5 year surverys | data.table | 1183 | 16 |
| dict_all_geocomponent_2000 | totalcensus | List of all geographic components, 2000 version | data.table | 99 | 2 |
| dict_all_geocomponent_2010 | totalcensus | List of all geographic components, 2010 version | data.table | 114 | 2 |
| dict_all_summarylevel | totalcensus | List of all summary levels | data.table | 216 | 2 |
| dict_cbsa | totalcensus | List CBSA code of Metropolitan Statistical Area/Micropolitan Statistical Area | data.table | 1882 | 12 |
| dict_decennial_geocomponent_2000 | totalcensus | List of geographic components and codes in census 2000 | data.table | 98 | 4 |
| dict_decennial_geocomponent_2010 | totalcensus | List of geographic components and codes in census 2010 | data.table | 96 | 4 |
| dict_decennial_geoheader_2000 | totalcensus | List of geographic headers in census 2000 | data.table | 83 | 4 |
| dict_decennial_geoheader_2010 | totalcensus | List of geographic headers in census 2010 | data.table | 101 | 4 |
| dict_decennial_summarylevel_2000 | totalcensus | Summary levels available in Census 2000 | data.table | 114 | 4 |
| dict_decennial_summarylevel_2010 | totalcensus | Summary levels available in Census 2010 | data.table | 165 | 4 |
| dict_decennial_table_2000 | totalcensus | Complete list of 2000 census tables | data.table | 286 | 4 |
| dict_decennial_table_2010 | totalcensus | Complete list of 2010 census tables | data.table | 333 | 4 |
| dict_fips | totalcensus | List of FIPS code as of 2016 in the US | data.table | 43934 | 9 |
| lookup_acs1year_2005 | totalcensus | ACS 1-year 2005 file segment and table lookup data | data.table | 27106 | 7 |
| lookup_acs1year_2006 | totalcensus | ACS 1-year 2006 file segment and table lookup data | data.table | 27986 | 3 |
| lookup_acs1year_2007 | totalcensus | ACS 1-year 2007 file segment and table lookup data | data.table | 29709 | 3 |
| lookup_acs1year_2008 | totalcensus | ACS 1-year 2008 file segment and table lookup data | data.table | 30403 | 3 |
| lookup_acs1year_2009 | totalcensus | ACS 1-year 2009 file segment and table lookup data | data.table | 34408 | 3 |
| lookup_acs1year_2010 | totalcensus | ACS 1-year 2010 file segment and table lookup data | data.table | 35081 | 3 |
| lookup_acs1year_2011 | totalcensus | ACS 1-year 2011 file segment and table lookup data | data.table | 34454 | 3 |
| lookup_acs1year_2012 | totalcensus | ACS 1-year 2012 file segment and table lookup data | data.table | 34394 | 3 |
| lookup_acs1year_2013 | totalcensus | ACS 1-year 2013 file segment and table lookup data | data.table | 32752 | 3 |
| lookup_acs1year_2014 | totalcensus | ACS 1-year 2014 file segment and table lookup data | data.table | 31561 | 3 |
| lookup_acs1year_2015 | totalcensus | ACS 1-year 2015 file segment and table lookup data | data.table | 31600 | 3 |
| lookup_acs1year_2016 | totalcensus | ACS 1-year 2016 file segment and table lookup data | data.table | 31683 | 3 |
| lookup_acs1year_2017 | totalcensus | ACS 1-year 2017 file segment and table lookup data | data.table | 33593 | 3 |
| lookup_acs1year_2018 | totalcensus | ACS 1-year 2018 file segment and table lookup data | data.table | 35502 | 3 |
| lookup_acs1year_2019 | totalcensus | ACS 1-year 2019 file segment and table lookup data | data.table | 35527 | 3 |
| lookup_acs5year_2009 | totalcensus | ACS 5-year 2009 file segment and table lookup data | data.table | 21207 | 7 |
| lookup_acs5year_2010 | totalcensus | ACS 5-year 2010 file segment and table lookup data | data.table | 21487 | 7 |
| lookup_acs5year_2011 | totalcensus | ACS 5-year 2011 file segment and table lookup data | data.table | 21038 | 7 |
| lookup_acs5year_2012 | totalcensus | ACS 5-year 2012 file segment and table lookup data | data.table | 22527 | 7 |
| lookup_acs5year_2013 | totalcensus | ACS 5-year 2013 file segment and table lookup data | data.table | 22711 | 7 |
| lookup_acs5year_2014 | totalcensus | ACS 5-year 2014 file segment and table lookup data | data.table | 22627 | 7 |
| lookup_acs5year_2015 | totalcensus | ACS 5-year 2015 file segment and table lookup data | data.table | 22767 | 7 |
| lookup_acs5year_2016 | totalcensus | ACS 5-year 2016 file segment and table lookup data | data.table | 22815 | 7 |
| lookup_acs5year_2017 | totalcensus | ACS 5-year 2017 file segment and table lookup data | data.table | 25070 | 7 |
| lookup_acs5year_2018 | totalcensus | ACS 5-year 2018 file segment and table lookup data | data.table | 26996 | 7 |
| lookup_acs5year_2019 | totalcensus | ACS 5-year 2019 file segment and table lookup data | data.table | 27039 | 7 |
| lookup_acs5year_2020 | totalcensus | ACS 5-year 2020 file segment and table lookup data | data.table | 27850 | 7 |
| lookup_acs5year_2021 | totalcensus | ACS 5-year 2021 file segment and table lookup data | data.table | 27886 | 7 |
| lookup_decennial_2000 | totalcensus | Lookup data files and table contents of Census 2000 | data.table | 8321 | 6 |
| lookup_decennial_2010 | totalcensus | Lookup data files and table contents of Census 2010 | data.table | 9199 | 6 |
| states_DC | totalcensus | Vector of the abbreviations of 50 states and DC | character | | |
| table_content_acs1year_all_years | totalcensus | ACS 1-year table contents of all years | data.table | 44137 | 19 |
| corona_data_all | hystReet | Downloaded data for the vignette | data.frame | 18988 | 5 |
| data_73_74 | hystReet | Downloaded data for the vignette | data.frame | 62 | 3 |
| location_71 | hystReet | Downloaded data for the vignette | list | | |
| locations | hystReet | Downloaded data for the vignette | data.frame | 188 | 3 |
| ratio | hystReet | Downloaded data for the vignette | data.frame | 188 | 3 |
| degelder | kohonen | Powder pattern data by Rene de Gelder | list | | |
| nir | kohonen | Near-infrared data with temperature effects | list | | |
| peppaPic | kohonen | Synthetic image of a pepper plant with peppers | matrix | 480000 | 4 |
| vintages | kohonen | Wine data | factor | | |
| wines | kohonen | Wine data | matrix | 177 | 13 |
| yeast | kohonen | Yeast cell-cycle data | list | | |
| hilic_neg_sample | notame | Toy data set | SummarizedExperiment | | |
| hilic_pos_sample | notame | Toy data set | SummarizedExperiment | | |
| rp_neg_sample | notame | Toy data set | SummarizedExperiment | | |
| rp_pos_sample | notame | Toy data set | SummarizedExperiment | | |
| toy_notame_set | notame | Toy data set | SummarizedExperiment | | |
| bird_colors | JNplots | Climatic drivers of coloration in Furnariidae species | data.frame | 250 | 21 |
| lizard_home_range | JNplots | Determinants of home range size in sleepy lizards | data.frame | 203 | 13 |
| microlophus | JNplots | Morphology of Microlophus lizards (Squamata: Tropiduridae) | data.frame | 137 | 5 |
| tree_Furnariidae | JNplots | Phylogeny of Furnariidae | phylo | | |
| angristlavy | rbounds | Angrist and Lavy (1999) Maimonides' Rule Data Set | data.frame | 172 | 9 |
| inference | LACE | Results obtained with the function LACE on the provided input data from Rambow, Florian, et al. "Toward minimal residual disease-directed therapy in melanoma." Cell 174.4 (2018): 843-855. | list | | |
| longitudinal_sc_variants | LACE | Mutation data from Rambow, Florian, et al. "Toward minimal residual disease-directed therapy in melanoma." Cell 174.4 (2018): 843-855. | list | | |
| nih_sample | tidylda | Abstracts and metadata from NIH research grants awarded in 2014 | tbl_df | 100 | 44 |
| nih_sample_dtm | tidylda | Abstracts and metadata from NIH research grants awarded in 2014 | dgCMatrix | | |
| fs_VHX_BPD | Pv3Rs | Allele frequencies computed using example _Plasmodium vivax_ data | list | | |
| ys_VHX_BPD | Pv3Rs | Example _Plasmodium vivax_ data | list | | |
| sampledata | IATanalytics | Sample Dataset from a Typical IAT | tbl_df | 220 | 6 |
| mwa_data | mwa | Data to Illustrate the Functionality of mwa | data.frame | 394 | 6 |
| new_long2 | JMbdirect | longitudinal- survival dataset | tbl_df | 5639 | 15 |
| new_surv2 | JMbdirect | survival data | tbl_df | 1000 | 15 |
| Angeville | Guerry | Data from d'Angeville (1836) on the population of France | data.frame | 86 | 16 |
| gfrance | Guerry | Map of France in 1830 with the Guerry data | SpatialPolygonsDataFrame | | |
| gfrance85 | Guerry | Map of France in 1830 with the Guerry data, excluding Corsica | SpatialPolygonsDataFrame | | |
| Guerry | Guerry | Data from A.-M. Guerry, "Essay on the Moral Statistics of France" | data.frame | 86 | 23 |
| Guerry_ranks | Guerry | Ranks of Data from A.-M. Guerry, "Essay on the Moral Statistics of France" | data.frame | 86 | 23 |
| propensity | Guerry | Distribution of crimes against persons at different ages | tbl_df | 124 | 4 |
| glycanDatabase | glycoTraitR | Glycan annotation reference database | spec_tbl_df | 10936 | 4 |
| meta_toyexample | glycoTraitR | Toy metadata for glycoTraitR examples | tbl_df | 34 | 3 |
| election | EffectStars2 | Election Data | data.frame | 816 | 31 |
| insolvency | EffectStars2 | Insolvency data | data.frame | 1224 | 16 |
| plebiscite | EffectStars2 | Chilean Plebiscite | data.frame | 2431 | 7 |
| iRTpeptides | specL | iRT peptides - independent retention time peptides | data.frame | 31 | 2 |
| ms1.p2069 | specL | ms1 mass | numeric | | |
| peptideStd | specL | Peptide standard | psmSet | | |
| peptideStd.redundant | specL | Peptide standard | psmSet | | |
| GMWL | isoWater | Global Meteoric Water Line | numeric | | |
| australia | spectacles | Australia spectra library data set | data.frame | 100 | 2155 |
| oz | spectacles | Australia spectra library data set | data.frame | 5 | 2155 |
| argo2016 | GpGp | Ocean temperatures from Argo profiling floats | data.frame | 32436 | 6 |
| jason3 | GpGp | Windspeed measurements from Jason-3 Satellite | data.frame | 18973 | 4 |
| survey_obs | wdsmatch | Simulated Survey Observational Data | data.frame | 121 | 9 |
| Animal | SASmixed | Animal breeding experiment | data.frame | 20 | 3 |
| AvgDailyGain | SASmixed | Average daily weight gain of steers on different diets | data.frame | 32 | 6 |
| BIB | SASmixed | Data from a balanced incomplete block design | data.frame | 24 | 5 |
| Bond | SASmixed | Strengths of metal bonds | data.frame | 21 | 3 |
| Cultivation | SASmixed | Bacterial innoculation applied to grass cultivars | data.frame | 24 | 4 |
| Demand | SASmixed | Per-capita demand deposits by state and year | data.frame | 77 | 7 |
| Genetics | SASmixed | Heritability data | data.frame | 60 | 4 |
| HR | SASmixed | Heart rates of patients on different drug treatments | data.frame | 120 | 5 |
| IncBlk | SASmixed | An unbalanced incomplete block experiment | data.frame | 24 | 4 |
| Mississippi | SASmixed | Nitrogen concentrations in the Mississippi River | data.frame | 37 | 3 |
| Multilocation | SASmixed | A multilocation trial | data.frame | 108 | 7 |
| PBIB | SASmixed | A partially balanced incomplete block experiment | data.frame | 60 | 3 |
| Semi2 | SASmixed | Oxide layer thicknesses on semiconductors | data.frame | 72 | 5 |
| Semiconductor | SASmixed | Semiconductor split-plot experiment | data.frame | 48 | 5 |
| SIMS | SASmixed | Second International Mathematics Study data | data.frame | 3691 | 3 |
| TeachingI | SASmixed | Teaching Methods I | data.frame | 96 | 7 |
| TeachingII | SASmixed | Teaching Methods II | data.frame | 96 | 6 |
| WaferTypes | SASmixed | Data on different types of silicon wafers | data.frame | 144 | 8 |
| Weights | SASmixed | Data from a weight-lifting program | data.frame | 399 | 5 |
| WWheat | SASmixed | Winter wheat | data.frame | 60 | 3 |
| abalone | DTSR | Abalone Data | data.frame | 4177 | 9 |
| Frogs | DTSR | Frogs Data | data.frame | 7195 | 26 |
| iris_var1 | myrror | Iris Dataset Variation 1 | data.table | 155 | 6 |
| iris_var2 | myrror | Iris Dataset Variation 2 | data.table | 150 | 5 |
| iris_var3 | myrror | Iris Dataset Variation 3 | data.table | 150 | 5 |
| iris_var4 | myrror | Iris Dataset Variation 4 | data.table | 160 | 5 |
| iris_var5 | myrror | Iris Dataset Variation 5 | data.table | 150 | 5 |
| iris_var6 | myrror | Iris Dataset Variation 6 | data.table | 146 | 5 |
| iris_var7 | myrror | Iris Dataset Variation 7 | data.table | 146 | 5 |
| survey_data | myrror | Survey Data A country-year level dataset with 15 rows and 6 variables. 2 countries, 4 years, and 4 additional variables. | data.table | 16 | 6 |
| survey_data_1m | myrror | Survey Data 1:m Variation 1 | data.table | 36 | 6 |
| survey_data_1m_2 | myrror | Survey Data 1:m Variation 2 | data.table | 36 | 6 |
| survey_data_2 | myrror | Survey Data Variation 2 | data.table | 16 | 6 |
| survey_data_2_cap | myrror | Survey Data Variation 2 with Cap Keys | data.table | 16 | 6 |
| survey_data_3 | myrror | Survey Data Variation 3 | data.table | 16 | 6 |
| survey_data_4 | myrror | Survey Data Variation 4 | data.table | 12 | 6 |
| survey_data_5 | myrror | Survey Data Variation 5 | data.table | 16 | 4 |
| survey_data_6 | myrror | Survey Data Variation 6 | data.table | 32 | 6 |
| survey_data_all | myrror | Survey Data Variation All | data.table | 12 | 5 |
| survey_data_m1 | myrror | Survey Data m:1 | data.table | 16 | 6 |
| X | jtdm | Site x environmental covariates dataset | matrix | 116 | 3 |
| Y | jtdm | Site x CWM traits dataset | matrix | 116 | 3 |
| geo_melbourne | googleway | geo_melbourne | json | | |
| melbourne | googleway | Melbourne | data.frame | 397 | 7 |
| tram_route | googleway | Tram Route | data.frame | 55 | 3 |
| tram_stops | googleway | Tram stops along tram route 35 in Melbourne | data.frame | 51 | 4 |
| real_peaks | target | AR peaks in LNCaP cell line | GRanges | | |
| real_transcripts | target | Differential expression of DHT treated LNCaP cell line | GRanges | | |
| sim_peaks | target | Simulated peaks | GRanges | | |
| sim_transcripts | target | Simulated transcripts The transcripts chromosome 1 of the mm10 mouse genome with randomly singed statistics assigned to each. | GRanges | | |
| testDataBasis | CalibrateSSB | testDataBasis | matrix | 5582 | 9 |
| radon | ggmcmc | Simulations of the parameters of a hierarchical model | list | | |
| s | ggmcmc | Simulations of the parameters of a simple linear regression with fake data. | mcmc.list | | |
| s | ggmcmc | Simulations of the parameters of a simple linear regression with fake data. | mcmc.list | | |
| s.binary | ggmcmc | Simulations of the parameters of a simple linear regression with fake data. | mcmc.list | | |
| s.binary | ggmcmc | Simulations of the parameters of a simple linear regression with fake data. | mcmc.list | | |
| s.y.rep | ggmcmc | Simulations of the posterior predictive distribution of a simple linear regression with fake data. | mcmc.list | | |
| s.y.rep | ggmcmc | Simulations of the posterior predictive distribution of a simple linear regression with fake data. | mcmc.list | | |
| y | ggmcmc | Values for the observed outcome of a simple linear regression with fake data. | numeric | | |
| y | ggmcmc | Values for the observed outcome of a simple linear regression with fake data. | numeric | | |
| y.binary | ggmcmc | Values for the observed outcome of a binary logistic regression with fake data. | integer | | |
| y.binary | ggmcmc | Values for the observed outcome of a binary logistic regression with fake data. | integer | | |
| dalton | dichromat | Effects of Daltonism (Red-Green Color Blindness) | array | | |
| dalton.colors | dichromat | Effects of Daltonism (Red-Green Color Blindness) | list | | |
| ICS_ex | vici | Toy data to upload in the app. | data.frame | 540 | 6 |
| Users | Authenticate | Users Dataset | data.frame | 3 | 4 |
| charnes1981 | Benchmarking | Data: Charnes et al. (1981): Program follow through | data.frame | 70 | 11 |
| milkProd | Benchmarking | Data: Milk producers | data.frame | 108 | 5 |
| norWood2004 | Benchmarking | Data: Forestry in Norway | data.frame | 113 | 7 |
| pigdata | Benchmarking | Data: Multi-output pig producers | data.frame | 248 | 19 |
| projekt | Benchmarking | Data: Milk producers | data.frame | 101 | 14 |
| beanie | ipsRdbs | Age and value of 50 beanie baby toys | data.frame | 50 | 3 |
| bill | ipsRdbs | Wealth, age and region of 225 billionaires in 1992 as reported in the Fortune magazine | data.frame | 225 | 3 |
| bodyfat | ipsRdbs | Body fat percentage data for 102 elite male athletes training at the Australian Institute of Sport. | data.frame | 102 | 2 |
| bombhits | ipsRdbs | Number of bomb hits in London during World War II | data.frame | 6 | 2 |
| cement | ipsRdbs | Breaking strength of cement data | data.frame | 36 | 3 |
| cfail | ipsRdbs | Weekly number of failures of a university computer system over a period of two years. This is a data vector containing 104 values. | numeric | | |
| cheese | ipsRdbs | Testing of cheese data set | data.frame | 30 | 5 |
| emissions | ipsRdbs | Nitrous oxide emission data | data.frame | 54 | 13 |
| err_age | ipsRdbs | Errors in guessing ages of Southampton mathematicians | data.frame | 550 | 10 |
| ffood | ipsRdbs | Service (waiting) times (in seconds) of customers at a fast-food restaurant. | data.frame | 10 | 2 |
| gasmileage | ipsRdbs | Gas mileage of four models of car | data.frame | 11 | 2 |
| possum | ipsRdbs | Body weight and length of possums (tree living furry animals who are mostly nocturnal (marsupial) caught in 7 different regions of Australia. | data.frame | 101 | 3 |
| puffin | ipsRdbs | Puffin nesting data set. It contains data regarding nesting habits of common puffin | data.frame | 38 | 5 |
| rice | ipsRdbs | Riece yield data | data.frame | 16 | 2 |
| wgain | ipsRdbs | Weight gain data from 68 first year students during their first 12 weeks in college | data.frame | 68 | 3 |
| Bcells | flowAI | flowSet of B cells. | flowSet | | |
| data.abiotic | maSigPro | Gene expression data potato abiotic stress | data.frame | 1000 | 36 |
| edesign.abiotic | maSigPro | Experimental design potato abiotic stress | matrix | 36 | 6 |
| edesignCT | maSigPro | Experimental design with a shared time | matrix | 32 | 7 |
| edesignDR | maSigPro | Experimental design with different replicates | matrix | 54 | 7 |
| ISOdata | maSigPro | RNA-Seq dataset example for isoforms | data.frame | 2782 | 37 |
| ISOdesign | maSigPro | Experimental design for ISOdata dataset example | data.frame | 36 | 4 |
| NBdata | maSigPro | RNA-Seq dataset example | matrix | 100 | 36 |
| NBdesign | maSigPro | Experimental design for RNA-Seq example | matrix | 36 | 4 |
| bglm_data | bmco | Simulated Single-Level Clinical Trial Data | data.frame | 200 | 4 |
| bglm_fit | bmco | Pre-computed bglm Example Fit | bglm | | |
| bglmm_data | bmco | Simulated Multilevel Clinical Trial Data | data.frame | 300 | 5 |
| bglmm_fit | bmco | Pre-computed bglmm Example Fit | bglmm | | |
| dynamic_inactivation | bioinactivation | Example Dynamic Inactivation of a Microorganis | data.frame | 40 | 3 |
| isothermal_inactivation | bioinactivation | Example Isothermal Inactivation of a Microorganis | data.frame | 68 | 3 |
| laterosporus_dyna | bioinactivation | Example Dynamic Inactivation of a Laterosporus | data.frame | 20 | 3 |
| laterosporus_iso | bioinactivation | Example Isothermal Inactivation of a Laterosporus | data.frame | 52 | 3 |
| genes.blacklist.default | scGate | Blocklist of genes for dimensionality reduction | list | | |
| query.seurat | scGate | Toy dataset to test the package | Seurat | | |
| customer_churn_tbl | correlationfunnel | Customer Churn Data Set for a Telecommunications Company | spec_tbl_df | 7043 | 21 |
| marketing_campaign_tbl | correlationfunnel | Marketing Data for a Bank | tbl_df | 45211 | 18 |
| MCodes | IRTM | Methodological Codes | spec_tbl_df | 793 | 9 |
| synth_idvs | IRTM | Synthetic Independent Variables | data.frame | 3000 | 27 |
| synth_questions | IRTM | Questions for the Synthetic European sentiment survey in the vignette | data.frame | 3000 | 146 |
| chesstools | chess2plyrs | chesstools | list | | |
| aaaggregation | signalHsmm | Reduced amino acid alphabet | list | | |
| benchmark_dat | signalHsmm | Benchmark data set for signalHsmm | list | | |
| allQC | mdqc | QC report for MLL.B | data.frame | 20 | 11 |
| demo_aftable | aftables | A Demo 'aftables' Object | aftable | 5 | 7 |
| demo_df | aftables | A Demo 'data.frame' Object | data.frame | 5 | 7 |
| demo_workbook | aftables | A Demo 'Workbook' Object | wbWorkbook | | |
| BLUEs.pheno | polyqtlR | Best Linear Unbiased Estimates of phenotype | data.frame | 50 | 2 |
| GIC_4x | polyqtlR | Genotypic Information Coefficient for example tetraploid | list | | |
| IBD_4x | polyqtlR | Identical by descent probabilities for example tetraploid | list | | |
| mr.ls | polyqtlR | Example output of meiosis report function | list | | |
| phased_maplist.4x | polyqtlR | Phased maplist for example tetraploid | list | | |
| Phenotypes_4x | polyqtlR | Phenotypes for example tetraploid | data.frame | 150 | 3 |
| qtl_LODs.4x | polyqtlR | QTL output for example tetraploid | list | | |
| Rec_Data_4x | polyqtlR | Recombination data for example tetraploid | list | | |
| segList_2x | polyqtlR | Expected segregation for all markers types of a diploid cross | list | | |
| segList_3x | polyqtlR | Expected segregation for all markers types of a triploid cross (4 x 2) | list | | |
| segList_3x_24 | polyqtlR | Expected segregation for all markers types of a triploid cross (2 x 4) | list | | |
| segList_4x | polyqtlR | Expected segregation for all markers types of a tetraploid cross | list | | |
| segList_6x | polyqtlR | Expected segregation for all markers types of a hexaploid cross | list | | |
| SNP_dosages.4x | polyqtlR | SNP marker dosage data for example tetraploid | matrix | 186 | 52 |
| county_crosswalk | rurality | County Crosswalk with Multiple Rurality Classification Schemes | data.frame | 3143 | 16 |
| county_rurality | rurality | County-Level Rurality Data for the United States | tbl_df | 3235 | 24 |
| ruca_codes | rurality | RUCA Code Data for U.S. ZIP Codes | tbl_df | 41146 | 4 |
| pisa_data | wdiexplorer | PISA mathematics average scores | data.frame | 15407 | 13 |
| pm_data | wdiexplorer | PM2.5 air pollution data | data.frame | 14040 | 13 |
| P | dks | Simulated null p-values from the uniform distribution. | matrix | 200 | |
| frailty | rsimsum | Example of a simulation study on frailty survival models | data.frame | 16000 | 6 |
| frailty2 | rsimsum | Example of a simulation study on frailty survival models | data.frame | 16000 | 7 |
| MIsim | rsimsum | Example of a simulation study on missing data | tbl_df | 3000 | 4 |
| MIsim2 | rsimsum | Example of a simulation study on missing data | tbl_df | 3000 | 5 |
| nlp | rsimsum | Example of a simulation study on survival modelling | data.frame | 30000 | 10 |
| relhaz | rsimsum | Example of a simulation study on survival modelling | data.frame | 1200 | 6 |
| tt | rsimsum | Example of a simulation study on the t-test | data.frame | 4000 | 8 |
| gvs_testfile | GVS | Example GVS data | data.frame | 27 | 2 |
| cprs | stRoke | Data frame of 200 cpr numbers | data.frame | 200 | 1 |
| metadata_names | stRoke | Vector of REDCap metadata headers | character | | |
| pase | stRoke | Data frame with sample data of PASE score questionnaire | data.frame | 642 | 21 |
| score | stRoke | Data frame with sample data of cognitive testing score | data.frame | 20 | 26 |
| talos | stRoke | Data frame with sample of TALOS data | data.frame | 200 | 6 |
| trial | stRoke | Data frame with synthetic data generated from the TALOS trial cohort | tbl_df | 642 | 16 |
| vert_df | pciR | Threat data for 33565 global terrestrial vertebrates. | data.frame | 33565 | 22 |
| hs_peptides | ProteoMM | hs_peptides - peptide-level intensities for human | data.frame | 695 | 13 |
| mm_peptides | ProteoMM | mm_peptides - peptide-level intensities for mouse | data.frame | 1102 | 13 |
| accrualdemo | accrualPlot | Demonstration data set | data.frame | 250 | 2 |
| test.data | BOLDconnectR | Canadian spider data by Blagoev et al.(2015) | data.frame | 1336 | 2 |
| test.data2 | BOLDconnectR | Example Barcode Core Data Model (BCDM) dataset | data.frame | 78 | 72 |
| occupations | promptr | Occupations | tbl_df | 3948 | 2 |
| occupations_examples | promptr | Labelled Occupations | tbl_df | 9 | 2 |
| scotus_tweets | promptr | Tweets About The Supreme Court of the United States | tbl_df | 945 | 6 |
| scotus_tweets_examples | promptr | Labelled Example Tweets About The Supreme Court of the United States | tbl_df | 12 | 4 |
| USlandfall | ppgam | Times of landfalling US hurricanes | data.frame | 61129 | 2 |
| windstorm | ppgam | Locations of windstorm peaks and tracks over the North Atlantic | data.frame | 3133 | 4 |
| desaturationsdb | FAMetA | Desaturation reactions database. | data.frame | 13 | 3 |
| examplefadata | FAMetA | Example fadata list. | list | | |
| fattyacidsdb | FAMetA | Fatty Acids database. | data.frame | 34 | 3 |
| parameters | FAMetA | Parameters for FA metabolic analysis. | data.frame | 167 | 8 |
| ssexamplefadata | FAMetA | Toy example fadata list. | list | | |
| cophe_multi_trait_data | cophescan | Simulated multi-trait data | list | | |
| a3_hil | SafeVote | Tideman a3_hil | matrix | 989 | 15 |
| a4_hil | SafeVote | Tideman a4_hil | matrix | 43 | 14 |
| a53_hil | SafeVote | Tideman a53_hil | matrix | 460 | 10 |
| auckland2022 | SafeVote | Results of Auckland Local Elections 2022 | grouped_df | 1076 | 12 |
| dublin_west | SafeVote | Dublin West | data.frame | 29988 | 9 |
| dublin_west_vote_count | SafeVote | Dublin West vote count | vote.stv | | |
| dublin_west_vote_count_3seats | SafeVote | Dublin West vote count, identifying the top 3 candidates | vote.stv | | |
| food_election | SafeVote | Food Election | data.frame | 20 | 5 |
| gisborne2022 | SafeVote | Results of Gisborne Local Elections 2022 | grouped_df | 126 | 22 |
| gisborne2022analysis | SafeVote | Analysis of results from the Gisborne 2022 election. | grouped_df | 9 | 11 |
| hamilton2022 | SafeVote | Results of Hamilton Local Elections 2022 | tbl_df | 102 | 19 |
| hamilton2022analysis | SafeVote | Analysis of results from the Hamilton 2022 election. | grouped_df | 8 | 11 |
| ims_approval | SafeVote | IMS Approval | data.frame | 620 | 10 |
| ims_election | SafeVote | IMS Election | data.frame | 620 | 10 |
| ims_plurality | SafeVote | IMS Plurality | data.frame | 620 | 10 |
| ims_score | SafeVote | IMS Score | data.frame | 620 | 10 |
| ims_stv | SafeVote | IMS STV | data.frame | 620 | 10 |
| uk_labour_2010 | SafeVote | UK Labour Party Leader 2010 | data.frame | 266 | 5 |
| wellington2022analysis | SafeVote | Analysis of results from the Wellington 2022 election. | grouped_df | 8 | 11 |
| yale_ballots | SafeVote | Yale Faculty Senate 2016 | data.frame | 479 | 44 |
| canada_mischief | bayesiansurpriser | Canadian Mischief Crime Data by Province | data.frame | 13 | 6 |
| example_counties | bayesiansurpriser | Example County Data with Simulated Events | data.frame | 50 | 7 |
| bandicoot.gl | dartR | A genlight object created via the read.dart functions | genlight | | |
| possums.gl | dartR | A simulated genlight object created to run a landscape genetic example | genlight | | |
| testset.gl | dartR | A genlight object created via the gl.read.dart function | genlight | | |
| testset.gs | dartR | A genlight object created via the gl.read.silicodart function | genlight | | |
| GunPoint | LPStimeSeries | The Gun-Point Data | list | | |
| berkey98 | mvmeta | Five Published Trials on Periodontal Disease | data.frame | 5 | 7 |
| fibrinogen | mvmeta | Fibrinogen Studies Collaboration | data.frame | 31 | 15 |
| hsls | mvmeta | High School Longitudinal Study | data.frame | 8 | 10 |
| hyp | mvmeta | Ten Studies Assessing an Hypertension Treatment | data.frame | 10 | 7 |
| p53 | mvmeta | Mutant p53 Gene and Squamous Cell Carcinoma | data.frame | 6 | 5 |
| smoking | mvmeta | Meta-Analysis of Interventions to Promote Smoking Cessation | data.frame | 24 | 19 |
| cephia | XSRecencyX | CEPHIA Public-Use Dataset | data.frame | 212831 | 38 |
| test.cross | XSRecencyX | Cross-sectional recency testing example dataset | data.table | 5000 | 9 |
| test.target | XSRecencyX | Target example dataset | data.table | 2500 | 8 |
| bem_dfmdata | bvartools | FRED-QD data | mts | 225 | 196 |
| e1 | bvartools | West German economic time series data | mts | 92 | 3 |
| e6 | bvartools | German interest and inflation rate data | mts | 107 | 2 |
| us_macrodata | bvartools | US macroeconomic data | mts | 195 | 3 |
| VanDyke | MRMCbinary | Multi-reader multi-case dataset | data.frame | 1140 | 7 |
| sampleDyadData | roundRobinR | Sample Round Robin Dataset | data.frame | 1584 | 16 |
| england | tsgc | Cumulative cases of Covid-19 in England. | xts | 733 | 4 |
| gauteng | tsgc | Cumulative cases of Covid-19 in the South African province of Gauteng. | xts | 667 | 1 |
| comparability | pccc | Multiple Cause of Death (MCOD) file extract | data.frame | 65037 | 3 |
| pccc_icd10_dataset | pccc | Randomly Generated ICD 10 Sample Data Set | data.frame | 1000 | 31 |
| pccc_icd9_dataset | pccc | Randomly Generated ICD 9 Sample Data Set | data.frame | 1000 | 31 |
| exHartigan | diptest | Hartigan's Artificial n-modal Example Data Set | numeric | | |
| qDiptab | diptest | Table of Quantiles from a Large Simulation for Hartigan's Dip Test | matrix | 21 | 26 |
| statfaculty | diptest | Faculty Quality in Statistics Departments | numeric | | |
| cForwardCurve | vamc | Constant Forward Curve | numeric | | |
| fundMap | vamc | Fund Map for 10 Funds | matrix | 10 | 5 |
| histDates | vamc | Historical Scenario Dates | Date | | |
| histIdxScen | vamc | Historical Index Scenario for 5 Indices over 175 Months | data.frame | 175 | 5 |
| indexNames | vamc | Index Names | character | | |
| indexScen | vamc | 5 Indices for 10 Scenarios over 360 Months | array | | |
| mCov | vamc | Covariance Matrix for 5 Indices | matrix | 5 | |
| mortTable | vamc | Mortality Rate for Male and Female from Ages 5 to 115 | data.frame | 111 | 3 |
| swapRate | vamc | Swap Rates across 30 Years | numeric | | |
| VAPort | vamc | A Randomly Generated Pool of Variable Annuities | data.frame | 19 | 45 |
| Diabetes | gemR | Diabetes data | data.frame | 30 | 4 |
| Lactobacillus | gemR | Lactobacillus data | data.frame | 12 | 5 |
| MS | gemR | Multiple Sclerosis data | data.frame | 101 | 5 |
| capital_cities | sugarbag | The point locations of Australian capital cities. | tbl_df | 8 | 3 |
| fp19 | sugarbag | 2019 Australian Federal election data: First preference votes for candidates (House of Representatives) in each electorate. | spec_tbl_df | 1207 | 18 |
| homeless | sugarbag | The amount of homeless people in each Statistical Area at Level 2 in 2016. | tbl_df | 2292 | 2 |
| tas_lga | sugarbag | The polygons of Tasmanian Local Government Areas in 2016. | sf | 31 | 8 |
| tas_lga_hexctr | sugarbag | The hexagon centres for polygons of Tasmanian Local Government Areas in 2016. | tbl_df | 29 | 12 |
| tas_sa2 | sugarbag | The polygons of Tasmanian Statistical Areas in 2016. | sf | 101 | 15 |
| d.bfsrg | bfsMaps | Swiss Federal Statistical Office (SFSO) Spatial Divisions | data.frame | 2136 | 27 |
| kt | bfsMaps | Abbreviations for Swiss Cantons | factor | | |
| filter_verbs | cjar | Verbs available to be used in filter rules. | data.frame | 32 | 5 |
| monkeys | PhylogeneticEM | New World Monkeys dataset | list | | |
| table10.1 | rencher | Table 10.1 Chemical Reaction Data | tbl_df | 19 | 7 |
| table13.1 | rencher | Table 13.1 | tbl_df | 7 | 6 |
| table15.1 | rencher | Table 15.1 City Crime Rates per 100,000 Population | tbl_df | 16 | 8 |
| table15.13 | rencher | Table 15.13 Air Pollution Levels in US Cities | tbl_df | 41 | 8 |
| table15.14 | rencher | Table 15.14 Yields of Winter Wheat (kg per unit area) | tbl_df | 12 | 5 |
| table15.7 | rencher | Table 15.7 Protein Data | tbl_df | 25 | 10 |
| table16.1 | rencher | Table 16.1 Airline Distances Between Ten US Cities | tbl_df | 10 | 11 |
| table16.13 | rencher | Table 16.13 Do-It-Yourself Data | tbl_df | 24 | 7 |
| table16.16 | rencher | Table 16.16 Dissimilarity Matrix for World War II Politicians oliticians | tbl_df | 12 | 13 |
| table16.17 | rencher | Table 16.17 | tbl_df | 12 | 13 |
| table16.19 | rencher | Table 16.19 Byssinosis Data | tbl_df | 48 | 8 |
| table16.8 | rencher | Table 16.8 A List of 12 People and Their Categories on Four Variable | tbl_df | 12 | 5 |
| table3.1 | rencher | Table 3.1 | tbl_df | 20 | 3 |
| table3.2 | rencher | Table 3.2 | tbl_df | 6 | 7 |
| table3.3 | rencher | Table 3.4 | tbl_df | 10 | 4 |
| table3.4 | rencher | Table 3.4 | tbl_df | 46 | 6 |
| table3.5 | rencher | Table 3.5 | tbl_df | 11 | 6 |
| table3.6 | rencher | Table 3.6 | tbl_df | 20 | 5 |
| table3.7 | rencher | Table 3.7 | tbl_df | 25 | 4 |
| table3.8 | rencher | Table 3.8 | tbl_df | 49 | 6 |
| table4.2 | rencher | Table 4.2 Table 4.2 Hematology Data | tbl_df | 51 | 7 |
| table5.1 | rencher | Table 5.1 | tbl_df | 64 | 5 |
| table5.10 | rencher | Table 5.10 | tbl_df | 16 | 4 |
| table5.3 | rencher | Table 5.3 | tbl_df | 15 | 7 |
| table5.5 | rencher | Table 5.5 | tbl_df | 39 | 6 |
| table5.6 | rencher | Table 5.6 | tbl_df | 20 | 12 |
| table5.7 | rencher | Table 5.7 Comparison of Carriers and Noncarriers of Muscular Dystrophy | tbl_df | 73 | 7 |
| table5.8 | rencher | Table 5.8 | tbl_df | 19 | 6 |
| table5.9 | rencher | Table5.9 | tbl_df | 15 | 7 |
| table6.16 | rencher | Table 6.16 | tbl_df | 27 | 6 |
| table6.17 | rencher | Table 6.17 Judges' Scores on Fish Prepared by Three Methods | tbl_df | 12 | 12 |
| table6.18 | rencher | Table 6.18 | tbl_df | 60 | 7 |
| table6.19 | rencher | Table 6.19 | tbl_df | 20 | 13 |
| table6.21 | rencher | Table 6.21 Table 6.21 Weights of Cork Borings (eg) in Four Directions for 28 Trees | tbl_df | 28 | 5 |
| table6.22 | rencher | Table 6.22 Survival Times for Cancer Patients | tbl_df | 63 | 7 |
| table6.23 | rencher | Table 6.23 | tbl_df | 13 | 8 |
| table6.24 | rencher | Table 6.24 | tbl_df | 12 | 5 |
| table6.25 | rencher | Weekly Gains in Weight for 27 Rats | tbl_df | 27 | 6 |
| table6.26 | rencher | Coronary Sinus Potassium Measured at 2-Minute Intervals on Dogs Table 6.26 contains measurements of coronary sinus potassium at 2-minute intervals after coronary occlusion on four groups of dogs | tbl_df | 36 | 8 |
| table6.27 | rencher | Blood Pressure Data | tbl_df | 31 | 7 |
| table6.28 | rencher | Plasma Inorganic Phosphate (mg/dl) | tbl_df | 33 | 10 |
| table6.29 | rencher | Mandible Measurements | tbl_df | 18 | 11 |
| table6.6 | rencher | Table 6.6 Table 6.6 Two-Way Classification of Measurements on Bar Ste | tbl_df | 16 | 5 |
| table6.8 | rencher | Table 6.8 Table 6.8 Weight of Guinea Pigs Under 3 Levels of Vitamin E Supplements | tbl_df | 15 | 8 |
| table7.1 | rencher | Seishu Measurements | tbl_df | 30 | 10 |
| table7.2 | rencher | Table 7.2 Temperati, Humidity, and Evaporation | tbl_df | 46 | 11 |
| table8.1 | rencher | Table 8.1 | tbl_df | 12 | 3 |
| table8.3 | rencher | Table 8.3 Head Measurements for Three Groups | tbl_df | 90 | 7 |
| ex1 | rosario | Example temporal activity dataset | matrix | 5 | 12 |
| dataset | sanketphonetictranslator | Dataset Description | spec_tbl_df | 4 | 3 |
| bootstrap_iRAM_2node | pompom | Bootstrapped iRAM (including replications of iRAM and corresponding time profiles) for the bivariate time-series (simts2node) | list | | |
| bootstrap_iRAM_3node | pompom | Bootstrapped iRAM (including replications of iRAM and corresponding time profiles) for the 3-variate time-series (simts) | list | | |
| simts_2node | pompom | Simulated bivariate time-series data | data.frame | 200 | 2 |
| simts_3node | pompom | Simulated 3-variate time-series data | data.frame | 100 | 3 |
| true_beta_2node | pompom | The true beta matrix (4 by 4) used in simulation. | matrix | 4 | |
| true_beta_3node | pompom | The true beta matrix (6 by 6) used in simulation. | matrix | 6 | |
| usemmodelfit | pompom | Model fitbased on similated time-series by uSEM. | lavaan | | |
| ce10.blacklist | GreyListChIP | A 'GRanges' object representing ENCODE signal artefact or "black list" regions. | GRanges | | |
| ce11.blacklist | GreyListChIP | A 'GRanges' object representing ENCODE signal artefact or "black list" regions. | GRanges | | |
| dm3.blacklist | GreyListChIP | A 'GRanges' object representing ENCODE signal artefact or "black list" regions. | GRanges | | |
| dm6.blacklist | GreyListChIP | A 'GRanges' object representing ENCODE signal artefact or "black list" regions. | GRanges | | |
| grch37.blacklist | GreyListChIP | A 'GRanges' object representing ENCODE signal artefact or "black list" regions. | GRanges | | |
| grch38.blacklist | GreyListChIP | A 'GRanges' object representing ENCODE signal artefact or "black list" regions. | GRanges | | |
| greyList | GreyListChIP | A sample 'GreyList' object for use in examples. | GreyList | | |
| hg19.blacklist | GreyListChIP | A 'GRanges' object representing ENCODE signal artefact or "black list" regions. | GRanges | | |
| hg38.blacklist | GreyListChIP | A 'GRanges' object representing ENCODE signal artefact or "black list" regions. | GRanges | | |
| mm10.blacklist | GreyListChIP | A 'GRanges' object representing ENCODE signal artefact or "black list" regions. | GRanges | | |
| mm9.blacklist | GreyListChIP | A 'GRanges' object representing ENCODE signal artefact or "black list" regions. | GRanges | | |
| diabetes | FWDselect | Diabetes data. | data.frame | 442 | 11 |
| episode | FWDselect | Episode of SO2. Pollution incident data. | data.frame | 194 | 20 |
| pollution | FWDselect | Emission of SO2. Pollution incident data. | data.frame | 2000 | 19 |
| Ki67 | Qindex.data | Ki67 Data | data.frame | 366812 | 18 |
| l7 | satellite | Landsat 7 sample data | RasterStack | | |
| l8 | satellite | Landsat 8 sample data | RasterStack | | |
| ocsge_metaleurop | spacemodR | SF object defining very simplified OCS-GE soil cover metaleurop | sf | 9 | 12 |
| ocsge_species_dict | spacemodR | Valued weight between OCSGE layer and species | data.frame | 13090 | 6 |
| ref_ocsge | spacemodR | Nomenclature of OCS-GE soil cover | data.frame | 14 | 4 |
| roi_metaleurop | spacemodR | SF object defining ROI metaleurop | sf | 1 | 2 |
| sf_micromammals | spacemodR | DataBase of collected MicroMammals species | sf | 1426 | 27 |
| tatar | paths | The Legacy of Political Violence among Crimean Tatars | data.frame | 427 | 19 |
| welfare | paths | Issue Framing and Support for Welfare Reform | data.frame | 213 | 15 |
| dff4 | tsrsa | Daily Fama French 4 Factor Returns | xts | 24795 | 5 |
| dvix | tsrsa | Daily VIX Close | xts | 7809 | 1 |
| gw | tsrsa | Goyal Welch Equity Premium Data. | xts | 215 | 23 |
| mff4 | tsrsa | Monthly Fama French 4 Factor Returns | xts | 1128 | 5 |
| mff6 | tsrsa | Monthly Fama French 6 Factor Returns | xts | 690 | 7 |
| mind10 | tsrsa | Monthly Fama French 10 Industry Returns | xts | 1128 | 10 |
| mind5 | tsrsa | Monthly Fama French 5 Industry Returns | xts | 1128 | 5 |
| LDprofile | zalpha | Dataset containing an example LD profile | data.frame | 50 | 5 |
| snps | zalpha | Dataset containing details on simulated SNPs | data.frame | 20 | 12 |
| cell_line_fsa_list | trace | A list of fsa files | list | | |
| example_data | trace | example_data | data.frame | 1719 | 9 |
| example_data_repeat_table | trace | example_data_repeat_table | data.frame | 976 | 3 |
| metadata | trace | metadata | data.frame | 19 | 9 |
| RTF_testing | STMr | Reps to failure testing of 12 athletes | tbl_df | 36 | 7 |
| strength_training_log | STMr | Strength Training Log | tbl_df | 144 | 8 |
| exanet | bc3net | Example gene regulatory network for testing purposes only | igraph | | |
| exgensets | bc3net | Example gene sets defined from the CPDB database | list | | |
| expmat | bc3net | Test gene expression dataset | matrix | 100 | 100 |
| logindata.opal.demo | DSOpal | DataSHIELD login data file | data.frame | 3 | 6 |
| fmridata | BHMSMAfMRI | A simulated fMRI data for 3 subjects | list | | |
| simwide | mtvc | This data frame contains variables that are time varying, which might change at a specific time point. In particular, are referred to Graft Versus Host Disease, both Acute and Chronic, and relapse, which is the recurrence of the disease. In addition, this data frame contains specific dates, that refer to the first and last visit of the patient, and the respective dates of each time varying covariate, day from which that variable will change its value. | data.frame | 47 | 12 |
| education | selectMeta | Dataset open vs. traditional education on creativity | data.frame | 10 | 5 |
| passive_smoking | selectMeta | Dataset on the effect of environmental tobacco smoke | data.frame | 37 | 2 |
| pphpc_diff | micompr | Data from two implementations of the PPHPC model, one of which setup with a different parameter | grpoutputs | | |
| pphpc_noshuff | micompr | Data from two implementations of the PPHPC model, one of which has agent list shuffling deactivated | grpoutputs | | |
| pphpc_ok | micompr | Data from two similar implementations of the PPHPC model | grpoutputs | | |
| pphpc_testvlo | micompr | Data for testing variable length outputs | grpoutputs | | |
| orca | csquares | Killer whale realm | data.frame | 2058 | 2 |
| syngraphs | netseer | A dataset containing synthetic graphs | list | | |
| germancities | designmatch | Data from German cities before and after the Second World War | data.frame | 122 | 21 |
| lalonde | designmatch | Lalonde data set | data.frame | 614 | 10 |
| benefits | peopleanalytics | benefits | data.frame | 1471 | 3 |
| demographics | peopleanalytics | demographics | data.frame | 1470 | 7 |
| employees | peopleanalytics | employees | data.frame | 1470 | 36 |
| job | peopleanalytics | job | data.frame | 1470 | 6 |
| payroll | peopleanalytics | payroll | data.frame | 1470 | 6 |
| performance | peopleanalytics | performance | data.frame | 1470 | 3 |
| prior_employment | peopleanalytics | prior_employment | data.frame | 1470 | 2 |
| sentiment | peopleanalytics | sentiment | data.frame | 1470 | 6 |
| status | peopleanalytics | status | data.frame | 1470 | 2 |
| survey_responses | peopleanalytics | survey_responses | data.frame | 400 | 12 |
| tenure | peopleanalytics | tenure | data.frame | 1470 | 6 |
| turnover_trends | peopleanalytics | turnover_trends | data.frame | 3000 | 6 |
| countData | GLAMMGoF | Simulated count data | data.frame | 1000 | 5 |
| countModel_GAM | GLAMMGoF | Simulated count GAM example model | gam | | |
| countModel_GAMM | GLAMMGoF | Simulated count GAMM example model | gam | | |
| countModel_GAMM2 | GLAMMGoF | Simulated count GAMM2 example model | gam | | |
| countModel_GLM | GLAMMGoF | Simulated count GLM example model | glmmTMB | | |
| countModel_GLMM | GLAMMGoF | Simulated count GLMM example model | glmmTMB | | |
| countModel_GLMM2 | GLAMMGoF | Simulated count GLMM2 example model | glmmTMB | | |
| logitData | GLAMMGoF | Simulated binary data | data.frame | 1000 | 5 |
| logitModel_GAM | GLAMMGoF | Simulated binary GAM example model | gam | | |
| logitModel_GAMM | GLAMMGoF | Simulated binary GAMM example model | gam | | |
| logitModel_GAMM2 | GLAMMGoF | Simulated binary GAMM2 example model | gam | | |
| logitModel_GLM | GLAMMGoF | Simulated binary GLM example model | glmmTMB | | |
| logitModel_GLMM | GLAMMGoF | Simulated binary GLMM example model | glmmTMB | | |
| logitModel_GLMM2 | GLAMMGoF | Simulated binary GLMM2 example model | glmmTMB | | |
| rmet_example | rmet | Hourly weather data from INMET station A801 (Porto Alegre), 2023 | data.frame | 8663 | 25 |
| calWeights_movies | icarus | Calibration weights for 'data_employees' | data.frame | 15 | 4 |
| data_employees | icarus | A small example sample for calibration with Icarus | data.frame | 15 | 13 |
| dataPop | icarus | Test population for Icarus. | data.frame | 50000 | 16 |
| poptest_calmar | icarus | Calibration on population test - made on Calmar2 | data.frame | 1000 | 8 |
| poptest_calmar_nr | icarus | Calibration with nonresponse on population test - made on Calmar2 | data.frame | 718 | 8 |
| table_margins_1 | icarus | Margins for calibration of test population | matrix | 11 | 7 |
| table_margins_2 | icarus | Margins for calibration of test population | matrix | 11 | 7 |
| growthdata | growthcurver | Simulated growth curve data | tbl_df | 145 | 97 |
| lorbridge_data | lorbridge | Individual-Level VM and Minority Group Dataset | data.frame | 900 | 4 |
| tab_IQ | lorbridge | IQ Contingency Table (4 Races x 6 IQ Bins) | matrix | 4 | 6 |
| tab_IQ_VM | lorbridge | IQ x VM Contingency Table (6 IQ Bins x 6 VM Bins) | matrix | 6 | 6 |
| tab_VM | lorbridge | VM Contingency Table (4 Races x 6 VM Bins) | matrix | 4 | 6 |
| sampleSWCRTLarge | geeCRT | simulated large SW-CRT data | data.frame | 1508 | 10 |
| sampleSWCRTSmall | geeCRT | simulated small SW-CRT data | data.frame | 373 | 9 |
| gN3dist | SDPDmod | Distance between the centroids of NUTS3 regions in Germany | matrix | 401 | 401 |
| usa46 | SDPDmod | Spatial weights matrix of 46 USA states | matrix | 46 | 46 |
| grayleafspot_files | grayleafspotdata | Top-level deposited file manifest for S-BSST3199 | data.frame | 6 | 11 |
| grayleafspot_images | grayleafspotdata | Image manifest for the S-BSST3199 Magnaporthe colony dataset | data.frame | 49 | 18 |
| dt_banks | pedquant | dataset of bank stocks in sse | data.frame | 3645 | 11 |
| dt_ssec | pedquant | dataset of shanghai composite index | data.frame | 1215 | 11 |
| human_bodycontour | OrgHeatmap | Human body contour coordinate data | data.table | 13889 | 4 |
| human_organ_coord | OrgHeatmap | Human organ coordinate data | list | | |
| human_organ_systems | OrgHeatmap | Human Organ-system mapping data | data.frame | 41 | 2 |
| mouse_bodycontour | OrgHeatmap | Mouse body contour coordinate data | data.frame | 8527 | 4 |
| mouse_organ_coord | OrgHeatmap | Mouse organ coordinate data | list | | |
| mouse_organ_systems | OrgHeatmap | Mouse Organ-system mapping data | data.frame | 36 | 2 |
| organelle_bodycontour | OrgHeatmap | Organelle Body Contour Data | spec_tbl_df | 2300 | 4 |
| organelle_organ_coord | OrgHeatmap | Organelle Organ Coordinate Data | list | | |
| pie_template_list | tastypie | Available templates in 'tastypie' for 'pie_bake'. | character | | |
| pie_template_list_pro | tastypie | Available templates in 'tastypie' for 'pie_bake_pro'. | character | | |
| stock611 | robustfa | The Stocks Data - Year 2001 | data.frame | 611 | 12 |
| AccountingExample | ExploreTheData | Synthetic accounting dataset example, from Excel | data.frame | 8 | 6 |
| FirstAnomalyDataFrame | ExploreTheData | Dataset with missing values and other special cases | data.frame | 5 | 6 |
| pembro | nph | Reconstructed Data Set Based On Survival Curves In Burtess et al. 2019 | data.frame | 601 | 3 |
| data_1w_death | grafify | In vitro experiments measuring percentage cell death in three genotypes of cells. | data.frame | 15 | 3 |
| data_2w_Festing | grafify | Data from two-way ANOVA with randomised block design of treatments of strains of mice. | data.frame | 16 | 4 |
| data_2w_Tdeath | grafify | In vitro measurement of percentage cell death - two-way ANOVA design with repeated measures, and randomised blocks. | data.frame | 24 | 6 |
| data_cholesterol | grafify | Hierarchical data from 25 subjects either treated or not at 5 hospitals - two-way ANOVA design with repeated measures. | tbl_df | 50 | 4 |
| data_doubling_time | grafify | Doubling time of E.coli measured by 10 students three independent times. | tbl_df | 30 | 3 |
| data_t_pdiff | grafify | Matched data from two groups where difference between them is consistent. | data.frame | 20 | 3 |
| data_t_pratio | grafify | Matched data from two groups where ratio between them is consistent. | data.frame | 66 | 3 |
| data_zooplankton | grafify | Time-series data on zooplankton in lake Menon. | data.frame | 1127 | 8 |
| words | words | List of english words | data.frame | 175393 | 2 |
| finalReport | warabandi | Warabandi data for generate after making for calculation | data.frame | 59 | 9 |
| warabandi_data | warabandi | Warabandi data required for calculation | data.frame | 59 | 18 |
| sankeydf | sankeywheel | Example Data for Sankey Sankey Diagrams | tbl_df | 46 | 3 |
| seqdata | abtest | Synthetic Sequential Data | list | | |
| dentistry | randomLCA | Dental X-ray data | data.frame | 32 | 6 |
| genderrole | randomLCA | Gender Role Opinion Items | data.frame | 25 | 6 |
| hivtests | randomLCA | HIV testing data | data.frame | 16 | 5 |
| myocardial | randomLCA | Myocardial Infarction (MI) | data.frame | 11 | 5 |
| pap | randomLCA | Positive Action program implementation | data.frame | 606 | 11 |
| symptoms | randomLCA | Symptoms data | data.frame | 444 | 17 |
| uterinecarcinoma | randomLCA | Uterine Carcinoma Data | data.frame | 20 | 8 |
| Data_IATA | whereport | Data_IATA | data.frame | 8965 | 6 |
| sim_social_events | socialdrift | Simulated social interaction event log | data.frame | 591 | 7 |
| cal_example | srpi | Example calibration dataset | data.frame | 12 | 5 |
| val_example | srpi | Example validation dataset | data.frame | 12 | 5 |
| walking_shin_1 | imuf | University of Mannheim Real World Activities | tbl_df | 31946 | 6 |
| simul | gte | Simulated Data | data.frame | 100 | 3 |
| human_ref | oCELLoc | Human Cell Type Reference Data | matrix | 2006 | 170 |
| mouse_ref | oCELLoc | Mouse Cell Type Reference Data | matrix | 2468 | 125 |
| GastricCancer | ELYP | Gastric Cancer Data | matrix | 4 | |
| smallcell | ELYP | Smallcell Lung Cancer Data | data.frame | 121 | 4 |
| Artificial_3 | SparseMSE | Artificial data set to demonstrate possible instabilities | data.frame | 4 | 4 |
| hiermodels | SparseMSE | Hierarchical models | character | | |
| Korea | SparseMSE | Korea data | matrix | 7 | 4 |
| Kosovo | SparseMSE | Kosovo data | data.frame | 15 | 5 |
| Ned | SparseMSE | The Netherlands data | data.frame | 24 | 7 |
| Ned_5 | SparseMSE | Netherlands data five list version | data.frame | 17 | 6 |
| NewOrl | SparseMSE | New Orleans data | data.frame | 19 | 9 |
| NewOrl_5 | SparseMSE | New Orleans data five list version | data.frame | 14 | 6 |
| UKdat | SparseMSE | UK data | data.frame | 25 | 7 |
| UKdat_5 | SparseMSE | UK data five list version | data.frame | 18 | 6 |
| Western | SparseMSE | Victims related to sex trafficking in a U.S. Western site | data.frame | 13 | 6 |
| life_tables | R4GoodPersonalFinances | HMD life tables | tbl_df | 51948 | 6 |
| dmeladh | gggibbous | _Adh_ allele frequencies in Australasian _Drosophila melanogaster_ | data.frame | 34 | 6 |
| lunardist | gggibbous | Lunar distances and principal phases for 2019 | data.frame | 365 | 3 |
| training_load | ACWR | Training load dataframe | tbl_df | 84 | 5 |
| exampleBidirectional | CAGEfightR | Example CAGE Data | RangedSummarizedExperiment | | |
| exampleCTSSs | CAGEfightR | Example CAGE Data | RangedSummarizedExperiment | | |
| exampleDesign | CAGEfightR | Example CAGE Data | DFrame | | |
| exampleGenes | CAGEfightR | Example CAGE Data | RangedSummarizedExperiment | | |
| exampleUnidirectional | CAGEfightR | Example CAGE Data | RangedSummarizedExperiment | | |
| census11_deprivation_england_wales_lsoa11 | IMD | Census "Households by deprivation" data for LSOAs in England and Wales | tbl_df | 173765 | 4 |
| census11_deprivation_england_wales_msoa11 | IMD | Census "Households by deprivation" data for MSOAs in England and Wales | tbl_df | 36005 | 4 |
| census11_deprivation_northern_ireland_lgd14 | IMD | Census 2011 "Households by deprivation" data for Local Government Districtrs in Northern Ireland | tbl_df | 55 | 4 |
| census21_deprivation_england_wales_lsoa21 | IMD | Census "Households by deprivation" data for LSOAs in England and Wales | tbl_df | 178360 | 4 |
| census21_deprivation_england_wales_msoa21 | IMD | Census "Households by deprivation" data for MSOAs in England and Wales | tbl_df | 36320 | 4 |
| census21_deprivation_northern_ireland_lgd14 | IMD | Census 2021 "Households by deprivation" data for Local Government Districtrs in Northern Ireland | tbl_df | 55 | 4 |
| census21_deprivation_northern_ireland_sdz21 | IMD | Census 2021 "Households by deprivation" data for Super Data Zones in Northern Ireland | tbl_df | 4250 | 4 |
| cni2019_england_ward17 | IMD | Community Needs Index (2019) and Left Behind Areas for Wards (2017 codes) in England | spec_tbl_df | 7433 | 13 |
| cni2022_northern_ireland_soa11 | IMD | Community Needs Index (2022) and Left Behind Areas for Super Output Areas (2011 codes) in Northern Ireland | spec_tbl_df | 890 | 13 |
| cni2022_scotland_iz11 | IMD | Community Needs Index (2022) and Left Behind Areas for Intermediate Zones (2011 codes) in Scotland | spec_tbl_df | 1279 | 11 |
| cni2022_wales_msoa11 | IMD | Community Needs Index (2022) and Left Behind Areas for MSOAs (2011 codes) in Wales | spec_tbl_df | 410 | 12 |
| cni2023_england_lsoa21 | IMD | Community Needs Index (2023) and Left Behind Areas for LSOAs (2021 codes) in England | tbl_df | 33755 | 11 |
| imd_uk_lsoa | IMD | Index of Multiple Deprivation (multiple years) for LSOAs across the UK | tbl_df | 43530 | 2 |
| imd2000_england_ward98 | IMD | Index of Multiple Deprivation (2000) for Wards in England | tbl_df | 8414 | 29 |
| imd2004_england_lsoa01 | IMD | Index of Multiple Deprivation (2004) for LSOAs in England | tbl_df | 32482 | 6 |
| imd2004_england_lsoa01_subdomains | IMD | Index of Multiple Deprivation (2004) subdomains for LSOAs in England | tbl_df | 32482 | 28 |
| imd2004_england_ltla21 | IMD | Index of Multiple Deprivation (2004) for Local Authorities in England | tbl_df | 309 | 4 |
| imd2004_england_msoa11 | IMD | Index of Multiple Deprivation (2004) for MSOA in England | tbl_df | 6791 | 4 |
| imd2007_england_lsoa01 | IMD | Index of Multiple Deprivation (2007) for LSOAs in England | tbl_df | 32482 | 6 |
| imd2007_england_lsoa01_subdomains | IMD | Index of Multiple Deprivation (2007) subdomains for LSOAs in England | tbl_df | 32482 | 28 |
| imd2007_england_ltla21 | IMD | Index of Multiple Deprivation (2007) for Local Authorities in England | tbl_df | 309 | 4 |
| imd2007_england_msoa11 | IMD | Index of Multiple Deprivation (2007) for MSOA in England | tbl_df | 6791 | 4 |
| imd2010_england_lsoa01 | IMD | Index of Multiple Deprivation (2010) for LSOAs in England | tbl_df | 32482 | 25 |
| imd2010_england_lsoa01_subdomains | IMD | Index of Multiple Deprivation (2010) subdomains for LSOAs in England | tbl_df | 32482 | 19 |
| imd2010_england_ltla21 | IMD | Index of Multiple Deprivation (2010) for Local Authorities in England | tbl_df | 309 | 4 |
| imd2010_england_msoa11 | IMD | Index of Multiple Deprivation (2010) for MSOA in England | tbl_df | 6791 | 4 |
| imd2010_northern_ireland_soa01 | IMD | Index of Multiple Deprivation (2010) for Super Output Areas in Northern Ireland | tbl_df | 890 | 17 |
| imd2011_wales_lsoa01 | IMD | Index of Multiple Deprivation (2011) for LSOAs in Wales | tbl_df | 1896 | 19 |
| imd2012_scotland_dz01 | IMD | Index of Multiple Deprivation (2012) for Data Zones in Scotland | tbl_df | 6505 | 17 |
| imd2012_scotland_dz01_indicators | IMD | Underlying indicators for Scottish Index of Multiple Deprivation 2012 | tbl_df | 6505 | 47 |
| imd2015_england_lsoa11 | IMD | Index of Multiple Deprivation (2015) for LSOAs in England | tbl_df | 32844 | 7 |
| imd2015_england_lsoa11_subdomains | IMD | Index of Multiple Deprivation (2015) subdomains for LSOAs in England | tbl_df | 32844 | 19 |
| imd2015_england_ltla21 | IMD | Index of Multiple Deprivation (2015) for Local Authorities in England | tbl_df | 309 | 4 |
| imd2015_england_msoa11 | IMD | Index of Multiple Deprivation (2015) for MSOAs in England | tbl_df | 6791 | 4 |
| imd2016_scotland_dz11 | IMD | Index of Multiple Deprivation (2016) for Data Zones in Scotland | tbl_df | 6976 | 17 |
| imd2016_scotland_dz11_indicators | IMD | Underlying indicators for Scottish Index of Multiple Deprivation 2016 | tbl_df | 6976 | 34 |
| imd2017_northern_ireland_lgd14 | IMD | Index of Multiple Deprivation for Local Government Districts in Northern Ireland | tbl_df | 11 | 17 |
| imd2017_northern_ireland_soa01 | IMD | Index of Multiple Deprivation (2017) for Super Output Areas in Northern Ireland | tbl_df | 890 | 17 |
| imd2017_northern_ireland_soa01_indicators | IMD | Northern Ireland Multiple Deprivation Measure 2017 underlying indicators | tbl_df | 890 | 81 |
| imd2019_england_lsoa11 | IMD | Index of Multiple Deprivation for LSOAs (2011) in England | tbl_df | 32844 | 17 |
| imd2019_england_lsoa11_indicators | IMD | Underlying indicators for Index of Multiple Deprivation for LSOAs (2011) in England | tbl_df | 32844 | 29 |
| imd2019_england_lsoa11_subdomains | IMD | Sub-domains of deprivation for LSOAs (2011) in England | tbl_df | 32844 | 19 |
| imd2019_england_ltla19 | IMD | Index of Multiple Deprivation (2019) for Local Authorities (2019) in England | tbl_df | 317 | 18 |
| imd2019_england_ltla22 | IMD | Index of Multiple Deprivation (2019) for Local Authorities (2022) in England | tbl_df | 309 | 25 |
| imd2019_england_ltla23 | IMD | Index of Multiple Deprivation (2019) for Local Authorities (2023) in England | tbl_df | 296 | 25 |
| imd2019_england_ltla23_subdomains | IMD | Sub-domains of deprivation for Local Authorities (2023) in England | tbl_df | 296 | 13 |
| imd2019_england_msoa11 | IMD | Index of Multiple Deprivation for MSOAs (2011) in England | tbl_df | 6791 | 25 |
| imd2019_england_msoa11_subdomains | IMD | Sub-domains of deprivation for MSOAs (2011) in England | tbl_df | 6791 | 13 |
| imd2019_wales_lsoa11 | IMD | Index of Multiple Deprivation for LSOAs in Wales | tbl_df | 1909 | 28 |
| imd2019_wales_ltla22 | IMD | Index of Multiple Deprivation for Local Authorities in Wales | tbl_df | 22 | 28 |
| imd2019_wales_msoa11 | IMD | Index of Multiple Deprivation for MSOAs in Wales | tbl_df | 410 | 28 |
| imd2020_scotland_dz11 | IMD | Index of Multiple Deprivation (2020) for Data Zones in Scotland | tbl_df | 6976 | 17 |
| imd2020_scotland_dz11_indicators | IMD | Underlying indicators for Scottish Index of Multiple Deprivation 2020 | tbl_df | 6976 | 35 |
| imd2020_scotland_iz11 | IMD | Index of Multiple Deprivation for IZs in Scotland | tbl_df | 1279 | 17 |
| imd2020_scotland_ltla24 | IMD | Index of Multiple Deprivation for Council Areas in Scotland | tbl_df | 32 | 17 |
| imd2025_england_icb24 | IMD | Index of Multiple Deprivation (2025) for ICBs (2024) in England | tbl_df | 42 | 58 |
| imd2025_england_lrf | IMD | Index of Multiple Deprivation (2025) for LRFs (2024) in England | tbl_df | 38 | 58 |
| imd2025_england_lsoa21 | IMD | Index of Multiple Deprivation for LSOAs (2021) in England | tbl_df | 33755 | 25 |
| imd2025_england_lsoa21_indicators | IMD | Index of Multiple Deprivation (2025) Indicators for LSOAs (2021) in England | tbl_df | 33755 | 29 |
| imd2025_england_lsoa21_subdomains | IMD | Index of Multiple Deprivation (2025) Subdomain Scores for LSOAs (2021) in England | tbl_df | 33755 | 19 |
| imd2025_england_ltla24 | IMD | Index of Multiple Deprivation (2025) for Lower Tier Local Authorities (2024) in England | tbl_df | 296 | 58 |
| imd2025_wales_lsoa21 | IMD | Index of Multiple Deprivation for LSOAs (2021) in Wales. | tbl_df | 1917 | 29 |
| imd2025_wales_ltla24 | IMD | Index of Multiple Deprivation for LTLAs (2025) in Wales | tbl_df | 1782 | 5 |
| imd2025_wales_msoa21 | IMD | Index of Multiple Deprivation for MSOAs (2025) in Wales | tbl_df | 33048 | 5 |
| crypto_symbols | Riex | 'IEX' Crypto currency symbols | data.frame | 18 | 1 |
| dataLH | VirtualPop | dataLH data | data.frame | 29954 | 29 |
| dpopus | VirtualPop | dpopus data Population of the United States in 2019 reported in the HMD (Population.txt file) | data.frame | 111 | 2 |
| rates | VirtualPop | rates data | list | | |
| edgelist.humannet | SANTA | Pre-processed dataset for the SANTA vignette | data.frame | 58636 | 3 |
| edgelist.intact | SANTA | Pre-processed dataset for the SANTA vignette | data.frame | 21291 | 2 |
| g.bandyopadhyay.treated | SANTA | Pre-processed dataset for the SANTA vignette | igraph | | |
| g.bandyopadhyay.untreated | SANTA | Pre-processed dataset for the SANTA vignette | igraph | | |
| g.costanzo.cor | SANTA | Pre-processed dataset for the SANTA vignette | igraph | | |
| g.costanzo.raw | SANTA | Pre-processed dataset for the SANTA vignette | igraph | | |
| g.srivas.high | SANTA | Pre-processed dataset for the SANTA vignette | igraph | | |
| g.srivas.untreated | SANTA | Pre-processed dataset for the SANTA vignette | igraph | | |
| go.entrez | SANTA | Pre-processed dataset for the SANTA vignette | character | | |
| rnai.cheung | SANTA | Pre-processed dataset for the SANTA vignette | matrix | 10690 | 6 |
| all_biodepth | multifunc | Biodepth Data | data.frame | 480 | 128 |
| duffy_2003 | multifunc | Seagrass Mesocosm Data | data.frame | 40 | 25 |
| lubomPD | BenchHub | Human Gut Microbiome Abundance and Patient Classes | factor | | |
| x | BenchHub | Human Gut Microbiome Abundance and Patient Classes | matrix | 575 | 1192 |
| anatomy.data | RAPTOR | Anatomical measurements | data.frame | 18838 | 7 |
| dat.cooper15.3 | MAd | Data from Table 15.3 in The Handbook for Research Synthesis and Meta-Analysis (Cooper et al., 2009) | data.frame | 10 | 7 |
| dat.cooperA2 | MAd | Data from Table A2 in The Handbook for Research Synthesis and Meta-Analysis (Cooper et al., 2009) | data.frame | 19 | 4 |
| dat.hoyt | MAd | Subset of simulated data to demonstrate aggregation for dependent effect sizes | data.frame | 22 | 7 |
| dat.sim.es | MAd | Subset of simulated psychotherapy treatment studies (k=8) with dependent effect sizes | data.frame | 16 | 7 |
| dat.sim.final | MAd | Final aggregated dataset of simulated psychotherapy treatment studies (k=8) | data.frame | 8 | 5 |
| dat.sim.raw | MAd | Subset of simulated psychotherapy treatment studies (k=8) with means and SD | data.frame | 8 | 13 |
| dat.sim1 | MAd | Simulated psychotherapy treatment studies for 'outcome one' | data.frame | 1000 | 6 |
| dat.sim2 | MAd | Simulated psychotherapy treatment studies for 'outcome two' | data.frame | 1000 | 6 |
| bmt | CFC | The Bone Marrow Transplant Data | data.frame | 408 | 5 |
| canopy_oc_file | RHC | Canopy - oc Data | data.frame | 18 | 65 |
| trait_file | RHC | Trait Data | data.frame | 39 | 6 |
| petBrainGlioma | auRoc | Standard Uptake Value (SUV) for Brain Glioma Grading | data.frame | 10 | 3 |
| splice | grplasso | Dataset of Human Donor Splice Sites | data.frame | 400 | 8 |
| eusilc | laeken | Synthetic EU-SILC survey data | data.frame | 14827 | 28 |
| ses | laeken | Synthetic SES survey data | data.frame | 15691 | 27 |
| faux_census | fauxnaif | A small sample of a fabricated census-like dataset | spec_tbl_df | 20 | 6 |
| D | BiCausality | An example of aligned list of transactions | list | | |
| mat | BiCausality | A simulation dataset | matrix | 200 | |
| resC | BiCausality | An example of causal inference result | list | | |
| rice_cold | IGST | A gene expression dataset of rice under cold stress | data.frame | 251 | 36 |
| eusilc2 | rtip | Modified synthetic EU-SILC survey data | data.frame | 6000 | 7 |
| LCS2014 | rtip | Spanish living conditions survey data for the year 2014 | data.frame | 11965 | 7 |
| tables_acs | easycensus | Parsed Census SF1 and ACS Tables | list | | |
| tables_sf1 | easycensus | Parsed Census SF1 and ACS Tables | list | | |
| hpoly | scdb | pholy | sf | 3 | 3 |
| covid19_england_nhscalls_2020 | outbreaks | Potential COVID19 cases reported through NHS pathways | tbl_df | 253670 | 11 |
| dengue_fais_2011 | outbreaks | Dengue on the island of Fais, Micronesia, 2011 | tbl_df | 57 | 3 |
| dengue_yap_2011 | outbreaks | Dengue on the Yap Main Islands, Micronesia, 2011 | tbl_df | 185 | 3 |
| ebola_kikwit_1995 | outbreaks | Ebola in Kikwit, Democratic Republic of the Congo, 1995 | data.frame | 192 | 4 |
| ebola_sierraleone_2014 | outbreaks | Ebola in Sierra Leone, 2014 | data.frame | 11903 | 8 |
| ebola_sim | outbreaks | Simulated Ebola outbreak | list | | |
| ebola_sim_clean | outbreaks | Simulated Ebola outbreak | list | | |
| fluH7N9_china_2013 | outbreaks | Influenza A H7N9 in China, 2013 | data.frame | 136 | 8 |
| influenza_england_1978_school | outbreaks | Influenza in a boarding school in England, 1978 | data.frame | 14 | 3 |
| measles_hagelloch_1861 | outbreaks | Measles in Hagelloch, Germany, 1861 | data.frame | 188 | 12 |
| mers_korea_2015 | outbreaks | Middle East respiratory syndrome in South Korea, 2015 | list | | |
| nipah_malaysia | outbreaks | Nipah in Malaysia and Sinagapore, 1997-1999 | tbl_df | 49 | 5 |
| norovirus_derbyshire_2001_school | outbreaks | Norovirus in a primary school in Derbyshire, England, 2001 | data.frame | 492 | 5 |
| rabies_car_2003 | outbreaks | Dog Rabies in Central African Republic, 2003-2012 | list | | |
| s_enteritidis_pt59 | outbreaks | Salmonella Enteritidis PT59 outbreak | list | | |
| sars_canada_2003 | outbreaks | Severe Acute Respiratory Syndrome in Canada, 2003 | data.frame | 110 | 5 |
| sarscov2_who_2019 | outbreaks | SARS-CoV-2 World Health Organization Situation Reports 2019 Outbreak (COVID-19) | data.frame | 92 | 485 |
| smallpox_abakaliki_1967 | outbreaks | Smallpox in Abakaliki, Nigeria, 1967 | data.frame | 32 | 8 |
| varicella_sim_berlin | outbreaks | Simulated Varicella outbreak | data.frame | 500 | 13 |
| zika_girardot_2015 | outbreaks | Zika in Girardot, Colombia, 2015 | data.frame | 93 | 2 |
| zika_sanandres_2015 | outbreaks | Zika in San Andres, Colombia, 2015 | data.frame | 101 | 2 |
| zika_yap_2007 | outbreaks | Zika on the Yap Main Islands, Micronesia, 2007 | tbl_df | 29 | 3 |
| x3p4c | odetector | Synthetic data set consists of three variables with four clusters | matrix | 130 | 4 |
| hacide.test | ROSE | Half circle filled data | data.frame | 250 | 3 |
| hacide.train | ROSE | Half circle filled data | data.frame | 1000 | 3 |
| MSDecision | ADVICE | Multiple Sclerosis Decision Delay | data.frame | 54 | 15 |
| data_rec | rmdcev | Recreation data from Value of Nature to Canadians Survey | data.frame | 34000 | 8 |
| immLynx_example | immLynx | Example Single-Cell RNA-seq Data with TCR Information | SingleCellExperiment | | |
| fipsCounty | housingData | FIPS county data | data.frame | 3235 | 3 |
| geoCounty | housingData | County geolocation lookup table | data.frame | 3075 | 7 |
| housing | housingData | Housing data | data.frame | 247082 | 7 |
| wikiCounty | housingData | County Wikipedia lookup table | data.frame | 3143 | 5 |
| bladder | yarn | Bladder RNA-seq data from the GTEx consortium | ExpressionSet | | |
| skin | yarn | Skin RNA-seq data from the GTEx consortium | ExpressionSet | | |
| multiple_stress | stressaddition | Survival of Daphnia magna exposed to multiple stressors | data.frame | 58 | 4 |
| idoveData | iDOVE | Toy Dataset For Illustration | data.frame | 40000 | 6 |
| CM | GSA.UN | @title First four conditional moments of example data | list | | |
| data_Bstat | GSA.UN | @title First four conditional moments of example data | data.frame | 365 | 6 |
| out_set | GSA.UN | @title Results of a sample model | matrix | 500 | |
| parameters_set | GSA.UN | @title Set of parameters randomly generated | matrix | 500 | 10 |
| pp_names | GSA.UN | @title Example - parameters names | character | | |
| AgevsProter_Canada_full | sageR | Population du Canada par classes d'âge et provinces et territoires en 2020 | data.frame | 21 | 13 |
| AgevsSexe_Canada_full | sageR | Population du Canada par classes d'âge et sexe en 2020 | data.frame | 21 | 2 |
| air_pollution | sageR | Air pollution data | data.frame | 50 | 15 |
| bilan | sageR | Évaluation du risque de défaut d'entreprises | tbl_df | 1060 | 14 |
| bitcoin | sageR | Prix journalier du Bitcoin du 31/12/2014 au 15/05/2018 | tbl_df | 1233 | 2 |
| champignons | sageR | Caractéristiques de champignons | data.frame | 1000 | 8 |
| conso_temp | sageR | Consommation d'électricité | data.frame | 365 | 8 |
| Copies | sageR | Correction de copies | data.frame | 30 | 2 |
| Emploi_Etude_Age | sageR | Emploi par niveau d’études et taux d’emploi par groupe d’âge | data.frame | 37 | 6 |
| Essence | sageR | Prix de l'essence sans plomb 95 | data.frame | 30 | 2 |
| Europe | sageR | Taux d'emploi en % de la classe d'age | data.frame | 35 | 6 |
| Flux | sageR | Flux bancaires | numeric | | |
| HospitFull | sageR | Personnes hospitalisées atteintes de la Covid 19 (21/02/2021) | data.frame | 19 | 12 |
| Marque.Valeur | sageR | Prix de vente en fonction de la marque, format long ou empilé | data.frame | 90 | 2 |
| Marque.Valeur.large | sageR | Prix de vente en fonction de la marque, format large ou dépilé | data.frame | 30 | 3 |
| Personnes_Foyer | sageR | Nombre de personnes dans un foyer | data.frame | 6 | 2 |
| Precipitations_USA | sageR | Précipitations aux USA en 1975 | data.frame | 60 | 4 |
| presid_2012 | sageR | Résultats des élections présidentielles françaises de 2012 | data.frame | 96 | 13 |
| Proter_Canada | sageR | Population du Canada par provinces et territoires en 2020 | data.frame | 13 | 2 |
| ProtervsSexe_Canada | sageR | Population du Canada par classes d'âge et provinces et territoires en 2020 | data.frame | 13 | 2 |
| ReaFull | sageR | Personnes en réanimation atteintes de la Covid 19 (21/02/2021) | data.frame | 19 | 12 |
| Resistance | sageR | Résistance à l'éclatement | numeric | | |
| Richesse | sageR | Part du revenu national total équivalent en Euro en 2019 | data.frame | 10 | 36 |
| Secteur | sageR | Emploi par secteur et par pays dans les pays de l'OCDE en 2020-Q3. | data.frame | 34 | 6 |
| Sieges_Voix | sageR | Nombre de sièges et de voix dans l'Union européenne | data.frame | 27 | 4 |
| Total_Pays | sageR | Emploi total par pays dans les pays de l'OCDE en 2020-Q3. | data.frame | 34 | 2 |
| Total_Secteur | sageR | Emploi total par secteur dans les pays de l'OCDE en 2020-Q3. | data.frame | 5 | 3 |
| mix | crmn | Dilution mixture dataset. | ExpressionSet | | |
| authors | nberwp | NBER working paper authors | spec_tbl_df | 15930 | 6 |
| paper_authors | nberwp | NBER working paper authors | tbl_df | 69104 | 2 |
| paper_programs | nberwp | NBER working paper programs | tbl_df | 55813 | 2 |
| papers | nberwp | NBER working papers | tbl_df | 30083 | 5 |
| programs | nberwp | NBER working paper programs | spec_tbl_df | 21 | 3 |
| ncbi | ncbit | retrieve and build NBCI taxonomic data | taxdump | 1481741 | 6 |
| webshop | mixPHM | Webshop dataset for mixPHM package | matrix | 333 | 7 |
| example_baf | baf | Example BAF data from Adams County, WA | list | | |
| nfi_col | knfi | The Korean and English names of the column names | data.frame | 174 | 3 |
| nfi_donghae | knfi | National Forest Inventory data for Donghae-si, Gangwon-do, Republic of Korea for testing the function | list | | |
| data_flu_ses | discord | Flu Vaccination and SES Data | data.frame | 12686 | 23 |
| data_sample | discord | Sample Data from NLSY | tbl_df | 1200 | 9 |
| mikedata | ForeComp | Simulated data 1 | data.frame | 75 | 3 |
| PGDP | ForeComp | Price Index for Gross National Product/Gross Domestic Product (PGDP) | data.frame | 232 | 31 |
| RGDP | ForeComp | Real Gross National Product/Gross Domestic Product (RGDP) | data.frame | 232 | 31 |
| TBILL | ForeComp | 3-Month Treasury Bill Rate (TBILL) | data.frame | 232 | 31 |
| UNEMP | ForeComp | Civilian Unemployment Rate (UNEMP) | data.frame | 232 | 31 |
| americas_annual_data | denguedatahub | Dengue and severe dengue cases and deaths for subregions of the Americas | tbl_df | 899134 | 5 |
| cdc_casesby_week | denguedatahub | All dengue cases by week in US states and territories, 2010 - 2023 | spec_tbl_df | 742 | 4 |
| cdc_dengue_agesex | denguedatahub | All dengue cases by age group and sex in US states and territories, 2010 - 2023 | spec_tbl_df | 140 | 5 |
| cdc_dengue_casesbyjurisdiction | denguedatahub | All dengue cases by jurisdiction of residence in US states and territories, 2010 - 2023 | spec_tbl_df | 616 | 6 |
| cdc_dengue_countyyear | denguedatahub | All dengue cases by county of residence in US states and territories, 2010 - 2023 | spec_tbl_df | 3900 | 7 |
| cdc_local_dengue_cases | denguedatahub | Locally acquired dengue cases by year, 2010 - 2023 | spec_tbl_df | 14 | 3 |
| cdc_travel_associated_dengue_cases | denguedatahub | Travel associated dengue cases by year, 2010 - 2023 | spec_tbl_df | 14 | 3 |
| cdc_usa_dengue_infection | denguedatahub | Annual number of dengue fever infections in the USA | tbl_df | 9039 | 6 |
| china_annual_data | denguedatahub | Dengue related data in China | spec_tbl_df | 16 | 5 |
| india_annual_data | denguedatahub | DENGUE/DHF situation in India since 2017 | tbl_df | 432 | 5 |
| korea_dengue | denguedatahub | Imported dengue cases in Korea | tbl_df | 33 | 7 |
| level_of_risk | denguedatahub | Level of Dengue risk around the world | tbl_df | 293 | 4 |
| philippines_daily_data | denguedatahub | Daily number of dengue fever infections in Philippines | tbl_df | 32701 | 5 |
| singapore_weekly_data | denguedatahub | Weekly number of dengue fever infections in Sri Lanka | tbl_df | 272 | 3 |
| sl_annual | denguedatahub | Annual reported dengue cases in Sri Lanka | spec_tbl_df | 35 | 3 |
| sl_dengue_serotype | denguedatahub | Identification of dengue serotypes circulating in Sri Lanka | spec_tbl_df | 13 | 2 |
| sl_province_districts | denguedatahub | Provinces and Districts of Sri Lanka | tbl_df | 26 | 2 |
| sl_sites | denguedatahub | Type and proportion of breeding habitats positive for Aedes aegypti mosquitoes, across provinces in Sri Lanka, 2017. | spec_tbl_df | 9 | 7 |
| srilanka_weekly_data | denguedatahub | Weekly number of dengue fever infections in Sri Lanka | tbl_df | 26311 | 6 |
| taiwan_dengue | denguedatahub | Indigenous and imported dengue cases in Taiwan, 1987-2023. | tbl_df | 37 | 3 |
| world_annual | denguedatahub | Annual number of dengue fever infections around the world | data.frame | 2773284 | 10 |
| STE_2016_simple | ASGS.foyer | State shapefile | SpatialPolygonsDataFrame | | |
| data.xllim | xLLiM | Simulated data to run examples of usage of 'gllim' and 'sllim' functions | matrix | 52 | |
| data.xllim.test | xLLiM | Testing data to run examples of usage of 'gllim_inverse_map' and 'sllim_inverse_map' functions | matrix | 50 | |
| data.xllim.trueparameters | xLLiM | True parameters used to simulate the datasets 'data.xllim' and 'data.xllim.test' | list | | |
| Dunlap | ARPobservation | Dunlap et al.(1994) data | data.frame | 58 | 7 |
| Moes | ARPobservation | Moes(1998) data | data.frame | 80 | 7 |
| m.excess.c10sp9003 | FinCovRegularization | 10 stock and S&P 500 excess returns | matrix | 168 | 11 |
| nlsy97depression | mxmmod | NLSY97 Longitudinal Depression Scale Data | data.frame | 26952 | 10 |
| forest_biodiversity_data | MF.beta4 | Biodiversity data for six European forests | grouped_df | 481 | 4 |
| forest_function_data_normalized | MF.beta4 | Normalized ecosystem function data for six European forests | data.frame | 209 | 32 |
| forest_function_data_raw | MF.beta4 | Ecosystem function data for six European forests | data.frame | 209 | 32 |
| ROCR.hiv | ROCR | Data set: Support vector machines and neural networks applied to the prediction of HIV-1 coreceptor usage | list | | |
| ROCR.simple | ROCR | Data set: Simple artificial prediction data for use with ROCR | list | | |
| ROCR.xval | ROCR | Data set: Artificial cross-validation data for use with ROCR | list | | |
| massbank_alkanes | mssearchr | Mass spectra of alkanes | list | | |
| earth | globe | Major Coastline of Earth | list | | |
| covid1 | AEenrich | Covid Vaccine Adverse Event Data | tbl_df | 12500 | 5 |
| covid2 | AEenrich | Covid Vaccine Adverse Event Data | tbl_df | 2656 | 6 |
| group | AEenrich | Group Structure Data | tbl_df | 35339 | 2 |
| enzact | IBRtools | Biomarkers activity values with different treatments | data.frame | 28 | 6 |
| enzact_coef | IBRtools | Coefficient values for each biomarker within different treatments | data.frame | 1 | 6 |
| enzact2 | IBRtools | Biomarkers activity values with different treatments | data.frame | 38 | 6 |
| acupuncture | jarbes | Meta-analysis: 29 randomized controlled studies (RCT) assessing the efficacy of acupuncture treatments as complementary treatment in depression patients | spec_tbl_df | 32 | 18 |
| ai_distress | jarbes | Meta-analysis of RCTs evaluating the effect of AI agents to reduce psychological distress effects | tbl_df | 21 | 4 |
| colon_cancer | jarbes | Meta-analysis: Real World Evidence in metastatic colorectal cancer, comparing antiangiogenic treatments with chemotherapy | data.frame | 13 | 6 |
| covid19 | jarbes | Meta-analysis: Observational studies assessing the impact of risk factors on the severity and mortality of COVID-19 cases | data.frame | 89 | 12 |
| diabetes_eyes | jarbes | Individual Participant Data: Diabetic Eyes Data | data.frame | 97 | 270 |
| fnrpcr | jarbes | Meta-Analysis: Variation in False-Negative Rate of Reverse Transcriptase Polymerase Chain Reaction–Based SARS-CoV-2 Tests by Time Since Exposure | spec_tbl_df | 409 | 10 |
| ganzfeld | jarbes | Meta-Analysis of Free-Response Studies, 1992–2008: Assessing the Noise Reduction Model in Parapsychology | data.frame | 30 | 5 |
| healing | jarbes | Efficacy of diabetic foot healing using adjuvant treatments | data.frame | 35 | 9 |
| healingipd | jarbes | Individual participant data for diabetic patients | data.frame | 260 | 18 |
| hips | jarbes | Meta-analysis: generalized evidence synthesis of total hip replacement | tbl_df | 15 | 12 |
| longcovid | jarbes | Meta-analysis: Long-COVID Health Outcomes | tbl_df | 271 | 28 |
| macula_rwe | jarbes | Meta-analysis: 83 observational studies assessing the effectiveness of intravitreal therapy for diabetic maculaedema | data.frame | 82 | 24 |
| personality | jarbes | Meta-Analysis: Evolution of Personality Traits over time | tbl_df | 1394 | 18 |
| ppvcap | jarbes | Efficacy of Pneumococcal Polysaccharide Vaccine in Preventing Community Acquired Pneumonia | data.frame | 16 | 22 |
| ppvipd | jarbes | Efficacy of Pneumococcal Polysaccharide Vaccine in Preventing Invasive Pneumococcal Disease | data.frame | 11 | 6 |
| preterm | jarbes | Meta-analysis of Cognitive Outcomes in Very Preterm Children | data.frame | 61 | 18 |
| stemcells | jarbes | Meta-analysis: 31 randomized controled trials (RCTs) with reported discrepancies | tbl_df | 31 | 11 |
| trisomy21 | jarbes | Meta-analysis: Observational studies assessing the relationship of a positive ICPC (Isolated Choroid Plexus Cyst) on Trisomy 21 | tbl_df | 22 | 6 |
| tyge_2011 | jarbes | Mortality data from the tigecycline meta-analysis (Curcio & Verde, 2011) | spec_tbl_df | 14 | 8 |
| unihan_pinyin | hanyupinyin | Unihan Pinyin Dictionary | data.frame | 44348 | 4 |
| cpsmen | APCI | Labor force participation data for men from 1990 to 1979 in CPS | data.frame | 10000 | 7 |
| cpswomen | APCI | Women's labor force participation data from the 1990 to 2019 Current Population Survey (CPS) | data.frame | 10000 | 7 |
| simulation | APCI | Simulated Dataset | tbl_df | 10000 | 3 |
| women9017 | APCI | women9017 | data.frame | 1000 | 23 |
| bibRNews | svTools | A list of papers that where published in R News | data.frame | 205 | 10 |
| descriptionFields | svTools | List of possible fields in DESCRIPTION files | data.frame | 27 | 3 |
| topcolour.ig.vancouver.2014 | ig.vancouver.2014.topcolour | Instagram 2014 Vancouver dataset top colour | data.frame | 245736 | 2 |
| ERPT | pvars | Data set on the _Exchange Rate Pass-Through_ | data.frame | 861 | 21 |
| EU_w | pvars | Weights for the _Euro Monetary Policy Transmission_ | numeric | | |
| EURO | pvars | Data set on the _Euro Monetary Policy Transmission_ | list | | |
| ICAP | pvars | Data set on _Infrastructure Capital Stocks_ | data.frame | 3977 | 14 |
| MDEM | pvars | Data set for the _Monetary Demand Model_ | data.frame | 760 | 5 |
| MERM | pvars | Data set for the _Monetary Exchange Rate Model_ | data.frame | 2964 | 6 |
| PCAP | pvars | Data set on _Public Capital Stocks_ | data.frame | 1403 | 17 |
| PCIT | pvars | Data set on _Personal and Corporate Income Tax_ | data.frame | 228 | 22 |
| eusilcP | simFrame | Synthetic EU-SILC data | data.frame | 58654 | 28 |
| coasts.spdf | cartography | Coastline of Europe | SpatialLinesDataFrame | | |
| countries.spdf | cartography | Countries in the European Area | SpatialPolygonsDataFrame | | |
| frame.spdf | cartography | Frame around Europe | SpatialPolygonsDataFrame | | |
| graticule.spdf | cartography | Graticule around Europe | SpatialLines | | |
| nuts0.df | cartography | Nuts0 Dataset | data.frame | 34 | 10 |
| nuts0.spdf | cartography | Nuts0 Regions | SpatialPolygonsDataFrame | | |
| nuts1.df | cartography | Nuts1 Dataset | data.frame | 111 | 10 |
| nuts1.spdf | cartography | Nuts1 Regions | SpatialPolygonsDataFrame | | |
| nuts2.df | cartography | Nuts2 Dataset | data.frame | 310 | 10 |
| nuts2.spdf | cartography | Nuts2 Regions | SpatialPolygonsDataFrame | | |
| nuts3.df | cartography | Nuts3 Dataset | data.frame | 1448 | 7 |
| nuts3.spdf | cartography | Nuts3 Regions | SpatialPolygonsDataFrame | | |
| twincities.df | cartography | Twin Cities Dataset | data.frame | 6878 | 3 |
| world.spdf | cartography | World Background | SpatialPolygonsDataFrame | | |
| church | SparseVFC | The Church Photos | list | | |
| ENSO.dat | BINCOR | Equatorial Pacific SST anomalies from El Niño 3 region. | data.frame | 125 | 2 |
| ID31.dat | BINCOR | Unevenly-spaced pollen record from the marine sediments core (MD04-2845) collected on the southwestern European margin. | data.frame | 77 | 2 |
| ID32.dat | BINCOR | Unevenly-spaced pollen record from the marine sediments core (MD95-2039) collected on the southwestern European margin. | data.frame | 141 | 2 |
| NHSST.dat | BINCOR | Northern Hemisphere (NH) sea surface temperature (SST) anomalies. | data.frame | 125 | 2 |
| grid1 | gasper | Grid1 Graph from AG-Monien Graph Collection | list | | |
| minnesota | gasper | Minnesota Road Network | list | | |
| NYCdata | gasper | NYC Taxi Network Dataset | list | | |
| pittsburgh | gasper | Pittsburgh Census Tracts Network. | list | | |
| rlogo | gasper | R logo graph. | list | | |
| SuiteSparseData | gasper | Matrix Data from SuiteSparse Matrix Collection | data.frame | 2893 | 8 |
| allmeta | kgp | 1000 Genomes, SGDP, HGDP, and GGVP metadata | tbl_df | 212 | 8 |
| kgp3 | kgp | 1000 Genomes Project sample data (Phase 3) | tbl_df | 2504 | 10 |
| kgpe | kgp | 1000 Genomes Project sample data (Expanded) | tbl_df | 3202 | 11 |
| kgpmeta | kgp | 1000 Genomes Project population metadata | tbl_df | 26 | 7 |
| lalonde | kbal | Data from National Supported Work program and Panel Study in Income Dynamics | data.frame | 2675 | 14 |
| methydata | easyEWAS | Example Methylation Matrix | tbl_df | 1232 | 101 |
| sampledata | easyEWAS | Example Sample Metadata | data.frame | 100 | 5 |
| ae | BiostatsUHNplus | Simulated adverse events for patients receiving two study agents. | data.frame | 349 | 9 |
| demography | BiostatsUHNplus | Simulated demography for patients. | tbl_df | 12 | 2 |
| drug1_admin | BiostatsUHNplus | Simulated study agent 1 for patients. | tbl_df | 12 | 2 |
| drug2_admin | BiostatsUHNplus | Simulated study agent 2 for patients. | tbl_df | 12 | 2 |
| enrollment | BiostatsUHNplus | Enrollment data Simulated enrollment for patients. | tbl_df | 12 | 3 |
| ineligibility | BiostatsUHNplus | Simulated ineligibility for patients. | tbl_df | 11 | 2 |
| pandaResult | pandaR | Analysis result from PANDA algorithm on toy data | panda | | |
| pandaResultPairs | pandaR | Analysis result from PANDA algorithm on toy data converted into pairs | data.frame | 87000 | 4 |
| pandaToyData | pandaR | Toy gene expression, motif, and ppi data | list | | |
| df | trendchange | Example Data Frame for Innovative Polygon Trend Analysis | data.frame | 12 | 3 |
| x | trendchange | Annual flow of the Nile River | numeric | | |
| exeval_models | exeval | Built-in population PK/PKPD models | data.frame | 3 | 6 |
| tacrolimus_pk1_kidney | exeval | Tacrolimus pharmacokinetic data in kidney transplant patients | spec_tbl_df | 739 | 30 |
| kitchen_rolls | BayesTools | Kitchen Rolls data from Wagenmakers et al. (2015) replication study. | data.frame | 102 | 2 |
| census | lvplot | County demographics based on 1980 US Census | data.frame | 3068 | 10 |
| ontime | lvplot | Ontime Flight Data | tbl_df | 469968 | 16 |
| mtf.alt | gravy | Species Data and Altitude from Mt. Field, Tasmania. | data.frame | 167 | 1 |
| mtf01 | gravy | Species Data and Altitude from Mt. Field, Tasmania. | data.frame | 167 | 5 |
| EPA09 | BayesLN | Chrysene concentration data | numeric | | |
| fatigue | BayesLN | Low cycle fatigue data | data.frame | 22 | 2 |
| laminators | BayesLN | Laminators | data.frame | 39 | 2 |
| NCBC | BayesLN | Naval Construction Battalion Center data | data.frame | 17 | 2 |
| ReadingTime | BayesLN | Reading Times data | data.frame | 547 | 4 |
| immuno | CliquePercolation | Data: Immunoglobulin interaction network | qgraph | | |
| Obama | CliquePercolation | Data: Evaluative Reactions Toward Barack Obama (2012) | data.frame | 5914 | 10 |
| dat | coxsei | A simulated data set from a CoxSEI model | data.frame | 307 | 6 |
| sorlie | ahaz | Sorlie gene expressions | data.frame | 115 | 551 |
| dataExample_clmdu | lmap | Dummy data for clmdu example | data.frame | 200 | 9 |
| dataExample_clpca | lmap | Dummy data for clpca example | data.frame | 200 | 8 |
| dataExample_lmdu | lmap | Dummy data for lmdu example | data.frame | 234 | 13 |
| dataExample_lpca | lmap | Dummy data for lpca example | data.frame | 234 | 13 |
| dataExample_mru | lmap | Dummy data for mru example | data.frame | 234 | 6 |
| diabetes | lmap | Diabetes data | list | | |
| dpes | lmap | Dutch Parliamentary Election Study | list | | |
| kieskompas | lmap | Kieskompas data | list | | |
| liver | lmap | Liver | list | | |
| nesda | lmap | Netherlands Study for Depression and Anxiety | list | | |
| elowitz_data | noise | Expression of reporter genes in the D22 and M22 E. coli cells from Elowitz et al (2002). | list | | |
| yang_nl10 | noise | Expression of reporter genes in the NL010 E. coli cells from Yang et al (2014). | data.frame | 40683 | 3 |
| cbPalette13 | Ternary | Palettes compatible with colour blindness | character | | |
| cbPalette15 | Ternary | Palettes compatible with colour blindness | character | | |
| cbPalette8 | Ternary | Palettes compatible with colour blindness | character | | |
| holdridge | Ternary | Random sample of points for Holdridge plotting | data.frame | 39 | 4 |
| holdridgeClasses | Ternary | Names of the 38 classes defined with the Holdridge system | character | | |
| holdridgeClassesUp | Ternary | Names of the 38 classes defined with the Holdridge system | character | | |
| holdridgeLifeZones | Ternary | Names of the 38 classes defined with the Holdridge system | character | | |
| holdridgeLifeZonesUp | Ternary | Names of the 38 classes defined with the Holdridge system | character | | |
| DIA_MSstats_Normalized | MSstatsResponse | Example pre-processed DIA-MS dataset | list | | |
| Sugar | fsemipar | Sugar data | list | | |
| Tecator | fsemipar | Tecator data | list | | |
| onze_intercepts | nzilbb.vowels | Speaker random intercepts from GAMMs for 100 ONZE speakers | spec_tbl_df | 100 | 21 |
| onze_intercepts_full | nzilbb.vowels | Speaker random intercepts for 418 ONZE speakers | spec_tbl_df | 481 | 21 |
| onze_vowels | nzilbb.vowels | Monophthong data for random sample of speakers from the ONZE corpus | data.frame | 101572 | 8 |
| onze_vowels_full | nzilbb.vowels | Monophthong data for speakers from the ONZE corpus | data.frame | 414679 | 8 |
| qb_intervals | nzilbb.vowels | Formant and amplitude for intervals of QuakeBox monologues | grouped_df | 845 | 25 |
| qb_vowels | nzilbb.vowels | Formants from QuakeBox 1 | grouped_df | 26331 | 14 |
| sim_matrix | nzilbb.vowels | Similarity matrix from online perception test. | matrix | 38 | 38 |
| imports85 | RRF | The Automobile Data | data.frame | 205 | 26 |
| cmhc_cma_translation_data | cmhc | A dataset with geographic identifiers for CMHC and Census at the CMA level | tbl_df | 153 | 3 |
| cmhc_csd_translation_data | cmhc | A dataset with geographic identifiers for CMHC and Census at the CSD level | tbl_df | 5161 | 4 |
| cmhc_csd_translation_data_2023 | cmhc | A dataset with geographic identifiers for CMHC and Census at the CSD level for 2023 data portal version | tbl_df | 918 | 2 |
| cmhc_ct_translation_data | cmhc | A dataset with geographic identifiers for CMHC and Census at the CT level | tbl_df | 5934 | 18 |
| nyc_terrain_contours_sf | nycterrain | NYC bare-earth elevation contours | sf | 40 | 2 |
| schizo | mixcat | National Institute of Mental Health shizophrenia study | data.frame | 1603 | 4 |
| macroIT | SIRE | Italian Macroeconomic Data | tbl_df | 60 | 11 |
| caprion | pQTLdata | Caprion panel | grouped_df | 987 | 15 |
| inf1 | pQTLdata | Olink/INF1 panel | data.frame | 92 | 14 |
| Olink_Explore_1536 | pQTLdata | Olink/Explore 1536 panel | data.frame | 1472 | 3 |
| Olink_Explore_3072 | pQTLdata | Olink/Explore 3072 panels | data.frame | 2943 | 4 |
| Olink_Explore_HT | pQTLdata | Olink/Explore HT panels | data.frame | 5416 | 4 |
| Olink_qPCR | pQTLdata | Olink/qPCR panels | data.frame | 1112 | 7 |
| Olink_Target_96 | pQTLdata | Olink/Target 96 panels | data.frame | 1161 | 3 |
| scallop_inf1 | pQTLdata | Supplementary table 3 | data.frame | 180 | 19 |
| seer1980 | pQTLdata | Seer 1980 panel | data.frame | 1980 | 10 |
| SomaScan11k | pQTLdata | SomaScan 11k | data.frame | 10776 | 5 |
| SomaScan160410 | pQTLdata | Somascan panel | data.frame | 5178 | 10 |
| SomaScanV4.1 | pQTLdata | SomaScan v4.1 | data.frame | 7288 | 6 |
| swath_ms | pQTLdata | SWATH-MS panel | data.frame | 684 | 5 |
| SimData | jrSiCKLSNMF | A simulated dataset for use with jrSiCKLSNMF | list | | |
| SimSickleJrSmall | jrSiCKLSNMF | A small SickleJr object containing a subset of data from the SimData data object. Contains the completed analysis from the 'Getting Started' vignette for a small subset of 10 cells with 150 genes and 700 peaks. The clusters derived from this dataset are not accurate; this dataset is intended for use with code examples. | SickleJr | | |
| economic_growth | sValues | Economic Growth data | data.frame | 87 | 68 |
| economic_growth_original | sValues | Economic Growth data | data.frame | 139 | 68 |
| economic_growth_sala_i_martin | sValues | Economic Growth data | data.frame | 88 | 68 |
| covertype_small | rcppmlpackexamples | Covertype data subset used for classification | matrix | 55 | |
| loanData | rcppmlpackexamples | Loan data subset used for default prediction | data.table | 10000 | 4 |
| logisticRegressionData | rcppmlpackexamples | Logistic regression example data set | data.frame | 1000 | 3 |
| Andres2016 | soilfoodwebs | The soil food webs published for grazed and ungrazed plots in the Shortgrass Steppe long-term research station. | list | | |
| deRuiter1994 | soilfoodwebs | The soil food webs published for conventional (CON) and integrated (INT) management at Lovinkhoeve experimental farm. | list | | |
| Holtkamp2011 | soilfoodwebs | The soil food webs published along a chronosequence in the Netherlands. | list | | |
| Hunt1987 | soilfoodwebs | The soil food web published for CPER | list | | |
| intro_comm | soilfoodwebs | A baseline community for examples | list | | |
| Koltz2018 | soilfoodwebs | The soil food web published for an Arctic Tundra site | list | | |
| ramdata | ramchoice | ramdata: Simulated Choice Data | list | | |
| geneinfo_alias_human | multinichenetr | Gene annotation information: version 2 - january 2022 - suited for alias conversion | spec_tbl_df | 132328 | 3 |
| geneinfo_alias_mouse | multinichenetr | Gene annotation information: version 2 - january 2022 - suited for alias conversion | spec_tbl_df | 167942 | 3 |
| ligand_target_matrix_test | multinichenetr | Ligand-Target Matrix: subset NicheNet 2.0. | matrix | 1673 | 104 |
| sce | multinichenetr | SingleCellExperiment object containing scRNAseq data (subsampled) | SingleCellExperiment | | |
| dol.count | mefa | The Dolina Dataset | data.frame | 297 | 4 |
| dol.samp | mefa | The Dolina Dataset | data.frame | 24 | 2 |
| dol.taxa | mefa | The Dolina Dataset | data.frame | 121 | 4 |
| cigarettes | bsreg | | data.frame | 1380 | 12 |
| us_states | bsreg | | sf | 51 | 2 |
| NM_geo | scanstatistics | Longitude and latitude of New Mexico county seats. | data.frame | 33 | 7 |
| NM_map | scanstatistics | Data to plot the counties of New Mexico. | data.frame | 867 | 7 |
| NM_popcas | scanstatistics | Population and brain cancer cases in New Mexico counties during 1973-1991. | data.frame | 608 | 4 |
| baac00 | matchingMarkets | Townsend Thai Project BAAC Annual Resurvey, 2000 | data.frame | 292 | 8 |
| klein15a | matchingMarkets | MCMC results in Klein (2015a) | stabit2 | | |
| klein15b | matchingMarkets | Results of Monte Carlo Simulations in Klein (2015b) | list | | |
| cancer | BASSLINE | VA Lung Cancer Trial Dataset | matrix | 137 | 11 |
| Battig | WordPools | Battig - Montague Categorized Word Norms | data.frame | 5231 | 9 |
| CatProp | WordPools | Joelson-Hermann Category Properties | data.frame | 56 | 24 |
| Paivio | WordPools | Paivio, Yuille & Madigan Word Pool | data.frame | 925 | 9 |
| TWP | WordPools | The Toronto Word Pool | data.frame | 1093 | 12 |
| Belgium | SupMZ | Data contains Household Consumption (C) and GDP (Y) for Belgium from 1969 to 1998. | data.frame | 30 | 3 |
| Japan | SupMZ | Data contains Household Consumption (C) and GDP (Y) for Japan from 1978 to 2007. | data.frame | 30 | 3 |
| Srilanka | SupMZ | Data contains Household Consumption (C) and GDP (Y) for Sri Lanka from 1978 to 2006. | data.frame | 29 | 3 |
| drill | rotations | Drill data set | data.frame | 720 | 8 |
| nickel | rotations | Nickel electron backscatter diffraction data set | data.frame | 48286 | 13 |
| simulate_robregcc | robregcc | Simulated date for testing functions in the robregcc package (sparse setting). | list | | |
| simulate_robregcc_nsp | robregcc | Simulated date for testing functions in the robregcc package (non-sparse setting). | list | | |
| simulate_robregcc_sp | robregcc | Simulated date for testing functions in the robregcc package (sparse setting). | list | | |
| gene | iBMQ | Gene expression from whole eye tissue from n = 68 BXD RIS mice. | ExpressionSet | | |
| genepos | iBMQ | Gene position data frame | data.frame | 1000 | 4 |
| genotype.liver | iBMQ | A set of 290 SNPs from 60 F2 mice. | SnpSet | | |
| map.liver | iBMQ | SNP position data frame | data.frame | 290 | 3 |
| phenotype.liver | iBMQ | Gene expression from liver tissue from n = 60 F2 mice. | ExpressionSet | | |
| PPA.liver | iBMQ | A matrix with Posterior Probabilities of Association | matrix | 290 | 5000 |
| probe.liver | iBMQ | Gene position data frame | data.frame | 4427 | 4 |
| snp | iBMQ | A set of 1700 SNP from 68 BXD RIS mice. | SnpSet | | |
| snppos | iBMQ | SNP position data frame | data.frame | 1700 | 3 |
| asia | jti | Asia | tbl_df | 5000 | 8 |
| asia2 | jti | Asia2 | list | | |
| data.berka | ldt | Berka and Sochorova (1993) Dataset for Loan Default | list | | |
| data.pcp | ldt | IMF's Primary Commodity Prices | list | | |
| data.wdi | ldt | Long-run Growth from World Development Indicator Dataset | list | | |
| su1_contact_mat | HiSpaR | Example Hi-C Contact Matrix | matrix | 649 | 649 |
| SN2011fe | bigGP | SN2011fe Supernova Dataset | data.frame | 67275 | 6 |
| SN2011fe_covfunc | bigGP | SN2011fe Supernova Dataset | function | | |
| SN2011fe_crosscovfunc | bigGP | SN2011fe Supernova Dataset | function | | |
| SN2011fe_initialParams | bigGP | SN2011fe Supernova Dataset | numeric | | |
| SN2011fe_meanfunc | bigGP | SN2011fe Supernova Dataset | function | | |
| SN2011fe_mle | bigGP | SN2011fe Supernova Dataset | list | | |
| SN2011fe_mle_subset | bigGP | SN2011fe Supernova Dataset | list | | |
| SN2011fe_newdata | bigGP | SN2011fe Supernova Dataset | data.frame | 56781 | 3 |
| SN2011fe_newdata_subset | bigGP | SN2011fe Supernova Dataset | data.frame | 247 | 3 |
| SN2011fe_predcovfunc | bigGP | SN2011fe Supernova Dataset | function | | |
| SN2011fe_predmeanfunc | bigGP | SN2011fe Supernova Dataset | function | | |
| SN2011fe_subset | bigGP | SN2011fe Supernova Dataset | data.frame | 1292 | 6 |
| morse | fechner | Rothkopf's Morse Code Data | data.frame | 36 | 36 |
| noRegMin | fechner | Artificial Data: Regular Minimality Violated | data.frame | 10 | 10 |
| regMin | fechner | Artificial Data: Regular Minimality In Non-canonical Form | data.frame | 10 | 10 |
| wish | fechner | Wish's Morse-code-like Data | data.frame | 32 | 32 |
| returns3 | RobGARCHBoot | Matrix of time series returns for illustrative purposes | data.frame | 1000 | 3 |
| returnsexample | RobGARCHBoot | Time series returns for illustrative purposes | numeric | | |
| countBT | OVESEG | RNAseq count data downsampled from GSE60424 | list | | |
| RocheBT | OVESEG | mRNA expression data downsampled from GSE28490 (Roche) | list | | |
| GSDB_example | iPath | example gene set database (GSDB) | list | | |
| prad_cli | iPath | simulated clinical data for PRAD cancer patients | data.frame | 499 | 3 |
| prad_exprs | iPath | expression matrix for PRAD cancer patients in TCGA | matrix | 10000 | 91 |
| prad_inds | iPath | normal (0) and tumor (1) classes associated with PRAD expression data | numeric | | |
| CNAchrom11 | Rbeast | DNA copy number alteration data in array-based CGH data for Chromesome 11 | numeric | | |
| covid19 | Rbeast | Daily confirmed COVID19 cases and deaths in the world | data.frame | 1037 | 4 |
| googletrend_beach | Rbeast | A monthly Google Trend time series of the US search interest in the word "beach" | ts | | |
| imagestack | Rbeast | Decades of Landsat NDVI time series over a small area in Ohio | list | | |
| ohio | Rbeast | An irregular Landsat NDVI time series at an Ohio site | data.frame | 400 | 17 |
| simdata | Rbeast | Simulated time series to test BEAST | matrix | 300 | |
| Yellowstone | Rbeast | 30 years' AVHRR NDVI data at a Yellostone site | numeric | | |
| toy | akiFlagger | Toy dataset | data.table | 1078 | 6 |
| france_dept | cartogramR | Map of the population of mainland France (year 2018) | sf | 96 | 9 |
| france_rivers | cartogramR | Map of french rivers of mainland France | sf | 1353 | 3 |
| usa | cartogramR | Map of the number of electors in each state of the USA | sf | 49 | 25 |
| boeal | flan | Number of nalidixic acid-resistant mutants from Boe et al. (1994) | list | | |
| david | flan | Mutant counts and final numbers from H. L. David (1970) | list | | |
| luriadel | flan | Resistant bacteria counts from Luria and Delbruck (1943) | list | | |
| roster | flan | Mutant counts from Rosche and Foster (2000) | numeric | | |
| werhoff | flan | Rifampin-resistant mutant counts from Werngren and Hoffner (2003) | list | | |
| GLES17 | MultOrdRS | German Longitudinal Election Study 2017 (GLES17) | data.frame | 2036 | 11 |
| tenseness | MultOrdRS | Tenseness data from the Freiburg Complaint Checklist (tenseness) | data.frame | 1847 | 14 |
| concen | mixor | Twins Trouble Concentrating Data | data.frame | 16 | 6 |
| irt.data | mixor | Armed Servces Vocational Aptitude Battery | data.frame | 128 | 13 |
| norcag | mixor | Attitudes Towards Sex Data from the 1989 General Social Survey | data.frame | 105 | 6 |
| schizophrenia | mixor | National Institute of Mental Health Schizophrenia Collaborative Study | data.frame | 1603 | 9 |
| SmokeOnset | mixor | Smoking Onset Data | data.frame | 1556 | 10 |
| SmokingPrevention | mixor | Television School and Family Smoking Prevention and Cessation Project Data | data.frame | 1600 | 9 |
| Exampleindividual | PPMR | Individual level dataset | list | | |
| Examplesummary | PPMR | Summary level dataset | list | | |
| plasma | bigReg | plasma data from the HSAUR package | data.frame | 32 | 3 |
| grizzly_sorts | bayesqm | Grizzly bear reintroduction Q sorts | qsort_data | | |
| obesity_sorts | bayesqm | Childhood obesity Q sorts | qsort_data | | |
| constants | gt | The fundamental physical constants | tbl_df | 354 | 6 |
| countrypops | gt | Yearly populations of countries from 1960 to 2024 | tbl_df | 13975 | 5 |
| exibble | gt | A toy example tibble for testing with gt: exibble | tbl_df | 8 | 9 |
| films | gt | Feature films in competition at the Cannes Film Festival | tbl_df | 1873 | 8 |
| gibraltar | gt | Weather conditions in Gibraltar, May 2023 | tbl_df | 1431 | 10 |
| gtcars | gt | Deluxe automobiles from the 2014-2017 period | tbl_df | 47 | 15 |
| illness | gt | Lab tests for one suffering from an illness | tbl_df | 39 | 11 |
| metro | gt | The stations of the Paris Metro | tbl_df | 321 | 11 |
| nuclides | gt | Nuclide data | tbl_df | 3383 | 29 |
| peeps | gt | A table of personal information for people all over the world | tbl_df | 100 | 14 |
| photolysis | gt | Data on photolysis rates for gas-phase organic compounds | tbl_df | 34 | 10 |
| pizzaplace | gt | A year of pizza sales from a pizza place | tbl_df | 49574 | 7 |
| reactions | gt | Reaction rates for gas-phase atmospheric reactions of organic compounds | tbl_df | 1683 | 39 |
| rx_addv | gt | An ADDV-flavored clinical trial toy dataset | tbl_df | 291 | 20 |
| rx_adsl | gt | An ADSL-flavored clinical trial toy dataset | tbl_df | 182 | 14 |
| sp500 | gt | Daily S&P 500 Index data from 1950 to 2015 | tbl_df | 16607 | 7 |
| sza | gt | Twice hourly solar zenith angles by month & latitude | tbl_df | 816 | 4 |
| towny | gt | Populations of all municipalities in Ontario from 1996 to 2021 | tbl_df | 414 | 25 |
| agaricus.test | gpboost | Test part from Mushroom Data Set | list | | |
| agaricus.train | gpboost | Training part from Mushroom Data Set | list | | |
| bank | gpboost | Bank Marketing Data Set | data.table | 4521 | 17 |
| coords | gpboost | Coordinates for example data for the GPBoost package | matrix | 500 | |
| coords_test | gpboost | Test coordinates for example data for the GPBoost package | matrix | 5 | |
| group_data | gpboost | Gouping data for example data for the GPBoost package | matrix | 500 | 2 |
| group_data_test | gpboost | Test grouping data for example data for the GPBoost package | matrix | 5 | 2 |
| X | gpboost | Predictor variable data for example data for the GPBoost package | matrix | 500 | |
| X_test | gpboost | Test predictor variable data for example data for the GPBoost package | matrix | 5 | |
| y | gpboost | Response variable data for example data for the GPBoost package | numeric | | |
| sim_onlinesurr | OnlineSurr | Simulated longitudinal surrogate dataset for 'OnlineSurr' | data.frame | 600 | 5 |
| chickenPox | kDGLM | Hospital admissions by chicken pox in Brazil | data.frame | 120 | 6 |
| cornWheat | kDGLM | Corn and wheat prices from 1986 to 2014 | data.frame | 7252 | 5 |
| gastroBR | kDGLM | Hospital admissions from gastroenteritis in Brazil | tbl_df | 4212 | 4 |
| noticeSARI | kDGLM | SARI data from Belo Horizonte | data.frame | 118 | 8 |
| srx | Boruta | Small redundant XOR data | data.frame | 32 | 9 |
| blocks_ex | forestecology | Example cross validation grid | sf | 2 | 3 |
| census_1_ex | forestecology | Example input census data for package use | tbl_df | 10 | 7 |
| census_2_ex | forestecology | Example input census data for package use | tbl_df | 11 | 7 |
| census_2008_bw | forestecology | Michigan Big Woods research plot data | tbl_df | 27193 | 8 |
| census_2014_bw | forestecology | Michigan Big Woods research plot data | tbl_df | 48371 | 8 |
| comp_bayes_lm_ex | forestecology | Example bayesian competition model fit | comp_bayes_lm | | |
| focal_vs_comp_ex | forestecology | Example focal versus comp data frame | tbl_df | 6 | 7 |
| growth_ex | forestecology | Example growth data frame for small example | sf | 10 | 8 |
| growth_spatial_ex | forestecology | Example growth data frame with spatial data for small example | sf | 10 | 10 |
| growth_toy | forestecology | Example input data for 'create_focal_vs_comp()' | sf | 5 | 10 |
| species_bw | forestecology | Phylogenic groupings and trait based clustering of various tree species | tbl_df | 46 | 6 |
| study_region_bw | forestecology | Bigwoods forest study region boundary | sf | 1 | 2 |
| study_region_ex | forestecology | Study region for example data | sf | 1 | 2 |
| MadelonD | praznik | Pre-discretised Madelon dataset | list | | |
| data_sjm | longsurr | Example data for semiparametric joint estimation functions | list | | |
| full_data | longsurr | Example data to illustrate functions | tbl_df | 10100 | 5 |
| example.data | hetsurrSurv | Example data | list | | |
| example.data | hetsurr | Example data | list | | |
| biomasseX | multisensi | A factorial input design for the main example | data.frame | 2187 | 7 |
| biomasseY | multisensi | Output of the biomasse model for the plan provided in the package | data.frame | 2187 | 22 |
| Climat | multisensi | Climate data | data.frame | 3126 | 4 |
| d_example | Rsurrogate | Hypothetical data | list | | |
| d_example_me | Rsurrogate | Hypothetical data with replicate measurements | list | | |
| d_example_multiple | Rsurrogate | Hypothetical survival data with multiple surrogate markers | list | | |
| d_example_surv | Rsurrogate | Hypothetical survival data | list | | |
| d_tar_example | Rsurrogate | Example data for treatment-aware regularization | list | | |
| ex_survey | saros | ex_survey: Mockup dataset of a survey. | tbl_df | 300 | 32 |
| example_kir | PONG2 | PONG2 Example Dataset | hlaAlleleClass | | |
| example_mobj | PONG2 | PONG2 Example Dataset | hlaAttrBagObj | | |
| example_snp | PONG2 | PONG2 Example Dataset | hlaSNPGenoClass | | |
| cichlids | phylopath | Cichlid traits and the evolution of cooperative breeding. | data.frame | 69 | 5 |
| cichlids_tree | phylopath | Cichlid phylogeny. | phylo | | |
| red_list | phylopath | Data on brain size, life history and vulnerability to extinction | data.frame | 474 | 7 |
| red_list_tree | phylopath | Mammalian phylogeny | phylo | | |
| rhino | phylopath | Rhinogrades traits. | data.frame | 100 | 6 |
| rhino_tree | phylopath | Rhinogrades phylogeny. | phylo | | |
| WDI_data | WDI | World Development Indicators series and country information | list | | |
| cldr_examples | countrycode | List of CLDR country name codes and associated examples | data.frame | 554 | 2 |
| codelist | countrycode | Country Code Translation Data Frame (Cross-Sectional) | tbl_df | 292 | 628 |
| codelist_panel | countrycode | Country Code Translation Data Frame (Country-Year Panel) | tbl_df | 29752 | 60 |
| countryname_dict | countrycode | A dataframe of alternative country names in many languages. Used internally by the 'countryname' function. | data.frame | 24725 | 2 |
| Defendants2025 | vannstats | Defendants, 2025 (Individual-Level) | data.frame | 1738 | 11 |
| GSS2014 | vannstats | General Social Survey, 2014 | data.frame | 2538 | 676 |
| GSS2022 | vannstats | General Social Survey, 2022 | tbl_df | 4149 | 1291 |
| GSS2024 | vannstats | General Social Survey, 2024 | tbl_df | 3986 | 983 |
| howell_aids_long | vannstats | Howell Student AIDS Knowledge Data (Long Form) | data.frame | 12 | 3 |
| howell_aids_wide | vannstats | Howell Student AIDS Knowledge Data (Wide Form) | data.frame | 4 | 4 |
| UCR2015 | vannstats | Uniform Crime Reports, 2015 (County-Level) | data.frame | 3108 | 102 |
| WBBN2019 | vannstats | Well-Being and Basic Needs Survey, 2019 (Individual-Level) | data.frame | 7694 | 23 |
| BLOSUM | DECIPHER | BLOSUM Amino Acid Substitution Matrices | array | | |
| deltaGrules | DECIPHER | Free Energy of Hybridization of Probe/Target Quadruplets on Microarrays | array | | |
| deltaGrulesRNA | DECIPHER | Pseudoenergy Parameters for RNA Quadruplets | array | | |
| deltaHrules | DECIPHER | Change in Enthalpy of Hybridization of DNA/DNA Quadruplets in Solution | array | | |
| deltaHrulesRNA | DECIPHER | Change in Enthalpy of Hybridization of RNA/RNA Quadruplets in Solution | array | | |
| deltaSrules | DECIPHER | Change in Entropy of Hybridization of DNA/DNA Quadruplets in Solution | array | | |
| deltaSrulesRNA | DECIPHER | Change in Entropy of Hybridization of RNA/RNA Quadruplets in Solution | array | | |
| HEC_MI1 | DECIPHER | Mutual Information for Protein Secondary Structure Prediction | array | | |
| HEC_MI2 | DECIPHER | Mutual Information for Protein Secondary Structure Prediction | array | | |
| MIQS | DECIPHER | MIQS Amino Acid Substitution Matrix | matrix | 25 | 25 |
| MMLSUM | DECIPHER | MMLSUM Amino Acid Substitution Matrices | array | | |
| NonCodingRNA_Archaea | DECIPHER | NonCoding Models for Common Non-Coding RNA Families | list | | |
| NonCodingRNA_Bacteria | DECIPHER | NonCoding Models for Common Non-Coding RNA Families | list | | |
| NonCodingRNA_Eukarya | DECIPHER | NonCoding Models for Common Non-Coding RNA Families | list | | |
| PAM | DECIPHER | PAM Amino Acid Substitution Matrices | array | | |
| PFASUM | DECIPHER | PFASUM Amino Acid Substitution Matrices | array | | |
| RESTRICTION_ENZYMES | DECIPHER | Common Restriction Enzyme's Cut Sites | character | | |
| TrainingSet_16S | DECIPHER | Training Set for Classification of 16S rRNA Gene Sequences | Taxa | | |
| IDs | MetaboDynamics | KEGG ID mapping of metabolites in data set longitudinalMetabolomics | spec_tbl_df | 98 | 2 |
| longitudinalMetabolomics | MetaboDynamics | A simulated data set of longitudinal concentration tables of metabolites. | SummarizedExperiment | | |
| longitudinalMetabolomics_df | MetaboDynamics | A simulated data set of longitudinal concentration tables of metabolites. In contrast to "longitudinalMetabolomics" this dataset is in data frame format. It was simulated with a different seed compared to longitudinalMetabolomics so the results can deviate. | grouped_df | 2352 | 8 |
| metabolite_modules | MetaboDynamics | KEGG Query Results of experimental metabolites | spec_tbl_df | 348 | 8 |
| modules_compounds | MetaboDynamics | Background KEGG Query Results Of Functional Modules | data.frame | 3242 | 6 |
| BiopsyTrees | dowser | Example Ig lineage trees with biopsy reconstructions. | tbl_df | 8 | 5 |
| ExampleAirr | dowser | Example AIRR database | tbl_df | 389 | 30 |
| ExampleAirrTyCHE | dowser | Example AIRR database for TyCHE | spec_tbl_df | 200 | 29 |
| ExampleClones | dowser | Example Ig lineage trees | tbl_df | 89 | 5 |
| ExampleDbChangeo | dowser | Example Change-O database | tbl_df | 2000 | 15 |
| ExampleMixedClones | dowser | Example Multiple Partition Trees | tbl_df | 4 | 7 |
| ExampleMixedDb | dowser | Example Change-O database | tbl_df | 38 | 56 |
| IsotypeTrees | dowser | Example Ig lineage trees with isotype reconstructions. | tbl_df | 34 | 5 |
| TimeTrees | dowser | Example Ig lineage trees sampled over time. | tbl_df | 6 | 12 |
| limma | hypeR | Differential Expression | data.frame | 9682 | 5 |
| wgcna | hypeR | Co-expression Modules | list | | |
| acronym | TextAnalysisR | Acronym List | spec_tbl_df | 145 | 2 |
| SpecialEduTech | TextAnalysisR | Special education technology bibliographic data | tbl_df | 490 | 6 |
| stm_15 | TextAnalysisR | An example structure of a structural topic model | STM | | |
| stopwords_list | TextAnalysisR | Stopwords List | character | | |
| state_fips | wru | Dataset with FIPS codes for US states | tbl_df | 57 | 3 |
| voters | wru | Example voter file. | data.frame | 10 | 15 |
| cfbd_conf_types_df | cfbfastR | *Data in the package for reference* | data.frame | 11 | 4 |
| cfbd_play_type_df | cfbfastR | *Data in the package for reference* | data.frame | 45 | 3 |
| min_year_map_df | cfbfastR | *Data in the package for reference* | tbl_df | 99 | 4 |
| longitudinal_mixed_type_data | clustGLMM | A Simulated Dataset of Longitudinal Mixed-Type Data | data.frame | 400 | 32 |
| psurvey | clustGLMM | A Simulated Panel Survey Data | data.frame | 4512 | 6 |
| psurvey_latent | clustGLMM | A Simulated Panel Survey Data | data.frame | 4512 | 6 |
| encyclopedists | plume | Famous encyclopedists | tbl_df | 4 | 10 |
| encyclopedists_fr | plume | Famous encyclopedists | tbl_df | 4 | 10 |
| chaco | stgam | Chaco dry rainforest data (2012-2022) | sf | 2000 | 12 |
| Haplotype_frequencies | immunogenetr | Ten HLA-A~C~B~DRB3/4/5~DRB1~DQA1~DQB1~DPA1~DPB1 haplotypes and their frequencies in the global population. | tbl_df | 10 | 12 |
| HLA_dictionary | immunogenetr | Data on HLA alleles in the 2008 HLA dictionary | tbl_df | 100 | 3 |
| HLA_typing_1 | immunogenetr | Synthetic HLA typing data for 10 individuals for the HLA-A, B, C, DRB1, DRB3/4/5, DQB1, DQA1, DPB1 and DPA1 loci. | tbl_df | 10 | 19 |
| HLA_typing_LIS | immunogenetr | Synthetic HLA typing data for 10 individuals for the HLA-A, B, C, DRB1, DRB3, DRB4, DRB5, DQB1, DQA1, DPB1 and DPA1 loci. Data formatted to resemble a HistoTrac table. | tbl_df | 10 | 23 |
| HLA_typing_synthetic_LIS | immunogenetr | Synthetic clinical-laboratory HLA typing table (HistoTrac "Patient" layout). | tbl_df | 63 | 53 |
| mismatch_table_2010 | immunogenetr | Consensus mismatch numbers for every possible allele combination at a single locus, from the 2010 publication. | tbl_df | 26 | 6 |
| mismatch_table_2016 | immunogenetr | Consensus mismatch numbers for every possible allele combination at a single locus, from the 2016 publication. | tbl_df | 26 | 5 |
| circa_table_genescarab | GeneSCARAB | circa_table_genescarab | data.frame | 5272 | 15 |
| tonedata | fpc | Tone perception data | data.frame | 150 | 2 |
| ace_codornices | estadisticaFcaUnl | Acetil colinesterasa en codornices | data.frame | 10 | 2 |
| actividad | estadisticaFcaUnl | Actividad predominante de explotaciones agropecuarias | data.frame | 12 | 3 |
| agua_forrajera | estadisticaFcaUnl | Relación entre agua aplicada y rendimiento en especie forrajera | data.frame | 5 | 2 |
| albumen | estadisticaFcaUnl | Variación en el color del albumen de semillas de plantas de Mendel | data.frame | 10 | 2 |
| algodon | estadisticaFcaUnl | Clasificación de plantas de algodón por color de corola y forma de hoja | data.frame | 4 | 3 |
| alimento | estadisticaFcaUnl | Contenido vitamínico en alimento animal | data.frame | 16 | 1 |
| almacenamiento_carne | estadisticaFcaUnl | Técnicas de envasado de carne | data.frame | 12 | 3 |
| area_trigo | estadisticaFcaUnl | Relación entre tamaño de explotación y área sembrada con trigo | data.frame | 12 | 2 |
| arroz_pd | estadisticaFcaUnl | Riego en arroz | data.frame | 24 | 4 |
| arvejas | estadisticaFcaUnl | Frecuencias fenotípicas en arvejas de Mendel | data.frame | 4 | 2 |
| balanceado_cerdos | estadisticaFcaUnl | Aumento de peso en cerdos con alimento balanceado | data.frame | 20 | 1 |
| barrenador | estadisticaFcaUnl | Eficacia de insecticida para control de barrenador del brote de soja | data.frame | 2050 | 3 |
| caballos | estadisticaFcaUnl | Medicamentos en caballos de carrera | data.frame | 12 | 2 |
| calificaciones | estadisticaFcaUnl | Calificaciones de alumnos en Física y Matemática | data.frame | 12 | 2 |
| camarones | estadisticaFcaUnl | Efecto de temperatura, densidad y salinidad en el crecimiento de camarones | data.frame | 36 | 5 |
| cana_azucar | estadisticaFcaUnl | Variedades de caña de azúcar | data.frame | 25 | 4 |
| cana_pd | estadisticaFcaUnl | Fechas de plantación en caña de azúcar | data.frame | 45 | 4 |
| cangrejo_ermitanio | estadisticaFcaUnl | Recuento de cangrejos ermitaños | data.frame | 150 | 2 |
| capturados | estadisticaFcaUnl | Captura de insectos en intervalos de tiempo | data.frame | 6 | 2 |
| cebada_salinidad | estadisticaFcaUnl | Efecto de salinidad y desarrollo en el consumo de agua en cebada | data.frame | 18 | 5 |
| cemento | estadisticaFcaUnl | Calidad de mezclas de cemento de diferentes proveedores | data.frame | 24 | 4 |
| cerdos | estadisticaFcaUnl | Ganancia de peso en cerdos según tipo de alimentación | data.frame | 16 | 3 |
| cesped_compactacion | estadisticaFcaUnl | Durabilidad de variedades de césped bajo compactación | data.frame | 54 | 4 |
| cesped_golf | estadisticaFcaUnl | Calidad de césped en campo de golf bajo fertilización y acumulación de biomasa | data.frame | 24 | 5 |
| citricos | estadisticaFcaUnl | Rendimiento de fruta en cítricos bajo diferentes pesticidas | data.frame | 24 | 4 |
| coleoptero | estadisticaFcaUnl | Pérdida de agua en coleópteros de la harina | data.frame | 9 | 2 |
| colesterol | estadisticaFcaUnl | Nivel de colesterol en sangre por tipo de dieta | data.frame | 43 | 3 |
| competencia | estadisticaFcaUnl | Competencia intraespecífica en cultivos | data.frame | 15 | 3 |
| conc_glucosa | estadisticaFcaUnl | Concentración de glucosa | data.frame | 18 | 4 |
| concentracion_insecticida | estadisticaFcaUnl | Comparación de métodos de estimación de concentración de insecticida | data.frame | 20 | 2 |
| conservante_frutilla | estadisticaFcaUnl | Conservantes en frutillas congeladas | data.frame | 32 | 2 |
| corderos | estadisticaFcaUnl | Peso de corderos | data.frame | 20 | 1 |
| corderos_des | estadisticaFcaUnl | Efecto de DES en fosfolípidos de corderos | data.frame | 20 | 3 |
| densidad_trigo_riego_suelo | estadisticaFcaUnl | Densidad de siembra en trigo bajo riego y gradientes de suelo | data.frame | 25 | 6 |
| dieta_bufalos | estadisticaFcaUnl | Pérdida de peso en búfalos bajo dieta hipocalórica | data.frame | 20 | 3 |
| dieta_cerdos | estadisticaFcaUnl | Efecto de la dieta en cerdos | data.frame | 26 | 2 |
| dieta_pavos | estadisticaFcaUnl | Dietas en pavos | data.frame | 30 | 2 |
| dietas_novillos | estadisticaFcaUnl | Dietas en novillos | data.frame | 20 | 3 |
| dietas_pollos | estadisticaFcaUnl | Ganancia de peso en pollos por ración de alimento | data.frame | 16 | 3 |
| digest_in_vitro | estadisticaFcaUnl | Digestibilidad in vitro | data.frame | 20 | 4 |
| dms_ensilaje | estadisticaFcaUnl | Digestibilidad de materia seca en ensilaje de maíz | data.frame | 13 | 2 |
| dosis_tomate | estadisticaFcaUnl | Efecto de dosis de fertilizante en crecimiento de tomate | data.frame | 11 | 3 |
| educacion | estadisticaFcaUnl | Permanencia de estudiantes universitarios por estado civil | data.frame | 4 | 3 |
| efectividad_sembradora | estadisticaFcaUnl | Comparación de rendimiento por tipo de máquina sembradora | data.frame | 20 | 3 |
| energia_abejas | estadisticaFcaUnl | Energía en abejas | data.frame | 27 | 6 |
| especies | estadisticaFcaUnl | Especies | data.frame | 6 | 3 |
| establecimientos | estadisticaFcaUnl | Superficie de establecimientos agropecuarios | data.frame | 12 | 1 |
| estrogeno | estadisticaFcaUnl | Actividad estrogénica | data.frame | 28 | 2 |
| fert_melon | estadisticaFcaUnl | Fertilización en melón | data.frame | 16 | 4 |
| fert_trigo_bajo_riego | estadisticaFcaUnl | Fertilización de trigo bajo riego | data.frame | 24 | 3 |
| fertilizante_pastizal | estadisticaFcaUnl | Fertilización en _Bouteloua gracilis_ | data.frame | 25 | 3 |
| fibra_quimico | estadisticaFcaUnl | Compuestos químicos en fibra | data.frame | 20 | 3 |
| fijacion_N | estadisticaFcaUnl | Fijación de Nitrógeno | data.frame | 48 | 5 |
| forestal_alt | estadisticaFcaUnl | Altura de árboles en ensayo forestal en San Agustín | data.frame | 40 | 4 |
| fungicida | estadisticaFcaUnl | Efectividad de dosis de fungicida para el control de roya en trigo | data.frame | 13 | 2 |
| gallinas | estadisticaFcaUnl | Función pituitaria en gallinas | data.frame | 25 | 2 |
| ganancia_novillos | estadisticaFcaUnl | Ganancia de peso en novillos Holando con suplementación mineral | data.frame | 28 | 3 |
| germinacion | estadisticaFcaUnl | Pruebas de germinación con tratamientos químicos | data.frame | 6 | 3 |
| gladiolos | estadisticaFcaUnl | Gladiolos y herbicidas | data.frame | 16 | 3 |
| glifosato | estadisticaFcaUnl | Peso de empaque de cajas de Glifosato | data.frame | 1150 | 3 |
| guayule | estadisticaFcaUnl | Contenido de caucho en guayule | data.frame | 27 | 2 |
| hibridos_maiz | estadisticaFcaUnl | Híbridos de maíz | data.frame | 32 | 2 |
| hileras_maiz | estadisticaFcaUnl | Relación entre peso de espiga y número de hileras en maíz | data.frame | 18 | 2 |
| hormona_ovario | estadisticaFcaUnl | Hormona foliculoestimulante en vacas | data.frame | 16 | 4 |
| huevos | estadisticaFcaUnl | Relación entre peso y producción de huevos en langostas | data.frame | 30 | 2 |
| inhibidor_N | estadisticaFcaUnl | Inhibidor de nitrificación | data.frame | 18 | 4 |
| insecticida_poroto | estadisticaFcaUnl | Insecticidas en poroto | data.frame | 12 | 3 |
| insectos | estadisticaFcaUnl | Distribución de insectos en plantas | data.frame | 6 | 2 |
| invernadero_spartina | estadisticaFcaUnl | Crecimiento de _Spartina altemiflora_ | data.frame | 48 | 3 |
| inyeccion_vaquillas | estadisticaFcaUnl | Métodos de inyección en vaquillas | data.frame | 16 | 6 |
| irrigacion_naranjo | estadisticaFcaUnl | Irrigación en naranjos | data.frame | 48 | 3 |
| labores_remolacha | estadisticaFcaUnl | Labores culturales en remolacha azucarera | data.frame | 9 | 4 |
| larvas_cesped | estadisticaFcaUnl | Insecticidas en césped | data.frame | 60 | 5 |
| lechuga | estadisticaFcaUnl | Respuesta de la lechuga a la fertilización nitrogenada | data.frame | 20 | 4 |
| lesiones_hojas | estadisticaFcaUnl | Lesiones virales en hojas de tabaco | data.frame | 16 | 3 |
| lisina_pollos | estadisticaFcaUnl | Suplementación con Lisina en pollos | data.frame | 12 | 3 |
| lluvia_forraje | estadisticaFcaUnl | Rendimiento de forraje en relación a la lluvia caída | data.frame | 10 | 2 |
| lodos | estadisticaFcaUnl | Acumulación de cinc en cebada fertilizada con lodos | data.frame | 36 | 5 |
| lotes_BA | estadisticaFcaUnl | Datos de fertilidad 0-15 y 15-30 cm de Argiudoles en 39 pares de lotes y bajo alambrado del departamento Las Colonias. | data.frame | 156 | 15 |
| maiz | estadisticaFcaUnl | Densidad de plantas en lote de maíz | data.frame | 6 | 2 |
| maiz2_pd | estadisticaFcaUnl | Abonos en maíz | data.frame | 36 | 4 |
| met_pesados | estadisticaFcaUnl | Metales pesados en lodos | data.frame | 36 | 5 |
| molinos | estadisticaFcaUnl | Resistencia al viento en torres de molinos | data.frame | 15 | 1 |
| monedas | estadisticaFcaUnl | Lanzamiento de cuatro monedas | data.frame | 5 | 2 |
| MS_girasol | estadisticaFcaUnl | Porcentaje de materia seca en plantas de girasol | data.frame | 30 | 3 |
| N_suelo | estadisticaFcaUnl | Contenido de nitrógeno nítrico en suelos | data.frame | 50 | 3 |
| nabos | estadisticaFcaUnl | Ácido ascórbico en nabos | data.frame | 15 | 3 |
| nectar_trebol_rojo | estadisticaFcaUnl | Néctar en trébol rojo | data.frame | 20 | 3 |
| nematodes_durazno | estadisticaFcaUnl | Nematodes en durazno | data.frame | 18 | 2 |
| neumaticos | estadisticaFcaUnl | Comparación de duración de neumáticos de camiones | data.frame | 30 | 3 |
| neumaticos_tractores | estadisticaFcaUnl | Duración de neumáticos de tractores | data.frame | 15 | 1 |
| ovejas | estadisticaFcaUnl | Ganancia en peso de ovejas | data.frame | 25 | 1 |
| P_maiz | estadisticaFcaUnl | Relación de fósforo inicial en suelo y recuperado por plantas de maíz | data.frame | 9 | 2 |
| penetrometro | estadisticaFcaUnl | Resistencia mecánica a la penetración de suelo | data.frame | 2000 | 3 |
| pesada_novillos | estadisticaFcaUnl | Datos del pesaje de dos tropas de novillos extraídos del sistema de registros del establecimiento "La Escondida S.A." | data.frame | 1842 | 13 |
| pesada_terneros | estadisticaFcaUnl | Pesaje de 1598 terneros que ingresaron al sistema de engorde de la estancia "La Escondida S.A." | data.frame | 1598 | 8 |
| peso_balanceado | estadisticaFcaUnl | Pesos de bolsas de alimento balanceado para cerdos | data.frame | 55 | 3 |
| peso_vaquillonas | estadisticaFcaUnl | Anabólicos en vaquillonas | data.frame | 11 | 2 |
| pezoneras | estadisticaFcaUnl | Duración de pezoneras de ordeño | data.frame | 20 | 1 |
| pH_laboratorio | estadisticaFcaUnl | Error en pH-metros | data.frame | 24 | 3 |
| plantacion | estadisticaFcaUnl | Altura de ejemplares de Eucalyptus | data.frame | 10 | 1 |
| plantacion_dap | estadisticaFcaUnl | Diámetro a la altura del pecho de ejemplares de Eucalyptus | data.frame | 10 | 1 |
| pollos | estadisticaFcaUnl | Suplementación con Lisina en pollos y ganancia de peso | data.frame | 12 | 2 |
| productores | estadisticaFcaUnl | Producción de lotes por grupo de productores | data.frame | 9 | 3 |
| prolactina_vacas | estadisticaFcaUnl | Prolactina en vaquillas | data.frame | 9 | 6 |
| proteina_caseina | estadisticaFcaUnl | Contenido de proteína bruta y caseína en leche | data.frame | 23 | 2 |
| pulsaciones_caballos | estadisticaFcaUnl | Pulsaciones del corazón de caballos de carrera | data.frame | 15 | 1 |
| raciones | estadisticaFcaUnl | Ganancia diaria de peso en novillos por tipo de ración | data.frame | 24 | 3 |
| raciones_pollos | estadisticaFcaUnl | Raciones alimenticias para pollos | data.frame | 16 | 4 |
| razas | estadisticaFcaUnl | Relación entre tipo de parto y raza en vacas lecheras | data.frame | 6 | 3 |
| resist_algodon | estadisticaFcaUnl | Resistencia de fibra sintética con algodón | data.frame | 25 | 4 |
| riesgo_arbolado | estadisticaFcaUnl | Datos de un relevamiento de 677 arboles de la vía pública de la ciudad de Rafaela para la determinación de riesgo según la metodología de la Sociedad Internacional de Arboricultura (ISA). | data.frame | 677 | 9 |
| rollos_tela | estadisticaFcaUnl | Resistencia a la tensión de rollos de tela | data.frame | 20 | 3 |
| ryegrass | estadisticaFcaUnl | Longitud de espigas de Ryegrass | data.frame | 75 | 2 |
| sal_cebada | estadisticaFcaUnl | Salinidad en cebada | data.frame | 18 | 6 |
| sal_suelo | estadisticaFcaUnl | Salinidad del suelo | data.frame | 54 | 7 |
| semilla | estadisticaFcaUnl | Variación en la forma de la semilla de plantas de Mendel | data.frame | 10 | 2 |
| semilla_trigo | estadisticaFcaUnl | Caracteres de semillas de trigo diploide | data.frame | 190 | 6 |
| semillas | estadisticaFcaUnl | Proporción de tamaños de semilla de maíz en tres partidas | data.frame | 9 | 3 |
| semillas_pd | estadisticaFcaUnl | Tratamiento de semillas de avena | data.frame | 64 | 4 |
| semillas_trigo | estadisticaFcaUnl | Rendimiento de semillas de trigo | data.frame | 16 | 4 |
| soja_pd | estadisticaFcaUnl | Espaciamiento en soja | data.frame | 60 | 5 |
| sorgo | estadisticaFcaUnl | Altura de plantas de sorgo forrajero | data.frame | 16 | 1 |
| sorgo_urea | estadisticaFcaUnl | Dosis de urea en sorgo granífero | data.frame | 16 | 4 |
| superficies_EAP | estadisticaFcaUnl | Cantidad de empresas agropecuarias y la superficie promedio de la provincia de Santa Fe registradas en el Censo Nacional Agropecuario 2008. | data.frame | 19 | 3 |
| susceptibilidad | estadisticaFcaUnl | Relación entre largo de semilla y susceptibilidad a roya en trigo | data.frame | 6 | 3 |
| tambos_2008_2015 | estadisticaFcaUnl | Datos sobre numero de establecimientos lecheros y composición de rodeo en la provincia de Santa Fe durante el período 2008-2015. | data.frame | 152 | 10 |
| tiempo_reaccion | estadisticaFcaUnl | Tiempo de reacción en proceso químico | data.frame | 25 | 4 |
| tomate | estadisticaFcaUnl | Crecimiento de tejido de tomate | data.frame | 20 | 3 |
| TRC | estadisticaFcaUnl | Tasa de recuperación cardíaca (TRC) | data.frame | 22 | 3 |
| trebol_rizhobium | estadisticaFcaUnl | Incoculación en trébol rojo | data.frame | 30 | 2 |
| tribolium | estadisticaFcaUnl | Pérdida de agua en Tribolium confusum | data.frame | 9 | 2 |
| trigo_fungicidas_momentos | estadisticaFcaUnl | Fungicidas y momentos en trigo | data.frame | 28 | 4 |
| trigo_zona_ciclo | estadisticaFcaUnl | Trigo en zonas y ciclos | data.frame | 24 | 4 |
| variedad_trigo | estadisticaFcaUnl | Rendimiento de variedades de trigo | data.frame | 16 | 4 |
| vitA_vaquillas | estadisticaFcaUnl | Vitamina A en vaquillas | data.frame | 28 | 2 |
| NIRcannabis | proximetricsR | NIRcannabis | proximate_data | 80 | 19 |
| observations_points | rangr | Example Of Observation Points List | data.frame | 1500 | 3 |
| boot.wmt | EGAnet | 'bootEGA' Results of 'wmt2'Data | bootEGA | | |
| depression | EGAnet | Depression Data | data.frame | 574 | 78 |
| dnn.weights | EGAnet | Loadings Comparison Test Deep Learning Neural Network Weights | list | | |
| ega.wmt | EGAnet | 'EGA' Network of 'wmt2'Data | EGA | | |
| intelligenceBattery | EGAnet | Intelligence Data | data.frame | 1152 | 125 |
| optimism | EGAnet | Optimism Data | data.frame | 282 | 10 |
| prime.num | EGAnet | Prime Numbers through 100,000 | integer | | |
| sim.dynEGA | EGAnet | sim.dynEGA Data | data.frame | 5000 | 26 |
| wmt2 | EGAnet | WMT-2 Data | data.frame | 1185 | 24 |
| example_confirmed | EpiNow2 | Example Confirmed Case Data Set | data.table | 130 | 2 |
| example_generation_time | EpiNow2 | Example generation time | dist_spec | | |
| example_incubation_period | EpiNow2 | Example incubation period | dist_spec | | |
| example_reporting_delay | EpiNow2 | Example reporting delay | dist_spec | | |
| example_truncated | EpiNow2 | Example Case Data Set with Truncation | list | | |
| ReineckeWell266 | copBasic | Porosity and Permeability Data for Well-266 of the Reinecke Oil Field, Horseshoe Atoll, Texas | data.frame | 235 | 6 |
| ReineckeWells | copBasic | Porosity and Permeability Data for the Reinecke Oil Field, Horseshoe Atoll, Texas | data.frame | 1271 | 5 |
| wolfCOPtest_data_smlsam | copBasic | Small Sample Components for Schweizer-Wolff Sigma Test | data.frame | 14268 | 3 |
| CPFs | moocore | Conditional Pareto fronts obtained from Gaussian processes simulations. | data.frame | 2967 | 3 |
| HybridGA | moocore | Results of Hybrid GA on Vanzyl and Richmond water networks | list | | |
| SPEA2minstoptimeRichmond | moocore | Results of SPEA2 when minimising electrical cost and maximising the minimum idle time of pumps on Richmond water network. | data.frame | 166 | 3 |
| SPEA2relativeRichmond | moocore | Results of SPEA2 with relative time-controlled triggers on Richmond water network. | data.frame | 91 | 3 |
| SPEA2relativeVanzyl | moocore | Results of SPEA2 with relative time-controlled triggers on Vanzyl's water network. | data.frame | 107 | 3 |
| tpls50x20_1_MWT | moocore | Various strategies of Two-Phase Local Search applied to the Permutation Flowshop Problem with Makespan and Weighted Tardiness objectives. | data.frame | 1511 | 4 |
| aids | gss | AIDS Incubation | data.frame | 295 | 3 |
| bacteriuria | gss | Treatment of Bacteriuria | data.frame | 820 | 4 |
| buffalo | gss | Buffalo Annual Snowfall | numeric | | |
| clim | gss | Average Temperatures During December 1980 Through February 1981 | data.frame | 690 | 2 |
| ColoCan | gss | Colorectal Cancer Mortality Rate in Indiana Counties | data.frame | 184 | 11 |
| DiaRet | gss | Diabetic Retinopathy | data.frame | 197 | 13 |
| esc | gss | Embryonic Stem Cell from Mouse | data.frame | 1027 | 8 |
| eyetrack | gss | Eyesight Fixation in Eyetracking Experiments | data.frame | 13891 | 5 |
| gastric | gss | Gastric Cancer Data | data.frame | 90 | 3 |
| LakeAcidity | gss | Water Acidity in Lakes | data.frame | 112 | 5 |
| NO2 | gss | Air Pollution and Road Traffic | data.frame | 500 | 6 |
| nox | gss | NOx in Engine Exhaust | data.frame | 88 | 3 |
| ozone | gss | Ozone Concentration in Los Angeles Basin | data.frame | 330 | 10 |
| penny | gss | Thickness of US Lincoln Pennies | data.frame | 90 | 2 |
| Sachs | gss | Protein Expression in Human Immune System Cells | data.frame | 7466 | 12 |
| stan | gss | Stanford Heart Transplant Data | data.frame | 184 | 4 |
| wesdr | gss | Progression of Diabetic Retinopathy | data.frame | 669 | 4 |
| wesdr1 | gss | Stages of Diabetic Retinopathy | data.frame | 2049 | 7 |
| example.1D | palm | 1-dimensional example data | matrix | 99 | |
| example.2D | palm | 2-dimensional example data | matrix | 30 | |
| example.twocamera | palm | Two-camera example data. | list | | |
| porpoise.data | palm | Two-camera porpoise data. | list | | |
| adg | abn | Dataset related to average daily growth performance and abattoir findings in pigs commercial production. | data.frame | 341 | 9 |
| ex0.dag.data | abn | Synthetic validation data set for use with abn library examples | data.frame | 300 | 30 |
| ex1.dag.data | abn | Synthetic validation data set for use with abn library examples | data.frame | 10000 | 10 |
| ex2.dag.data | abn | Synthetic validation data set for use with abn library examples | data.frame | 10000 | 18 |
| ex3.dag.data | abn | Validation data set for use with abn library examples | data.frame | 1000 | 14 |
| ex4.dag.data | abn | Valdiation data set for use with abn library examples | data.frame | 2000 | 11 |
| ex5.dag.data | abn | Valdiation data set for use with abn library examples | data.frame | 434 | 19 |
| ex6.dag.data | abn | Valdiation data set for use with abn library examples | data.frame | 800 | 8 |
| ex7.dag.data | abn | Valdiation data set for use with abn library examples | data.frame | 10648 | 3 |
| FCV | abn | Dataset related to Feline calicivirus infection among cats in Switzerland. | data.frame | 300 | 15 |
| g2b2c_data | abn | Toy Data Set for Examples in README | data.frame | 1000 | 5 |
| g2pbcgrp | abn | Toy Data Set for Examples in README | data.frame | 10000 | 6 |
| pigs.vienna | abn | Dataset related to diseases present in 'finishing pigs', animals about to enter the human food chain at an abattoir. | data.frame | 25000 | 11 |
| var33 | abn | simulated dataset from a DAG comprising of 33 variables | data.frame | 250 | 33 |
| arrests | equatiomatic | Arrest data from Gelman & Hill | grouped_df | 225 | 4 |
| hsb | equatiomatic | A subset of the full 1982 High School and Beyond Survey | data.frame | 7185 | 8 |
| penguins | equatiomatic | Size measurements for adult foraging penguins near Palmer Station, Antarctica | tbl_df | 344 | 8 |
| polls | equatiomatic | The polls data from Gelman and Hill () | tbl_df | 13544 | 7 |
| sim_longitudinal | equatiomatic | Simulated longitudinal data | grouped_df | 1000 | 8 |
| simple_ts | equatiomatic | Simple simulated time series data | ts | | |
| ts_reg_list | equatiomatic | Simulated data for time-series regression | list | | |
| data_ae | tfrmt | Adverse Events Analysis Results Data | grouped_df | 2794 | 8 |
| data_demog | tfrmt | Demography Analysis Results Data | grouped_df | 346 | 8 |
| data_efficacy | tfrmt | Efficacy Analysis Results Data | tbl_df | 70 | 7 |
| data_labs | tfrmt | Labs Analysis Results Data | grouped_df | 4770 | 10 |
| debt_distress | globalvuln | Individual humanitarian index datasets | data.frame | 195 | 19 |
| disaster_displacement | globalvuln | Individual humanitarian index datasets | data.frame | 195 | 19 |
| ghi | globalvuln | Individual humanitarian index datasets | data.frame | 195 | 19 |
| ghs | globalvuln | Individual humanitarian index datasets | data.frame | 195 | 19 |
| hdi | globalvuln | Individual humanitarian index datasets | data.frame | 195 | 19 |
| humanitarian_index_sources | globalvuln | Sources and provenance for the humanitarian indices | data.frame | 17 | 17 |
| inform_risk | globalvuln | Individual humanitarian index datasets | data.frame | 195 | 19 |
| inform_severity | globalvuln | Individual humanitarian index datasets | data.frame | 195 | 19 |
| internal_displacement | globalvuln | Individual humanitarian index datasets | data.frame | 195 | 19 |
| mpi | globalvuln | Individual humanitarian index datasets | data.frame | 195 | 19 |
| nd_gain | globalvuln | Individual humanitarian index datasets | data.frame | 195 | 19 |
| oecd_fragility | globalvuln | Individual humanitarian index datasets | data.frame | 195 | 19 |
| searo | globalvuln | Individual humanitarian index datasets | data.frame | 195 | 19 |
| un_mvi | globalvuln | Individual humanitarian index datasets | data.frame | 195 | 19 |
| underfunded_crisis | globalvuln | Individual humanitarian index datasets | data.frame | 195 | 19 |
| worldrisk | globalvuln | Individual humanitarian index datasets | data.frame | 195 | 19 |
| wps | globalvuln | Individual humanitarian index datasets | data.frame | 195 | 19 |
| ACE.13.TCE.df | EnvStats | Trichloroethylene Concentrations Before and After Remedation | data.frame | 20 | 3 |
| Air.df | EnvStats | Atmospheric Environmental Conditions in New York City | data.frame | 153 | 4 |
| Beal.2010.Pb.df | EnvStats | Lead concentration in soil samples. | data.frame | 29 | 3 |
| Benthic.df | EnvStats | Benthic Data from Monitoring Program in Chesapeake Bay | data.frame | 585 | 7 |
| BJC.2000.df | EnvStats | Randomly sampled measurements of an analyte in soil samples. | data.frame | 11 | 4 |
| Distribution.df | EnvStats | Data Frame Summarizing Available Probability Distributions and Estimation Methods | data.frame | 35 | 25 |
| Environmental.df | EnvStats | Atmospheric Environmental Conditions in New York City | data.frame | 153 | 4 |
| EPA.02d.Ex.2.ug.per.L.vec | EnvStats | Concentrations in Exhibit 2 of 2002d USEPA Guidance Document | numeric | | |
| EPA.02d.Ex.4.mg.per.kg.vec | EnvStats | Concentrations in Exhibit 4 of 2002d USEPA Guidance Document | numeric | | |
| EPA.02d.Ex.6.mg.per.kg.vec | EnvStats | Concentrations in Exhibit 6 of 2002d USEPA Guidance Document | numeric | | |
| EPA.02d.Ex.9.mg.per.L.vec | EnvStats | Concentrations in Exhibit 9 of 2002d USEPA Guidance Document | numeric | | |
| EPA.09.Ex.10.1.nickel.df | EnvStats | Nickel Concentrations from Example 10-1 of 2009 USEPA Guidance Document | data.frame | 20 | 3 |
| EPA.09.Ex.11.1.arsenic.df | EnvStats | Arsenic Concentrations from Example 11-1 of 2009 USEPA Guidance Document | data.frame | 24 | 3 |
| EPA.09.Ex.12.1.ccl4.df | EnvStats | Carbon Tetrachloride Concentrations from Example 12-1 of 2009 USEPA Guidance Document | data.frame | 20 | 2 |
| EPA.09.Ex.12.4.naphthalene.df | EnvStats | Naphthalene Concentrations from Example 12-4 of 2009 USEPA Guidance Document | data.frame | 25 | 3 |
| EPA.09.Ex.13.1.iron.df | EnvStats | Iron Concentrations from Example 13-1 of 2009 USEPA Guidance Document | data.frame | 24 | 4 |
| EPA.09.Ex.14.1.manganese.df | EnvStats | Manganese Concentrations from Example 14-1 of 2009 USEPA Guidance Document | data.frame | 32 | 3 |
| EPA.09.Ex.14.3.alkalinity.df | EnvStats | Alkalinity Measures from Example 14-3 of 2009 USEPA Guidance Document | data.frame | 54 | 2 |
| EPA.09.Ex.14.4.arsenic.df | EnvStats | Arsenic Concentrations from Example 14-4 of 2009 USEPA Guidance Document | data.frame | 16 | 4 |
| EPA.09.Ex.14.8.df | EnvStats | Analyte Concentrations from Example 14-8 of 2009 USEPA Guidance Document | data.frame | 36 | 4 |
| EPA.09.Ex.15.1.manganese.df | EnvStats | Manganese Concentrations from Example 15-1 of 2009 USEPA Guidance Document | data.frame | 25 | 5 |
| EPA.09.Ex.16.1.sulfate.df | EnvStats | Sulfate Concentrations from Example 16-1 of 2009 USEPA Guidance Document | data.frame | 16 | 4 |
| EPA.09.Ex.16.2.benzene.df | EnvStats | Benzene Concentrations from Example 16-2 of 2009 USEPA Guidance Document | data.frame | 16 | 3 |
| EPA.09.Ex.16.4.copper.df | EnvStats | Copper Concentrations from Example 16-4 of 2009 USEPA Guidance Document | data.frame | 18 | 4 |
| EPA.09.Ex.16.5.PCE.df | EnvStats | Tetrachloroethylene Concentrations from Example 16-5 of 2009 USEPA Guidance Document | data.frame | 14 | 4 |
| EPA.09.Ex.17.1.loglead.df | EnvStats | Log-transformed Lead Concentrations from Example 17-1 of 2009 USEPA Guidance Document | data.frame | 24 | 4 |
| EPA.09.Ex.17.2.toluene.df | EnvStats | Toluene Concentrations from Example 17-2 of 2009 USEPA Guidance Document | data.frame | 25 | 6 |
| EPA.09.Ex.17.3.chrysene.df | EnvStats | Chrysene Concentrations from Example 17-3 of 2009 USEPA Guidance Document | data.frame | 20 | 4 |
| EPA.09.Ex.17.3.log.chrysene.df | EnvStats | Log-transformed Chrysene Concentrations from Example 17-3 of 2009 USEPA Guidance Document | data.frame | 20 | 4 |
| EPA.09.Ex.17.4.copper.df | EnvStats | Copper Concentrations from Example 17-4 of 2009 USEPA Guidance Document | data.frame | 40 | 6 |
| EPA.09.Ex.17.5.chloride.df | EnvStats | Chloride Concentrations from Example 17-5 of 2009 USEPA Guidance Document | data.frame | 19 | 4 |
| EPA.09.Ex.17.6.sulfate.df | EnvStats | Sulfate Concentrations from Example 17-6 of 2009 USEPA Guidance Document | data.frame | 23 | 6 |
| EPA.09.Ex.17.7.sodium.df | EnvStats | Sodium Concentrations from Example 17-7 of 2009 USEPA Guidance Document | data.frame | 10 | 2 |
| EPA.09.Ex.18.1.arsenic.df | EnvStats | Arsenic Concentrations from Example 18-1 of 2009 USEPA Guidance Document | data.frame | 16 | 3 |
| EPA.09.Ex.18.2.chrysene.df | EnvStats | Chrysene Concentrations from Example 18-2 of 2009 USEPA Guidance Document | data.frame | 12 | 4 |
| EPA.09.Ex.18.3.TCE.df | EnvStats | Trichloroethylene Concentrations from Example 18-3 of 2009 USEPA Guidance Document | data.frame | 24 | 6 |
| EPA.09.Ex.18.4.xylene.df | EnvStats | Xylene Concentrations from Example 18-4 of 2009 USEPA Guidance Document | data.frame | 32 | 6 |
| EPA.09.Ex.19.1.sulfate.df | EnvStats | Sulfate Concentrations from Example 19-1 of 2009 USEPA Guidance Document | data.frame | 25 | 7 |
| EPA.09.Ex.19.2.chloride.df | EnvStats | Chloride Concentrations from Example 19-2 of 2009 USEPA Guidance Document | data.frame | 40 | 2 |
| EPA.09.Ex.19.5.mercury.df | EnvStats | Mercury Concentrations from Example 19-5 of 2009 USEPA Guidance Document | data.frame | 36 | 6 |
| EPA.09.Ex.20.1.nickel.df | EnvStats | Nickel Concentrations from Example 20-1 of 2009 USEPA Guidance Document | data.frame | 16 | 4 |
| EPA.09.Ex.21.1.aldicarb.df | EnvStats | Aldicarb Concentrations from Example 21-1 of 2009 USEPA Guidance Document | data.frame | 12 | 3 |
| EPA.09.Ex.21.2.benzene.df | EnvStats | Benzene Concentrations from Example 21-2 of 2009 USEPA Guidance Document | data.frame | 8 | 4 |
| EPA.09.Ex.21.5.beryllium.df | EnvStats | Beryllium Concentrations from Example 21-5 of 2009 USEPA Guidance Document | data.frame | 16 | 3 |
| EPA.09.Ex.21.6.nitrate.df | EnvStats | Nitrate Concentrations from Example 21-6 of 2009 USEPA Guidance Document | data.frame | 12 | 5 |
| EPA.09.Ex.21.7.TCE.df | EnvStats | Trichloroethylene Concentrations from Example 21-7 of 2009 USEPA Guidance Document | data.frame | 10 | 2 |
| EPA.09.Ex.22.1.VC.df | EnvStats | Vinyl Chloride Concentrations from Example 22-1 of 2009 USEPA Guidance Document | data.frame | 32 | 5 |
| EPA.09.Ex.22.2.Specific.Conductance.df | EnvStats | Specific Conductance from Example 22-2 of 2009 USEPA Guidance Document | data.frame | 43 | 3 |
| EPA.09.Ex.6.3.sulfate.df | EnvStats | Sulfate Concentrations from Example 6-3 of 2009 USEPA Guidance Document | data.frame | 10 | 4 |
| EPA.09.Ex.7.1.arsenic.df | EnvStats | Arsenic concentrations from Example 7.1 of 2009 USEPA Guidance Document | data.frame | 16 | 2 |
| EPA.09.Table.9.1.TCE.df | EnvStats | Trichloroethene concentrations in Table 9.1 of 2009 USEPA Guidance Document | data.frame | 30 | 5 |
| EPA.09.Table.9.3.df | EnvStats | Arsenic, Mercury and Strontium Concentrations in Table 9-3 of 2009 USEPA Guidance Document | data.frame | 15 | 8 |
| EPA.09.Table.9.4.nickel.vec | EnvStats | Nickel Concentrations in Table 9-4 of 2009 USEPA Guidance Document | numeric | | |
| EPA.89b.aldicarb1.df | EnvStats | Aldicarb Concentrations from 1989 USEPA Guidance Document | data.frame | 12 | 3 |
| EPA.89b.aldicarb2.df | EnvStats | Aldicarb Concentrations from 1989 USEPA Guidance Document | data.frame | 12 | 3 |
| EPA.89b.benzene.df | EnvStats | Benzene Concentrations from 1989 USEPA Guidance Document | data.frame | 24 | 6 |
| EPA.89b.cadmium.df | EnvStats | Cadmium Concentrations from 1989 USEPA Guidance Document | data.frame | 88 | 4 |
| EPA.89b.chlordane1.df | EnvStats | Chlordane Concentrations from 1989 USEPA Guidance Document | data.frame | 24 | 2 |
| EPA.89b.chlordane2.df | EnvStats | Chlordane Concentrations from 1989 USEPA Guidance Document | data.frame | 32 | 5 |
| EPA.89b.edb.df | EnvStats | EDB Concentrations from 1989 USEPA Guidance Document | data.frame | 12 | 3 |
| EPA.89b.lead.df | EnvStats | Lead Concentrations from 1989 USEPA Guidance Document | data.frame | 24 | 4 |
| EPA.89b.loglead.df | EnvStats | Log-transformed Lead Concentrations from 1989 USEPA Guidance Document | data.frame | 24 | 4 |
| EPA.89b.manganese.df | EnvStats | Manganese Concentrations from 1989 USEPA Guidance Document | data.frame | 24 | 3 |
| EPA.89b.sulfate.df | EnvStats | Sulfate Concentrations from 1989 USEPA Guidance Document | data.frame | 24 | 3 |
| EPA.89b.t29.df | EnvStats | T-29 Concentrations from 1989 USEPA Guidance Document | data.frame | 32 | 6 |
| EPA.89b.toc.vec | EnvStats | Total Organic Carbon Concentrations from 1989 USEPA Guidance Document | numeric | | |
| EPA.92c.arsenic1.df | EnvStats | Arsenic Concentrations from 1992 USEPA Guidance Document | data.frame | 24 | 3 |
| EPA.92c.arsenic2.df | EnvStats | Arsenic Concentrations from 1992 USEPA Guidance Document | data.frame | 24 | 6 |
| EPA.92c.arsenic3.df | EnvStats | Arsenic Concentrations from 1992 USEPA Guidance Document | data.frame | 20 | 3 |
| EPA.92c.benzene1.df | EnvStats | Benzene Concentrations from 1992 USEPA Guidance Document | data.frame | 36 | 5 |
| EPA.92c.benzene2.df | EnvStats | Benzene Concentrations from 1992 USEPA Guidance Document | data.frame | 20 | 3 |
| EPA.92c.ccl4.df | EnvStats | Carbon Tetrachloride Concentrations from 1992 USEPA Guidance Document | data.frame | 20 | 3 |
| EPA.92c.chrysene.df | EnvStats | Chrysene Concentrations from 1992 USEPA Guidance Document | data.frame | 20 | 3 |
| EPA.92c.copper1.df | EnvStats | Copper Concentrations from 1992 USEPA Guidance Document | data.frame | 18 | 4 |
| EPA.92c.copper2.df | EnvStats | Copper Concentrations from 1992 USEPA Guidance Document | data.frame | 40 | 6 |
| EPA.92c.lognickel1.df | EnvStats | Log-transformed Nickel Concentrations from 1992 USEPA Guidance Document | data.frame | 20 | 3 |
| EPA.92c.nickel1.df | EnvStats | Nickel Concentrations from 1992 USEPA Guidance Document | data.frame | 20 | 3 |
| EPA.92c.nickel2.df | EnvStats | Nickel Concentrations from 1992 USEPA Guidance Document | data.frame | 16 | 3 |
| EPA.92c.toluene.df | EnvStats | Toluene Concentrations from 1992 USEPA Guidance Document | data.frame | 25 | 6 |
| EPA.92c.zinc.df | EnvStats | Zinc Concentrations from 1992 USEPA Guidance Document | data.frame | 40 | 5 |
| EPA.92d.chromium.df | EnvStats | Chromium Concentrations from 1992 USEPA Guidance Document | data.frame | 15 | 1 |
| EPA.92d.chromium.vec | EnvStats | Chromium Concentrations from 1992 USEPA Guidance Document | numeric | | |
| EPA.94b.lead.df | EnvStats | Lead Concentrations from 1994 USEPA Guidance Document | data.frame | 28 | 4 |
| EPA.94b.tccb.df | EnvStats | 1,2,3,4-Tetrachlorobenzene Concentrations from 1994 USEPA Guidance Document | data.frame | 124 | 4 |
| EPA.97.cadmium.111.df | EnvStats | Calibration Data for Cadmium at Mass 111 | data.frame | 35 | 2 |
| Gibbons.et.al.09.Alkilinity.vec | EnvStats | Alkilinity Data from Gibbons et al. (2009) | numeric | | |
| Gibbons.et.al.09.Vinyl.Chloride.vec | EnvStats | Vinyl Chloride Data from Gibbons et al. (2009) | numeric | | |
| Graham.et.al.75.etu.df | EnvStats | Ethylene Thiourea Dose-Response Data | data.frame | 6 | 4 |
| Grice.Bain.80.mat | EnvStats | Adjusted Alpha Levels to Compute Confidence Intervals for the Mean of a Gamma Distribution | matrix | 5 | 7 |
| Helsel.Cohn.88.app.b.df | EnvStats | Example of Multiply Left-censored Data from Literature | data.frame | 18 | 3 |
| Helsel.Cohn.88.silver.df | EnvStats | Silver Concentrations From An Interlab Comparison | data.frame | 56 | 4 |
| Helsel.Hirsch.02.Mayfly.df | EnvStats | Paired Counts of Mayfly Nymphs Above and Below Industrial Outfalls | data.frame | 24 | 3 |
| Lin.Evans.80.df | EnvStats | Fecal Coliform Data from the Illinois River | data.frame | 24 | 2 |
| Millard.Deverel.88.df | EnvStats | Copper and Zinc Concentrations in Shallow Ground Water | data.frame | 118 | 8 |
| Modified.TcCB.df | EnvStats | Modified 1,2,3,4-Tetrachlorobenzene Data with Censored Values | data.frame | 47 | 3 |
| NIOSH.89.air.lead.vec | EnvStats | NIOSH Air Lead Levels Data | numeric | | |
| Olympic.NH4.df | EnvStats | Ammonium Concentration in Precipitation Measured at Olympic National Park Hoh Ranger Station | data.frame | 102 | 6 |
| Ozone.NE.df | EnvStats | Ozone Concentrations in the Northeast U.S. | data.frame | 41 | 5 |
| ProUCL.5.2.TRS.df | EnvStats | Real dataset from ProUCL 5.2.0. | data.frame | 55 | 3 |
| ProUCL.Crit.Vals.for.AD.Test.for.Gamma.array | EnvStats | ProUCL Critical Values for Anderson-Darling Goodness-of-Fit Test for Gamma Distribution | array | | |
| ProUCL.Crit.Vals.for.KS.Test.for.Gamma.array | EnvStats | ProUCL Critical Values for Kolmogorov-Smirnov Goodness-of-Fit Test for Gamma Distribution | array | | |
| Refinery.CO.df | EnvStats | Carbon Monoxide Emissions from Oil Refinery. | data.frame | 40 | 3 |
| Skagit.NH3_N.df | EnvStats | Ammonia Nitrogen Concentrations in the Skagit River, Marblemount, Washington | data.frame | 387 | 6 |
| Total.P.df | EnvStats | Total Phosphorus Data from Chesapeake Bay | data.frame | 60 | 4 |
| gsea_pathwaysets_toy | GSEAlens | Toy MSigDB pathway collection (Hallmark + KEGG_LEGACY) | list | | |
| gsea_pathwaysets_toy_hallmark | GSEAlens | Toy MSigDB pathway collection (Hallmark only) | list | | |
| precomputed_gseares | GSEAlens | Pre-computed GSEA results from the airway dataset | GseaRes | | |
| preprocessed_dds | GSEAlens | Pre-computed DESeq2 DESeqDataSet from the airway dataset | DESeqDataSet | | |
| preprocessed_dds_se | GSEAlens | Pre-computed DESeqDataSet (SummarizedExperiment form) | DESeqDataSet | | |
| preprocessed_limma | GSEAlens | Pre-computed limma-voom fit and DGEList from the airway dataset | list | | |
| ICSS | popEpi | Age standardisation weights from the ICSS scheme. | data.table | 16 | 4 |
| meanpop_fi | popEpi | Mean population counts in Finland year, sex, and age group. | data.table | 7272 | 4 |
| popmort | popEpi | Population mortality rates in Finland 1951 - 2013 in 101 age groups and by gender. This is an example of a population hazard table as used in 'popEpi'; for the general help page, see [pophaz]. | data.table | 12726 | 4 |
| sibr | popEpi | sibr - a simulated cohort of Finnish female breast cancer patients | data.table | 10000 | 6 |
| sire | popEpi | sire - a simulated cohort of Finnish female rectal cancer patients | data.table | 8243 | 6 |
| stdpop101 | popEpi | World standard population by 1 year age groups from 1 to 101. Sums to 100 000. | data.table | 101 | 2 |
| stdpop18 | popEpi | Standard populations from 2000: world, Europe and Nordic. | data.table | 18 | 4 |
| SRL_Claude | psychnets | Self-regulated-learning (MSLQ) construct scores simulated by large language models | data.frame | 300 | 5 |
| SRL_Gemini | psychnets | Self-regulated-learning (MSLQ) construct scores simulated by large language models | data.frame | 300 | 5 |
| SRL_GPT | psychnets | Self-regulated-learning (MSLQ) construct scores simulated by large language models | data.frame | 300 | 5 |
| SRL_LLaMa | psychnets | Self-regulated-learning (MSLQ) construct scores simulated by large language models | data.frame | 300 | 5 |
| SRL_Mistral | psychnets | Self-regulated-learning (MSLQ) construct scores simulated by large language models | data.frame | 300 | 5 |
| esbl_tests | certestats | Example Data Set with ESBL Test Outcomes | tbl_df | 500 | 19 |
| snp500 | msPCA | Market-deflated correlation matrix of S&P 500 daily returns | matrix | 423 | 423 |
| coffee_data | ggeffects | Sample dataset from a course about analysis of factorial designs | data.frame | 120 | 5 |
| efc | ggeffects | Sample dataset from the EUROFAMCARE project | data.frame | 908 | 28 |
| efc_test | ggeffects | Sample dataset from the EUROFAMCARE project | data.frame | 908 | 28 |
| fish | ggeffects | Sample data set | data.frame | 250 | 9 |
| lung2 | ggeffects | Sample data set | data.frame | 226 | 5 |
| lipidomics_dataset | mrmhub | Plasma lipidomics dataset with metadata | MRMhubExperiment | | |
| quant_lcms_dataset | mrmhub | LC-MS dataset with external calibration curve and metadata | MRMhubExperiment | | |
| adults | Sibyl | adults | phyloseq | | |
| larvae | Sibyl | larvae | phyloseq | | |
| estimates_bighump | plausibounds | Sinusoidal Estimates with Moderate Correlation | numeric | | |
| estimates_constant | plausibounds | Constant Estimates | numeric | | |
| estimates_smooth | plausibounds | Smooth Estimates from Freyaldenhoven and Hansen (2026) | numeric | | |
| var_bighump | plausibounds | Variance Matrix for Sinusoidal Estimates | matrix | 36 | |
| var_constant | plausibounds | Variance Matrix for Constant Estimates | matrix | 12 | |
| var_smooth | plausibounds | Variance Matrix for Smooth Estimates | matrix | 44 | |
| Advertisement | TangledFeatures | Advertisement dataset | data.frame | 200 | 4 |
| Housing_Prices_dataset | TangledFeatures | Housing prices dataset | data.frame | 1460 | 81 |
| pbmc3ksub | dreval | Expression profiles for 2,700 PBMCs | SingleCellExperiment | | |
| nhanes | EPLSIM | This is data from NHANES 2003–2004 | data.frame | 800 | 14 |
| nhanes.new | EPLSIM | This is an updated data from original data based on NHANES 2003–2004 survey | data.frame | 789 | 17 |
| codes | weathercan | Meaning of climate normal 'codes' | tbl_df | 4 | 2 |
| finches | weathercan | RFID Data on finch visits to feeders | tbl_df | 16886 | 10 |
| flags | weathercan | Meaning of coded 'flags' | tbl_df | 16 | 2 |
| glossary | weathercan | Glossary of units and terms | tbl_df | 79 | 5 |
| glossary_normals | weathercan | Glossary of terms for Climate Normals | tbl_df | 18 | 3 |
| kamloops | weathercan | Hourly weather data for Kamloops | tbl_df | 4368 | 38 |
| kamloops_day | weathercan | Daily weather data for Kamloops | tbl_df | 182 | 37 |
| normals_measurement_types | weathercan | List of climate normals measurements and types for each set of normals | tbl_df | 158 | 3 |
| normals_measurements | weathercan | List of climate normals measurements for each station | tbl_df | 376959 | 6 |
| pg | weathercan | Hourly weather data for Prince George | tbl_df | 4368 | 38 |
| variables_normals_new | weathercan | Index of variables for new Climate Normals | tbl_df | 158 | 3 |
| variables_normals_old | weathercan | Index of variables for new Climate Normals | tbl_df | 151 | 2 |
| AbortionAmbivalence | glmx | American Ambivalence towards Abortion Policy | data.frame | 1860 | 20 |
| BeetleMortality | glmx | Bliss (1935) Beetle Mortality Data | data.frame | 8 | 3 |
| MexicanLabor | glmx | Mexican Women's Labor-Force Participation | data.frame | 16 | 6 |
| WECO | glmx | Productivity and Quit Behavior of Western Electric Workers | data.frame | 683 | 7 |
| accounts | jfa | Accounts Receivable | data.frame | 20 | 3 |
| allowances | jfa | Legitimacy Audit | data.frame | 4076 | 5 |
| benchmark | jfa | Benchmark Analysis of Sales Versus Cost of Sales | data.frame | 100 | 2 |
| BuildIt | jfa | BuildIt Construction Financial Statements | data.frame | 3500 | 3 |
| carrier | jfa | Carrier Company Financial Statements | data.frame | 202 | 12 |
| compas | jfa | COMPAS Recidivism Prediction | data.frame | 6172 | 7 |
| retailer | jfa | Retailer Group Audit | data.frame | 20 | 4 |
| sanitizer | jfa | Factory Workers' use of Hand Sanitizer | data.frame | 1600 | 1 |
| sinoForest | jfa | Sino Forest Corporation's Financial Statements. | data.frame | 772 | 1 |
| covid_deaths_2020_uk | EpiEstim | Data on the 2020-2022 SARS-CoV-2 epidemic in the UK. | list | | |
| flu_2009_NYC_school | EpiEstim | Data on the 2009 H1N1 influenza pandemic in a school in New York city | list | | |
| Flu1918 | EpiEstim | Data on the 1918 H1N1 influenza pandemic in Baltimore. | list | | |
| Flu2009 | EpiEstim | Data on the 2009 H1N1 influenza pandemic in a school in Pennsylvania. | list | | |
| Measles1861 | EpiEstim | Data on the 1861 measles epidemic in Hagelloch, Germany. | list | | |
| mers_2014_15 | EpiEstim | Data on Middle East Respiratory Syndrome (MERS) in Saudi Arabia. | list | | |
| MockRotavirus | EpiEstim | Mock data on a rotavirus epidemic. | list | | |
| SARS2003 | EpiEstim | Data on the 2003 SARS epidemic in Hong Kong. | list | | |
| Smallpox1972 | EpiEstim | Data on the 1972 smallpox epidemic in Kosovo | list | | |
| rcheology | rcheology | Data on objects from current and previous versions of R | tbl_df | 527379 | 10 |
| Rversions | rcheology | Previous R versions with dates | data.frame | 157 | 2 |
| df.compare1 | metaConvert | Fictitious dataset 1 | data.frame | 5 | 7 |
| df.compare2 | metaConvert | Fictitious dataset 2 | data.frame | 6 | 7 |
| df.haza | metaConvert | Meta-analytic dataset inspired from Haza and colleagues (2024) | data.frame | 170 | 106 |
| df.psychom | metaConvert | Simulated dataset for a COSMIN-based systematic review of PROMs | data.frame | 30 | 25 |
| df.short | metaConvert | Short version of the df.haza dataset | grouped_df | 37 | 109 |
| Bakal2007 | PANR | Rich morphological phenotypes for gene overexpression and RNA interference screens | matrix | 273 | 7 |
| Bakal2007Cluster | PANR | Rich morphological phenotypes for gene overexpression and RNA interference screens | integer | | |
| bm1 | PANR | Rich morphological phenotypes for gene overexpression and RNA interference screens | BetaMixture | | |
| nodeColor | PANR | Rich morphological phenotypes for gene overexpression and RNA interference screens | character | | |
| cfa_example | semptools | Sample dataset pa_example | data.frame | 200 | 14 |
| pa_example | semptools | Sample dataset pa_example | data.frame | 100 | 4 |
| pa_example_3covs | semptools | Sample dataset pa_example_3covs | data.frame | 100 | 7 |
| sem_2nd_order_example | semptools | Sample dataset sem_2nd_order_example | data.frame | 500 | 21 |
| sem_example | semptools | Sample dataset sem_example | data.frame | 200 | 14 |
| cpt_names | cptcity | Names of all 7140 colour gradients | character | | |
| rockfall_eseis | eseis | Seismic trace of a rockfall event. | eseis | | |
| rockfall_t | eseis | Seismic trace of a rockfall event. | POSIXct | | |
| rockfall_z | eseis | Seismic trace of a rockfall event. | numeric | | |
| s | eseis | Seismic traces of a small earthquake | list | | |
| t | eseis | Seismic traces of a small earthquake | POSIXct | | |
| cmap | RGSEA | Data from Connectivity map build 01 | matrix | 22268 | 6 |
| e1 | RGSEA | Data from GDS4102 | matrix | 54675 | 2 |
| e2 | RGSEA | Data from GDS4100 | matrix | 54675 | 4 |
| Facet_group | LorMe | Tax summary object with Facet 2x2 Groups | LorMe | | |
| testotu | LorMe | test otudata | data.frame | 1000 | 22 |
| Three_group | LorMe | Tax summary object with three groups | LorMe | | |
| Two_group | LorMe | Tax summary object with two groups | LorMe | | |
| hiv_data | joinpointR | Simulated HIV rates | tbl_df | 160 | 4 |
| forecast_data_example | modelimportance | Example forecast outputs for modelimportance article vignette | model_out_tbl | 168 | 9 |
| forecast_data_ma_h1 | modelimportance | Forecast outputs for Massachusetts, horizon 1, used in vignette runtime data | model_out_tbl | 3 | 9 |
| forecast_data_raw | modelimportance | Raw forecast outputs for get-started vignette | model_out_tbl | 12 | 9 |
| target_data_example | modelimportance | Example target data for modelimportance article vignette | spec_tbl_df | 8 | 4 |
| target_data_ma | modelimportance | Target data for Massachusetts used in vignette runtime data | spec_tbl_df | 1 | 4 |
| target_data_raw | modelimportance | Raw target data for get-started vignette | spec_tbl_df | 4 | 4 |
| fgl | cirls | Measurement of forensic glass fragments | data.frame | 214 | 10 |
| london | cirls | Daily mortality, temperature and pollution data in London | data.frame | 2922 | 14 |
| warming | cirls | Global temperature anomaly | data.frame | 166 | 3 |
| EMpot | EMMAgeo | example data | EMMAgeo_empot | | |
| EMrob | EMMAgeo | example data | list | | |
| X | EMMAgeo | example data | matrix | 100 | 116 |
| BinomialExample | picreg | Small binary-classification dataset for the Binomial section of the vignette. | list | | |
| CoxExample | picreg | Small survival dataset for the Cox section of the vignette. | list | | |
| QuickStartExample | picreg | Small Gaussian dataset for the introductory vignette. | list | | |
| polar_h10 | detectPVC | Example Polar H10 data | data.frame | 78096 | 4 |
| browser_data | mschart | Dummy dataset for barchart | data.frame | 18 | 3 |
| browser_ts | mschart | Dummy dataset for time series charts | grouped_df | 36 | 3 |
| us_indus_prod | mschart | Index of US Industrial Production | data.frame | 256 | 3 |
| saeb | ivd | Basic Education Evaluation System (Saeb) -2021 | data.table | 11386 | 5 |
| leukaemia | XOMultinom | Data: Leukaemia cases | data.frame | 790 | 5 |
| mainsail | XOMultinom | MAINSAIL trial: comparator-arm data with Halabi 2014 risk scores | data.frame | 520 | 21 |
| pep_seed | pep725 | Minimal Seed Dataset for Synthetic Data Generation | data.table | 1319 | 10 |
| diabetes | hsstan | Diabetes data with interaction terms | data.frame | 442 | 65 |
| basic_smallworld | L0ggm | Toy Small-world Network Data Example | matrix | 500 | 20 |
| skew_tables | L0ggm | Skew Tables | list | | |
| weibull_weights | L0ggm | SUR Model Coefficients and Residuals for Weibull Parameter Prediction | list | | |
| SLE_gwas_sub | locuszoomr | SLE GWAS data subset | data.frame | 1990 | 12 |
| bile | diagL1 | Bile Data | data.frame | 29 | 2 |
| Fire | diagL1 | Fire Data | data.frame | 47 | 6 |
| keywords | rjtools | Keywords options for R Journal | character | | |
| cell_lines | SuperCell | Cancer cell lines dataset | list | | |
| pancreas | SuperCell | Pancreatic cell dataset | list | | |
| Anestheticdat | nmaINLA | Data for the Anesthetic example in Greco et al. (2013) | data.frame | 30 | 10 |
| Certolizumabdat | nmaINLA | Data for the Certolizumab NMA-network discussed in Dias et al. (2013) | data.frame | 12 | 9 |
| CooperStrokedat | nmaINLA | Data for the stroke prevention NMA-network discussed in Cooper et al. (2009) | data.frame | 26 | 18 |
| Diabetesdat | nmaINLA | Data for the Diabetes example in Senn et al. (2013) | data.frame | 26 | 11 |
| Dietaryfatdat | nmaINLA | Data for the Dietary fat example in Dias et al. (2011) | data.frame | 10 | 10 |
| Flourdat | nmaINLA | Data for the Flour NMA example in Dias et al. (2010) | data.frame | 130 | 20 |
| IncDiabetesdat | nmaINLA | Data for the Incident Diabetes example in Elliott et al. (2007) | data.frame | 22 | 12 |
| KussHeartdat | nmaINLA | Data for the ischemic heart disease sparse pairwise meta-analysis discussed in Kuss (2014) | tbl_df | 60 | 4 |
| Parkinsondat | nmaINLA | Data for the Parkinson NMA-network discussed in Dias et al. (2013) | data.frame | 7 | 11 |
| Smokdat | nmaINLA | Data for the smoking cessation NMA-network discussed in Dias et al. (2010) | data.frame | 24 | 11 |
| Strokedat | nmaINLA | Data for the Stroke NMA regression discussed in Batson et al. (2016) | data.frame | 19 | 18 |
| TBdat | nmaINLA | Trials investigating effectiveness of the BCG vaccine against TB | data.frame | 13 | 6 |
| Thrombdat | nmaINLA | Data for the thrombolytic NMA-network discussed in Dias et al. (2010) | data.frame | 50 | 10 |
| Woodsdat | nmaINLA | Data for the Woods example in Woods et al. (2010) | data.frame | 3 | 14 |
| ariari | geospt | Ariari Map. | SpatialPolygonsDataFrame | | |
| ariprec | geospt | Data from climatic stations of the Ariari River (Meta-Colombia Department) | data.frame | 18 | 6 |
| COSha10 | geospt | Soil organic carbon database at a sampling depth of 0-10 cm | data.frame | 122 | 10 |
| COSha10map | geospt | Map of total soil carbon stock (t/ha) at 0-10 cm depth | SpatialPixelsDataFrame | | |
| COSha30 | geospt | Soil organic carbon database at a sampling depth of 0-30 cm | data.frame | 118 | 10 |
| COSha30map | geospt | Map of total soil carbon stock (t/ha) at 0-30 cm depth | SpatialPixelsDataFrame | | |
| lalib | geospt | Map of boundary enclosing La Libertad Research Center | SpatialPolygonsDataFrame | | |
| preci | geospt | Empirical data related to rainfall | data.frame | 10 | 4 |
| PowderyMildew | epifitter | Powdery mildew disease progress curves in organic tomato | data.frame | 240 | 5 |
| Crohn | codacore | Microbiome composition related to Crohn's disease study | data.frame | 975 | 49 |
| HIV | codacore | Microbiome, HIV infection and MSM factor | data.frame | 155 | 62 |
| sCD14 | codacore | Microbiome and sCD14 inflammation parameter | matrix | 151 | 61 |
| cvi_example | contentValidity | Example expert ratings for content validity analysis | matrix | 6 | 10 |
| predictions | modelimpact | Predictions from a customer churn model. | spec_tbl_df | 2145 | 4 |
| rset | MultiDataSet | Example 'ResultSet' | ResultSet | | |
| ExampleData | PoolTestR | A synthetic dataset for pooled testing | data.frame | 7604 | 6 |
| SimpleExampleData | PoolTestR | A synthetic dataset for pooled testing | data.frame | 1152 | 6 |
| TruePrev | PoolTestR | A synthetic dataset for pooled testing | data.frame | 900 | 7 |
| data_to_check | SAvalidation | Example data for validation | list | | |
| vintages | SAvalidation | | list | | |
| betaNested | ecocbo | Data set containing the results of applying ecocbo::sim_beta() to a nested factors experiment. | ecocbo_beta | | |
| dataFish | ecocbo | Data set containing results from the PRCRMP. | data.frame | 60 | 68 |
| epiBetaR | ecocbo | Data set containing the results of applying ecocbo::sim_beta() to a single factor experiment. | ecocbo_beta | | |
| epiDat | ecocbo | Dataset on species count of marine communities. | data.frame | 24 | 151 |
| macrofDat | ecocbo | Dataset on species count of coastal macrofauna. | data.frame | 36 | 34 |
| simResults | ecocbo | Data set containing the results of applying ecocbo::prep_data(). | ecocbo_data | | |
| simResultsNested | ecocbo | Data set containing the results of applying ecocbo::prep_data() to a nested factors experiment. | ecocbo_data | | |
| modis_dates | npphen | A vector with dates for both RasterStack datasets: Mega drought and Blooming Desert, with 929 dates | Date | | |
| phents | npphen | A dataframe with 8-days MODIS NDVI of a pixel with deciduous Nothofagus macrocarpa forest, Central Chile, using MOD13Q1/MYD13Q1 combined | data.frame | 929 | 2 |
| exrtcga | GOpro | Expressions of human genes. | MultiAssayExperiment | | |
| SUSENAS2020 | SUSENAS | SUSENAS 2020 | tbl_df | 24915 | 299 |
| eSet | epigenomix | Example gene expression data set. | ExpressionSet | | |
| fpkm | epigenomix | Example RNA-seq data set. | data.frame | 3502 | 13 |
| mappedReads | epigenomix | Mapped reads obtained from a anti-histone ChIP-seq experiment. | CompressedGRangesList | | |
| transToTSS | epigenomix | A data frame with Ensemble transcript IDs and transcriptional start sites. | data.frame | 277 | 4 |
| bank_data | fsdaR | Bank data (Riani et al., 2014). | matrix | 1949 | |
| diabetes | fsdaR | Diabetes data | data.frame | 145 | 4 |
| emilia2001 | fsdaR | Demographic data from the 341 miniciplaities in Emilia Romagna (an Italian region). | data.frame | 341 | 28 |
| fishery | fsdaR | Fishery data. | data.frame | 677 | 2 |
| fishery2003 | fsdaR | Fishery 2003 data. | data.frame | 167 | 4 |
| flea | fsdaR | Flea | data.frame | 74 | 7 |
| forbes | fsdaR | Forbes' data on air pressure in the Alps and the boiling point of water (Weisberg, 1985). | data.frame | 17 | 2 |
| geyser2 | fsdaR | Old Faithful Geyser Data. | data.frame | 271 | 2 |
| hawkins | fsdaR | Hawkins data. | data.frame | 128 | 9 |
| hospital | fsdaR | Hospital data (Neter et al., 1996) | matrix | 108 | |
| Income1 | fsdaR | Income1 | data.frame | 200 | 4 |
| Income2 | fsdaR | Income2 | data.frame | 200 | 6 |
| loyalty | fsdaR | Loyalty data | data.frame | 509 | 4 |
| M5data | fsdaR | Mixture M5 Data. | data.frame | 2000 | 3 |
| multiple_regression | fsdaR | Multiple regression data showing the effect of masking (Atkinson and Riani, 2000). | data.frame | 60 | 4 |
| mussels | fsdaR | Mussels data. | data.frame | 82 | 5 |
| poison | fsdaR | Poison | data.frame | 48 | 7 |
| swissbanknotes | fsdaR | Swiss banknote data | data.frame | 200 | 7 |
| swissheads | fsdaR | Swiss heads data | data.frame | 200 | 6 |
| wool | fsdaR | Wool data. | data.frame | 27 | 4 |
| X | fsdaR | Simulated data X. | data.frame | 200 | 2 |
| z1 | fsdaR | z1 | data.frame | 150 | 2 |
| df_em | multibias | Simulated data with exposure misclassification | data.frame | 100000 | 5 |
| df_em_om | multibias | Simulated data with exposure misclassification and outcome misclassification | data.frame | 100000 | 5 |
| df_em_om_source | multibias | Data source for 'df_em_om' | data.frame | 100000 | 7 |
| df_em_sel | multibias | Simulated data with exposure misclassification and selection bias | data.frame | 100000 | 5 |
| df_em_sel_source | multibias | Data source for 'df_em_sel' | data.frame | 100000 | 7 |
| df_em_source | multibias | Data source for 'df_em' | data.frame | 100000 | 6 |
| df_om | multibias | Simulated data with outcome misclassification | data.frame | 100000 | 5 |
| df_om_sel | multibias | Simulated data with outcome misclassification and selection bias | data.frame | 100000 | 5 |
| df_om_sel_source | multibias | Data source for 'df_om_sel' | data.frame | 100000 | 7 |
| df_om_source | multibias | Data source for 'df_om' | data.frame | 100000 | 6 |
| df_sel | multibias | Simulated data with selection bias | data.frame | 100000 | 5 |
| df_sel_source | multibias | Data source for 'df_sel' | data.frame | 100000 | 6 |
| df_uc | multibias | Simulated data with uncontrolled confounding | data.frame | 100000 | 7 |
| df_uc_em | multibias | Simulated data with uncontrolled confounding and exposure misclassification | data.frame | 100000 | 5 |
| df_uc_em_sel | multibias | Simulated data with uncontrolled confounding, exposure misclassification, and selection bias | data.frame | 100000 | 5 |
| df_uc_em_sel_source | multibias | Data source for 'df_uc_em_sel' | data.frame | 100000 | 8 |
| df_uc_em_source | multibias | Data source for 'df_uc_em' | data.frame | 100000 | 7 |
| df_uc_om | multibias | Simulated data with uncontrolled confounding and outcome misclassification | data.frame | 100000 | 5 |
| df_uc_om_sel | multibias | Simulated data with uncontrolled confounding, outcome misclassification, and selection bias | data.frame | 100000 | 5 |
| df_uc_om_sel_source | multibias | Data source for 'df_uc_om_sel' | data.frame | 100000 | 8 |
| df_uc_om_source | multibias | Data source for 'df_uc_om' | data.frame | 100000 | 7 |
| df_uc_sel | multibias | Simulated data with uncontrolled confounding and selection bias | data.frame | 100000 | 5 |
| df_uc_sel_source | multibias | Data source for 'df_uc_sel' | data.frame | 100000 | 7 |
| df_uc_source | multibias | Data source for 'df_uc' | data.frame | 100000 | 8 |
| lena128 | filling | lena image at size of (128 \times 128) | matrix | 128 | |
| lena256 | filling | lena image at size of (256 \times 256) | matrix | 256 | |
| lena64 | filling | lena image at size of (64 \times 64) | matrix | 64 | |
| ghibli_palettes | ghibli | Complete list of available ghibli palettes | list | | |
| heart_disease | cheese | Heart Disease | tbl_df | 303 | 9 |
| talkers | hqmisc | Data set of talkers of Dutch from the Netherlands. | data.frame | 80 | 6 |
| biomass_models | silviculture | Biomass models | tbl_df | 427 | 15 |
| carbon_models | silviculture | Carbon content models | tbl_df | 264 | 13 |
| inventory_samples | silviculture | Forest inventory samples | tbl_df | 162 | 4 |
| snfi3_volume_coefficients | silviculture | SNFI3 tree volume coefficients | tbl_df | 37730 | 16 |
| snfi4_volume_coefficients | silviculture | SNFI4 tree volume coefficients | tbl_df | 20925 | 15 |
| absenteeism | openintro | Absenteeism from school in New South Wales | tbl_df | 146 | 5 |
| acs12 | openintro | American Community Survey, 2012 | tbl_df | 2000 | 13 |
| age_at_mar | openintro | Age at first marriage of 5,534 US women. | tbl_df | 5534 | 1 |
| ames | openintro | Housing prices in Ames, Iowa | tbl_df | 2930 | 82 |
| ami_occurrences | openintro | Acute Myocardial Infarction (Heart Attack) Events | tbl_df | 365 | 1 |
| antibiotics | openintro | Pre-existing conditions in 92 children | tbl_df | 92 | 1 |
| arbuthnot | openintro | Male and female births in London | tbl_df | 82 | 3 |
| arenosa | openintro | arenosa | data.frame | 1088 | 13 |
| ask | openintro | How important is it to ask pointed questions? | tbl_df | 219 | 3 |
| association | openintro | Simulated data for association plots | tbl_df | 121 | 15 |
| assortative_mating | openintro | Eye color of couples | tbl_df | 204 | 2 |
| assortive_mating | openintro | Eye color of couples | tbl_df | 204 | 2 |
| avandia | openintro | Cardiovascular problems for two types of Diabetes medicines | tbl_df | 227571 | 2 |
| babies | openintro | The Child Health and Development Studies | tbl_df | 1236 | 8 |
| babies_crawl | openintro | Crawling age | tbl_df | 12 | 5 |
| bac | openintro | Beer and blood alcohol content | tbl_df | 16 | 3 |
| ball_bearing | openintro | Lifespan of ball bearings | tbl_df | 75 | 1 |
| bdims | openintro | Body measurements of 507 physically active individuals. | tbl_df | 507 | 25 |
| biontech_adolescents | openintro | Efficacy of Pfizer-BioNTech COVID-19 vaccine on adolescents | tbl_df | 2260 | 2 |
| birds | openintro | Aircraft-Wildlife Collisions | tbl_df | 19302 | 17 |
| births | openintro | North Carolina births, 100 cases | tbl_df | 150 | 9 |
| births14 | openintro | US births | tbl_df | 1000 | 13 |
| blizzard_salary | openintro | Blizzard Employee Voluntary Salary Info. | spec_tbl_df | 466 | 9 |
| books | openintro | Sample of books on a shelf | tbl_df | 95 | 2 |
| Brazil_death_anxiety | openintro | Brazil death anxiety | tbl_df | 200 | 28 |
| burger | openintro | Burger preferences | tbl_df | 500 | 2 |
| cancer_in_dogs | openintro | Cancer in dogs | tbl_df | 1436 | 2 |
| cards | openintro | Deck of cards | tbl_df | 52 | 4 |
| cars04 | openintro | cars04 | spec_tbl_df | 428 | 19 |
| cars93 | openintro | cars93 | tbl_df | 54 | 6 |
| cchousing | openintro | Community college housing (simulated data) | tbl_df | 75 | 1 |
| cdc | openintro | cdc | data.frame | 20000 | 9 |
| cdc.samp | openintro | cdc.samp | data.frame | 60 | 9 |
| census | openintro | Random sample of 2000 U.S. Census Data | tbl_df | 500 | 8 |
| census.2010 | openintro | census.2010 | tbl_df | 51 | 3 |
| cherry | openintro | Summary information for 31 cherry trees | tbl_df | 31 | 3 |
| children_gender_stereo | openintro | Gender Stereotypes in 5-7 year old Children | list | | |
| china | openintro | Child care hours | tbl_df | 9788 | 3 |
| cia_factbook | openintro | CIA Factbook Details on Countries | tbl_df | 259 | 11 |
| classdata | openintro | Simulated class data | tbl_df | 164 | 2 |
| cle_sac | openintro | Cleveland and Sacramento | tbl_df | 500 | 8 |
| climate70 | openintro | Temperature Summary Data, Geography Limited | tbl_df | 197 | 7 |
| climber_drugs | openintro | Climber Drugs Data. | tbl_df | 211 | 6 |
| coast_starlight | openintro | Coast Starlight Amtrak train | tbl_df | 16 | 3 |
| COL | openintro | OpenIntro Statistics colors | matrix | 7 | 13 |
| comics | openintro | comics | spec_tbl_df | 21821 | 11 |
| corr_match | openintro | Sample datasets for correlation problems | tbl_df | 121 | 9 |
| country_iso | openintro | Country ISO information | tbl_df | 249 | 4 |
| county_2023 | openintro | county_2023 | tbl_df | 3144 | 43 |
| cpr | openintro | CPR dataset | tbl_df | 90 | 2 |
| cpu | openintro | CPU's Released between 2010 and 2020. | tbl_df | 875 | 12 |
| credits | openintro | College credits. | tbl_df | 100 | 1 |
| danish.ed.primary | openintro | danish.ed.primary | data.frame | 6249 | 21 |
| danish.ed.validation | openintro | Data from a Danish study on triage in an emergency department (ED) | data.frame | 6383 | 18 |
| daycare_fines | openintro | Daycare fines | tbl_df | 200 | 7 |
| dds.discr | openintro | A dataset on disbursements from the California Department of Developmental Services (DDS) | spec_tbl_df | 1000 | 6 |
| diabetes2 | openintro | Type 2 Diabetes Clinical Trial for Patients 10-17 Years Old | tbl_df | 699 | 2 |
| dream | openintro | Survey on views of the DREAM Act | tbl_df | 910 | 2 |
| drone_blades | openintro | Quadcopter Drone Blades | tbl_df | 2000 | 2 |
| drug_use | openintro | Drug use of students and parents | tbl_df | 445 | 2 |
| duke_forest | openintro | Sale prices of houses in Duke Forest, Durham, NC | tbl_df | 98 | 13 |
| earthquakes | openintro | Earthquakes | spec_tbl_df | 123 | 7 |
| ebola_survey | openintro | Survey on Ebola quarantine | tbl_df | 1042 | 1 |
| elmhurst | openintro | Elmhurst College gift aid | tbl_df | 50 | 3 |
| email | openintro | Data frame representing information about a collection of emails | tbl_df | 3921 | 21 |
| email_test | openintro | Data frame representing information about a collection of emails | tbl_df | 1252 | 21 |
| email50 | openintro | Sample of 50 emails | tbl_df | 50 | 21 |
| env_regulation | openintro | American Adults on Regulation and Renewable Energy | tbl_df | 705 | 1 |
| epa2012 | openintro | Vehicle info from the EPA for 2012 | tbl_df | 1129 | 28 |
| epa2021 | openintro | Vehicle info from the EPA for 2021 | tbl_df | 1108 | 28 |
| esi | openintro | Environmental Sustainability Index 2005 | tbl_df | 146 | 29 |
| ethanol | openintro | Ethanol Treatment for Tumors Experiment | tbl_df | 24 | 2 |
| evals | openintro | Professor evaluations and beauty | tbl_df | 463 | 23 |
| EVs_region | openintro | Electric vehicles | data.frame | 5085 | 2 |
| exam_grades | openintro | Exam and course grades for statistics students | spec_tbl_df | 233 | 6 |
| exams | openintro | Exam scores | tbl_df | 19 | 1 |
| exclusive_relationship | openintro | Number of Exclusive Relationships | tbl_df | 218 | 1 |
| fact_opinion | openintro | Can Americans categorize facts and opinions? | tbl_df | 5035 | 3 |
| family_college | openintro | Simulated sample of parent / teen college attendance | tbl_df | 792 | 2 |
| famuss | openintro | A dataset to examine the relationship between muscle strength and the single nucleotide polymorphism (SNP) actn3.r577x. | data.frame | 1397 | 10 |
| fastfood | openintro | Nutrition in fast food | tbl_df | 515 | 17 |
| fcid | openintro | Summary of male heights from USDA Food Commodity Intake Database | tbl_df | 100 | 2 |
| fheights | openintro | Female college student heights, in inches | tbl_df | 24 | 1 |
| fish_age | openintro | Young fish in the North Sea. | spec_tbl_df | 300 | 3 |
| fish_oil_18 | openintro | Findings on n-3 Fatty Acid Supplement Health Benefits | list | | |
| flow_rates | openintro | River flow data | spec_tbl_df | 31 | 3 |
| forest.birds | openintro | A dataset to study the relationship between species abundance of birds and habitat features. | data.frame | 56 | 8 |
| friday | openintro | Friday the 13th | tbl_df | 61 | 6 |
| frog | openintro | Frog Maternal Investment Based on Altitude in Tibetan Plateau | tbl_df | 431 | 6 |
| full_body_scan | openintro | Poll about use of full-body airport scanners | tbl_df | 1137 | 2 |
| gdp_countries | openintro | GDP Countries Data. | data.frame | 654 | 9 |
| gear_company | openintro | Fake data for a gear company example | tbl_df | 2000 | 2 |
| gender_discrimination | openintro | Bank manager recommendations based on gender | tbl_df | 48 | 2 |
| generation_climate_action | openintro | Poll on climate change activism | data.frame | 13664 | 2 |
| get_it_dunn_run | openintro | Get it Dunn Run, Race Times | tbl_df | 978 | 10 |
| gifted | openintro | Analytical skills of young gifted children | tbl_df | 36 | 8 |
| global_warming_pew | openintro | Pew survey on global warming | tbl_df | 2253 | 2 |
| goog | openintro | Google stock data | tbl_df | 98 | 7 |
| gov_poll | openintro | Pew Research poll on government approval ratings | tbl_df | 4223 | 2 |
| gpa | openintro | Survey of Duke students on GPA, studying, and more | tbl_df | 55 | 5 |
| gpa_iq | openintro | Sample of students and their GPA and IQ | tbl_df | 78 | 5 |
| gpa_study_hours | openintro | gpa_study_hours | tbl_df | 193 | 2 |
| gradestv | openintro | Simulated data for analyzing the relationship between watching TV and grades | tbl_df | 25 | 2 |
| gsearch | openintro | Simulated Google search experiment | tbl_df | 10000 | 2 |
| gss_wordsum_class | openintro | gss_wordsum_class | spec_tbl_df | 795 | 2 |
| gss2010 | openintro | 2010 General Social Survey | tbl_df | 2044 | 5 |
| health_coverage | openintro | Health Coverage and Health Status | tbl_df | 20000 | 2 |
| healthcare_law_survey | openintro | Pew Research Center poll on health care, including question variants | tbl_df | 1503 | 2 |
| heart_transplant | openintro | Heart Transplant Data | tbl_df | 103 | 8 |
| helium | openintro | Helium football | tbl_df | 39 | 3 |
| helmet | openintro | Socioeconomic status and reduced-fee school lunches | tbl_df | 12 | 2 |
| hfi | openintro | Human Freedom Index | tbl_df | 1458 | 123 |
| house | openintro | United States House of Representatives historical make-up | tbl_df | 116 | 12 |
| housing | openintro | Simulated dataset on student housing | tbl_df | 75 | 1 |
| hsb2 | openintro | High School and Beyond survey | tbl_df | 200 | 11 |
| husbands_wives | openintro | Great Britain: husband and wife pairs | tbl_df | 199 | 7 |
| hyperuricemia | openintro | Data from an observational study with potential predictors for uric acid levels. | tbl_df | 5168 | 8 |
| hyperuricemia.samp | openintro | Random sample of 500 cases from the 'hyperuricemia' dataset. | tbl_df | 500 | 8 |
| immigration | openintro | Poll on illegal workers in the US | tbl_df | 910 | 2 |
| IMSCOL | openintro | Introduction to Modern Statistics (IMS) Colors | matrix | 8 | 13 |
| infant_mortality_2022 | openintro | United States 2022 infant mortality and number of physicians by state, including the District of Columbia. | tbl_df | 51 | 3 |
| infmortrate | openintro | Infant Mortality Rates, 2012 | tbl_df | 222 | 2 |
| iowa | openintro | iowa | spec_tbl_df | 1386 | 5 |
| ipo | openintro | Facebook, Google, and LinkedIn IPO filings | list | | |
| ipod | openintro | Length of songs on an iPod | tbl_df | 3000 | 1 |
| iran | openintro | iran | spec_tbl_df | 366 | 9 |
| jury | openintro | Simulated juror dataset | tbl_df | 275 | 1 |
| kobe_basket | openintro | Kobe Bryant basketball performance | tbl_df | 133 | 6 |
| labor_market_discrimination | openintro | Are Emily and Greg More Employable Than Lakisha and Jamal? | tbl_df | 4870 | 63 |
| LAhomes | openintro | LAhomes | tbl_df | 1594 | 8 |
| law_resume | openintro | Gender, Socioeconomic Class, and Interview Invites | tbl_df | 316 | 3 |
| LEAP | openintro | Patient level data on the randomized trial Learning Early About Peanut (LEAP) allergies. | tbl_df | 640 | 7 |
| lecture_learning | openintro | Lecture Delivery Method and Learning Outcomes | tbl_df | 552 | 8 |
| leg_mari | openintro | Legalization of Marijuana Support in 2010 California Survey | tbl_df | 119 | 1 |
| lego_population | openintro | Population of Lego Sets for Sale between Jan. 1, 2018 and Sept. 11, 2020. | tbl_df | 1304 | 14 |
| lego_sample | openintro | Sample of Lego Sets | tbl_df | 75 | 14 |
| life_exp | openintro | life_exp | tbl_df | 3142 | 4 |
| lizard_habitat | openintro | Field data on lizards observed in their natural habitat | tbl_df | 332 | 2 |
| lizard_run | openintro | Lizard speeds | spec_tbl_df | 48 | 3 |
| loan50 | openintro | Loan data from Lending Club | tbl_df | 50 | 18 |
| loans_full_schema | openintro | Loan data from Lending Club | tbl_df | 10000 | 55 |
| london_boroughs | openintro | London Borough Boundaries | tbl_df | 45341 | 3 |
| london_murders | openintro | London Murders, 2006-2011 | tbl_df | 838 | 5 |
| mail_me | openintro | Influence of a Good Mood on Helpfulness | tbl_df | 42 | 4 |
| major_survey | openintro | Survey of Duke students and the area of their major | tbl_df | 218 | 2 |
| malaria | openintro | Malaria Vaccine Trial | tbl_df | 20 | 2 |
| male_heights | openintro | Sample of 100 male heights | tbl_df | 100 | 1 |
| male_heights_fcid | openintro | Random sample of adult male heights | tbl_df | 100 | 1 |
| mammals | openintro | Sleep in Mammals | tbl_df | 62 | 11 |
| mammogram | openintro | Experiment with Mammogram Randomized | tbl_df | 89835 | 2 |
| manhattan | openintro | manhattan | spec_tbl_df | 20 | 1 |
| marathon | openintro | New York City Marathon Times (outdated) | tbl_df | 59 | 3 |
| mariokart | openintro | Wii Mario Kart auctions from Ebay | tbl_df | 143 | 12 |
| mcas | openintro | A dataset containing the school-level percentage of students scoring proficient or advanced in the 2018 Grade 10 Mathematics test in the Massachusetts Comprehensive Assessment System, along with characteristics of the school. | tbl_df | 356 | 21 |
| mcu_films | openintro | Marvel Cinematic Universe films | tbl_df | 23 | 7 |
| midterms_house | openintro | President's party performance and unemployment rate | tbl_df | 31 | 5 |
| migraine | openintro | Migraines and acupuncture | tbl_df | 89 | 2 |
| military | openintro | US Military Demographics | tbl_df | 1414593 | 6 |
| mlb | openintro | Salary data for Major League Baseball (2010) | tbl_df | 828 | 4 |
| mlb_players_18 | openintro | Batter Statistics for 2018 Major League Baseball (MLB) Season | tbl_df | 1270 | 19 |
| mlb_teams | openintro | Major League Baseball Teams Data. | data.frame | 2784 | 41 |
| mlbbat10 | openintro | Major League Baseball Player Hitting Statistics for 2010 | tbl_df | 1199 | 19 |
| mn_police_use_of_force | openintro | Minneapolis police use of force data. | data.frame | 12925 | 13 |
| movies | openintro | movies | spec_tbl_df | 140 | 5 |
| mtl | openintro | Medial temporal lobe (MTL) and other data for 26 participants | tbl_df | 35 | 23 |
| murders | openintro | Data for 20 metropolitan areas | tbl_df | 20 | 4 |
| nba_finals | openintro | NBA Finals History | tbl_df | 73 | 9 |
| nba_finals_teams | openintro | NBA Finals Team Summary | tbl_df | 33 | 7 |
| nba_heights | openintro | NBA Player heights from 2008-9 | tbl_df | 435 | 4 |
| nba_players_19 | openintro | NBA Players for the 2018-2019 season | tbl_df | 494 | 7 |
| ncbirths | openintro | North Carolina births, 1000 cases | tbl_df | 1000 | 13 |
| nhanes.samp | openintro | Random sample of 200 observations from the dataset NHANES. | tbl_df | 200 | 76 |
| nhanes.samp.adult | openintro | Selection of participants 21 years of age or older from nhanes.samp. | tbl_df | 135 | 76 |
| nhanes.samp.adult.500 | openintro | A random sample of 500 participants age 21 or older from the full NHANES data. | tbl_df | 500 | 76 |
| nuclear_survey | openintro | Nuclear Arms Reduction Survey | tbl_df | 1028 | 1 |
| nyc | openintro | nyc | tbl_df | 168 | 6 |
| nyc_marathon | openintro | New York City Marathon Times | tbl_df | 108 | 7 |
| nycflights | openintro | Flights data | tbl_df | 32735 | 16 |
| offshore_drilling | openintro | California poll on drilling off the California coast | tbl_df | 828 | 2 |
| openintro_colors | openintro | OpenIntro colors | character | | |
| openintro_palettes | openintro | OpenIntro palettes | list | | |
| opp_insights_colleges | openintro | College education and upward mobility | tbl_df | 2153 | 27 |
| opp_insights_colleges_4year | openintro | Data from 'opp_insights_colleges' that is restricted to 4-year, not-for-profit colleges. | tbl_df | 1285 | 27 |
| opportunity_cost | openintro | Opportunity cost of purchases | tbl_df | 150 | 2 |
| orings | openintro | 1986 Challenger disaster and O-rings | tbl_df | 23 | 4 |
| oscars | openintro | Oscar winners, 1929 to 2018 | tbl_df | 184 | 11 |
| outliers | openintro | Simulated datasets for different types of outliers | tbl_df | 50 | 5 |
| paralympic_1500 | openintro | Race time for Olympic and Paralympic 1500m. | tbl_df | 82 | 9 |
| penelope | openintro | Guesses at the weight of Penelope (a cow) | tbl_df | 17184 | 1 |
| penetrating_oil | openintro | What's the best way to loosen a rusty bolt? | tbl_df | 30 | 2 |
| penny_ages | openintro | Penny Ages | tbl_df | 648 | 2 |
| pew_energy_2018 | openintro | Pew Survey on Energy Sources in 2018 | list | | |
| photo_classify | openintro | Photo classifications: fashion or not | tbl_df | 1822 | 2 |
| piracy | openintro | Piracy and PIPA/SOPA | tbl_df | 534 | 8 |
| playing_cards | openintro | Table of Playing Cards in 52-Card Deck | tbl_df | 52 | 3 |
| pm25_2011_durham | openintro | Air quality for Durham, NC | tbl_df | 449 | 20 |
| pm25_2022_durham | openintro | Air quality for Durham, NC | tbl_df | 356 | 19 |
| poker | openintro | Poker winnings during 50 sessions | tbl_df | 50 | 1 |
| possum | openintro | Possums in Australia and New Guinea | tbl_df | 104 | 8 |
| ppp_201503 | openintro | US Poll on who it is better to raise taxes on | tbl_df | 691 | 2 |
| present | openintro | Birth counts | tbl_df | 63 | 3 |
| president | openintro | United States Presidental History | tbl_df | 67 | 5 |
| prevend | openintro | Data with Ruff Figural Fluency Test (RFFT) scores with demographic predictors and statin use. | tbl_df | 4095 | 31 |
| prevend.samp | openintro | Random sample of size 500 from the prevend dataset | tbl_df | 500 | 31 |
| prison | openintro | Prison isolation experiment | tbl_df | 14 | 6 |
| prius_mpg | openintro | User reported fuel efficiency for 2017 Toyota Prius Prime | tbl_df | 19 | 5 |
| race_justice | openintro | Yahoo! News Race and Justice poll results | tbl_df | 1059 | 2 |
| reddit_finance | openintro | Reddit Survey on Financial Independence. | tbl_df | 1998 | 65 |
| res_demo_1 | openintro | Simulated data for regression | tbl_df | 100 | 3 |
| res_demo_2 | openintro | Simulated data for regression | tbl_df | 300 | 3 |
| resume | openintro | Which resume attributes drive job callbacks? | tbl_df | 4870 | 30 |
| rosling_responses | openintro | Sample Responses to Two Public Health Questions | tbl_df | 278 | 3 |
| russian_influence_on_us_election_2016 | openintro | Russians' Opinions on US Election Influence in 2016 | tbl_df | 506 | 1 |
| sa_gdp_elec | openintro | Sustainability and Economic Indicators for South Africa. | tbl_df | 16 | 7 |
| salinity | openintro | Salinity in Bimini Lagoon, Bahamas | spec_tbl_df | 30 | 2 |
| sat_improve | openintro | Simulated data for SAT score improvement | tbl_df | 30 | 1 |
| satgpa | openintro | SAT and GPA data | tbl_df | 1000 | 6 |
| scotus_healthcare | openintro | Public Opinion with SCOTUS ruling on American Healthcare Act | tbl_df | 1012 | 1 |
| sdea | openintro | Spatial distancing, emotional arousal | spec_tbl_df | 175 | 14 |
| seattlepets | openintro | Names of pets in Seattle | tbl_df | 52519 | 7 |
| sex_discrimination | openintro | Bank manager recommendations based on sex | tbl_df | 48 | 2 |
| simpsons_paradox_covid | openintro | Simpson's Paradox: Covid | tbl_df | 268166 | 3 |
| simulated_dist | openintro | Simulated datasets, not necessarily drawn from a normal distribution. | list | | |
| simulated_normal | openintro | Simulated datasets, drawn from a normal distribution. | list | | |
| simulated_scatter | openintro | Simulated data for sample scatterplots | tbl_df | 2033 | 3 |
| sinusitis | openintro | Sinusitis and antibiotic experiment | tbl_df | 166 | 2 |
| skill_acquisition | openintro | Skill Acquisition | spec_tbl_df | 1035 | 18 |
| sleep_deprivation | openintro | Survey on sleep deprivation and transportation workers | tbl_df | 1087 | 2 |
| smallpox | openintro | Smallpox vaccine results | tbl_df | 6224 | 2 |
| smoking | openintro | UK Smoking Data | tbl_df | 1691 | 12 |
| snowfall | openintro | Snowfall at Paradise, Mt. Rainier National Park | spec_tbl_df | 100 | 3 |
| socialexp | openintro | Social experiment | tbl_df | 45 | 2 |
| soda | openintro | soda | data.frame | 60 | 2 |
| solar | openintro | Energy Output From Two Solar Arrays in San Francisco | tbl_df | 284 | 3 |
| sowc_child_mortality | openintro | SOWC Child Mortality Data. | data.frame | 195 | 18 |
| sowc_demographics | openintro | SOWC Demographics Data. | data.frame | 202 | 18 |
| sowc_maternal_newborn | openintro | SOWC Maternal and Newborn Health Data. | data.frame | 202 | 18 |
| sp500 | openintro | Financial information for 50 S&P 500 companies | tbl_df | 50 | 12 |
| sp500_1950_2018 | openintro | Daily observations for the S&P 500 | tbl_df | 17346 | 7 |
| sp500_seq | openintro | S&P 500 stock data | tbl_df | 2948 | 1 |
| speed_gender_height | openintro | Speed, gender, and height of 1325 students | tbl_df | 1325 | 3 |
| ssd_speed | openintro | SSD read and write speeds | spec_tbl_df | 54 | 7 |
| starbucks | openintro | Starbucks nutrition | tbl_df | 77 | 7 |
| stats_scores | openintro | Final exam scores for twenty students | tbl_df | 20 | 1 |
| stem_cell | openintro | Embryonic stem cells to treat heart attack (in sheep) | tbl_df | 18 | 3 |
| stent30 | openintro | Stents for the treatment of stroke | tbl_df | 451 | 2 |
| stent365 | openintro | Stents for the treatment of stroke | tbl_df | 451 | 2 |
| stocks_18 | openintro | Monthly Returns for a few stocks | tbl_df | 36 | 4 |
| student_housing | openintro | Community college housing (simulated data, 2015) | tbl_df | 175 | 1 |
| student_sleep | openintro | Sleep for 110 students (simulated) | tbl_df | 110 | 1 |
| sugar.levels.A | openintro | Simulated fasting blood sugar levels for 100 residents of a hypothetical neighborhood labeled A. | numeric | | |
| sugar.levels.B | openintro | Simulated fasting blood sugar levels for 100 residents of a hypothetical neighborhood labeled B. | numeric | | |
| sulphinpyrazone | openintro | Treating heart attacks | tbl_df | 1475 | 2 |
| supreme_court | openintro | Supreme Court approval rating | tbl_df | 976 | 1 |
| swim | openintro | Swim velocities using different types of swimsuits | tbl_df | 12 | 6 |
| tb.interruption | openintro | Data used to model a triage scoring scale for a Danish emergency department. | tbl_df | 1293 | 18 |
| teacher | openintro | Teacher Salaries in St. Louis, Michigan | tbl_df | 71 | 8 |
| textbooks | openintro | Textbook data for UCLA Bookstore and Amazon | tbl_df | 73 | 7 |
| thanksgiving_spend | openintro | Thanksgiving spending, simulated based on Gallup poll. | tbl_df | 436 | 1 |
| thermometry | openintro | A dataframe of 130 rows and 3 on body temperature. | data.frame | 130 | 3 |
| tips | openintro | Tip data | tbl_df | 95 | 5 |
| toohey | openintro | Simulated polling dataset | tbl_df | 500 | 1 |
| tourism | openintro | Turkey tourism | tbl_df | 47 | 3 |
| toy_anova | openintro | Simulated dataset for ANOVA | tbl_df | 140 | 2 |
| transplant | openintro | Transplant consultant success rate (fake data) | tbl_df | 62 | 1 |
| twins | openintro | twins | tbl_df | 27 | 2 |
| ucb_admit | openintro | ucb_admit | data.frame | 4526 | 3 |
| ucla_f18 | openintro | UCLA courses in Fall 2018 | tbl_df | 3950 | 14 |
| ucla_textbooks_f18 | openintro | Sample of UCLA course textbooks for Fall 2018 | tbl_df | 201 | 20 |
| ukdemo | openintro | United Kingdom Demographic Data | tbl_df | 12 | 6 |
| unempl | openintro | Annual unemployment since 1890 | tbl_df | 121 | 3 |
| unemploy_pres | openintro | President's party performance and unemployment rate | tbl_df | 29 | 5 |
| us_temperature | openintro | US temperatures in 1950 and 2022 | tbl_df | 18759 | 9 |
| wdi_2022 | openintro | World Development Indicators, 2022. | data.frame | 217 | 11 |
| winery_cars | openintro | Time Between Gondola Cars at Sterling Winery | tbl_df | 52 | 2 |
| world_pop | openintro | World Population Data. | data.frame | 216 | 62 |
| xom | openintro | Exxon Mobile stock data | tbl_df | 98 | 7 |
| yawn | openintro | Contagiousness of yawning | tbl_df | 50 | 2 |
| yrbss | openintro | Youth Risk Behavior Surveillance System (YRBSS) | tbl_df | 13583 | 13 |
| yrbss_samp | openintro | Sample of Youth Risk Behavior Surveillance System (YRBSS) | tbl_df | 100 | 13 |
| getting_started | smer | Simulated Dataset for Genome-Wide Interaction Analysis | list | | |
| BankingCrisesDistances | cops | Banking Crises Distances | matrix | 69 | 70 |
| matchphi | cops | Distances of MATCH-ADTC modules | distance | 32 | 32 |
| Credit | kernelFactory | Credit approval (Frank and Asuncion, 2010) | data.frame | 653 | 16 |
| simMCMC | KDEmcmc | Simulated Markov Chain Monte Carlo Sample | numeric | | |
| adjnoun | networkdata | Words in David Copperfield | igraph | | |
| animal_1 | networkdata | Fishstickleback Proximity (weighted) | list | | |
| animal_10 | networkdata | Philetairus socius | list | | |
| animal_11 | networkdata | Wild birds | list | | |
| animal_12 | networkdata | Ants Proximity (weighted) | list | | |
| animal_13 | networkdata | Ants Trophallaxis (weighted) | list | | |
| animal_14 | networkdata | Beetle Proximity (weighted) | list | | |
| animal_15 | networkdata | Asianelephants Dominance (unweighted) | list | | |
| animal_16 | networkdata | Baboon Association (weighted) | list | | |
| animal_17 | networkdata | Bats Foodsharing (weighted) | list | | |
| animal_18 | networkdata | Bats Roostuse (weighted) | list | | |
| animal_19 | networkdata | Bison Dominance (weighted) | list | | |
| animal_2 | networkdata | Guppy Proximity Frequency (weighted) | list | | |
| animal_20 | networkdata | Cattle Dominance (weighted) | list | | |
| animal_21 | networkdata | Dolphin Association (weighted) | list | | |
| animal_22 | networkdata | Dolphin Proximity (unweighted) | list | | |
| animal_23 | networkdata | Elephantseal Dominance (weighted) | list | | |
| animal_24 | networkdata | Hyenas Groupmembership (weighted) | list | | |
| animal_25 | networkdata | Kangeroos Proximity (weighted) | list | | |
| animal_26 | networkdata | Possum Burrowsharing (weighted) | list | | |
| animal_27 | networkdata | Primates Association (weighted) | list | | |
| animal_28 | networkdata | Raccoon Proximity (weighted) | list | | |
| animal_29 | networkdata | Rhesusmacaque Association (weighted) | list | | |
| animal_3 | networkdata | Hirundo rustica | list | | |
| animal_30 | networkdata | Rhesusmacaque Dominance (weighted) | list | | |
| animal_31 | networkdata | Sheep Dominance (weighted) | list | | |
| animal_32 | networkdata | Spidermonkeys Contact (weighted) | list | | |
| animal_33 | networkdata | Voles Social Projection Bipartite (weighted) | list | | |
| animal_34 | networkdata | Zebra Groupmembership (weighted) | igraph | | |
| animal_35 | networkdata | Desert Tortoise Asynchronous Burrow Sharing (unweighted) | list | | |
| animal_36 | networkdata | Lizard Proximity (weighted) | list | | |
| animal_4 | networkdata | Branta leucopsis | list | | |
| animal_5 | networkdata | White Leghorn | list | | |
| animal_6 | networkdata | Haemorhous mexicanus | list | | |
| animal_7 | networkdata | Zonotrichia Atricapilla | list | | |
| animal_8 | networkdata | Zonotrichia atricapilla | list | | |
| animal_9 | networkdata | Acanthiza | list | | |
| ants_1 | networkdata | Ant Colony (I) | igraph | | |
| ants_2 | networkdata | Ant Colony (II) | igraph | | |
| arenas_email | networkdata | Arenas Email | igraph | | |
| arenas_meta | networkdata | Arenas Metabolic | igraph | | |
| atp | networkdata | ATP Tennis (1968-2021) | list | | |
| bible | networkdata | Nouns in the King's James Bible | igraph | | |
| bkfrab | networkdata | Bernard/Killworth - Fraternity (interaction) | igraph | | |
| bkfrac | networkdata | Bernard/Killworth - Fraternity (rankings) | igraph | | |
| bkoffb | networkdata | Bernard/Killworth - Office (interaction) | igraph | | |
| bkoffc | networkdata | Bernard/Killworth - Office (rankings) | igraph | | |
| bktecb | networkdata | Bernard/Killworth - Tech company (interaction) | igraph | | |
| bktecc | networkdata | Bernard/Killworth - Tech company (rankings) | igraph | | |
| bott | networkdata | Preschool | igraph | | |
| brunson_club_membership | networkdata | Brunson Club Membership | igraph | | |
| brunson_corporate_leadership | networkdata | Brunson Corporate Leadership | igraph | | |
| brunson_revolution | networkdata | Brunson Revolution | igraph | | |
| brunson_south_africa | networkdata | Brunson South Africa | igraph | | |
| cent_lit | networkdata | Centrality literature network | igraph | | |
| ceos_clubs | networkdata | Ceo's and Clubs | igraph | | |
| chicagoroad | networkdata | Road Transportation Network Chicago | igraph | | |
| clique_graph | networkdata | Illustrating cliques | igraph | | |
| coleman | networkdata | Coleman's High School Friendship Data | list | | |
| core_graph | networkdata | Illustrating core-periphery | igraph | | |
| cosponsor | networkdata | Senat 2015 Bill cosponsorship | igraph | | |
| covert_1 | networkdata | 17 November Greece Bombing | list | | |
| covert_10 | networkdata | Caviar | list | | |
| covert_11 | networkdata | Christmas Eve Bombings Indonesia 2000 | list | | |
| covert_12 | networkdata | CielNet | igraph | | |
| covert_13 | networkdata | Cocaine Dealing Natarajan | igraph | | |
| covert_14 | networkdata | Cocaine Smuggling | list | | |
| covert_15 | networkdata | Czech Corruption | igraph | | |
| covert_16 | networkdata | Domestic Terrorist Web Links | igraph | | |
| covert_17 | networkdata | Drugnet | igraph | | |
| covert_18 | networkdata | FIFA | list | | |
| covert_19 | networkdata | Global Suicide Attacks | list | | |
| covert_2 | networkdata | 9/11 Hijackers | list | | |
| covert_20 | networkdata | Hamburg Cell 9/11 2001 | list | | |
| covert_21 | networkdata | Heroin Dealing Natarajan | igraph | | |
| covert_22 | networkdata | Islamic State Allegiances | igraph | | |
| covert_23 | networkdata | Islamic State Group | list | | |
| covert_24 | networkdata | Italian Gangs | igraph | | |
| covert_25 | networkdata | Jakarta Bombing 2009 / Noordin Top | list | | |
| covert_26 | networkdata | Jemaah Islamiyah Koschade | igraph | | |
| covert_27 | networkdata | Linux Terrorists | list | | |
| covert_28 | networkdata | London Gang | igraph | | |
| covert_29 | networkdata | Madoff Fraud | igraph | | |
| covert_3 | networkdata | Al Qaeda 1993-2003 | list | | |
| covert_30 | networkdata | Madrid Train Bombing 2004 | list | | |
| covert_31 | networkdata | Mali Terrorist Network | igraph | | |
| covert_32 | networkdata | Montreal Street Gangs | igraph | | |
| covert_33 | networkdata | Ndrangheta Mafia 2 | igraph | | |
| covert_34 | networkdata | Noordin Top | list | | |
| covert_35 | networkdata | Paul Revere | list | | |
| covert_36 | networkdata | Philippine Kidnappings | igraph | | |
| covert_37 | networkdata | Philippines Bombing | list | | |
| covert_38 | networkdata | Provisional Irish Republican Army | list | | |
| covert_39 | networkdata | Rhodes Bombing | igraph | | |
| covert_4 | networkdata | Australian Embassy Bombing, Indonesia 2004 | list | | |
| covert_40 | networkdata | Saxena Terror India | list | | |
| covert_41 | networkdata | Siren | igraph | | |
| covert_42 | networkdata | Slumlords | list | | |
| covert_43 | networkdata | Southeast Asian Aggregated Attacks 2005 | list | | |
| covert_44 | networkdata | Suffragettes | list | | |
| covert_45 | networkdata | Swingers | igraph | | |
| covert_46 | networkdata | Kenya Tanzania Gerdes | igraph | | |
| covert_47 | networkdata | Togo | igraph | | |
| covert_6 | networkdata | Bali Bombing 2002/Jemaah Islamiyah | list | | |
| covert_7 | networkdata | Bali Bombing 2005 | list | | |
| covert_8 | networkdata | Baseball Steroid Use | list | | |
| covert_9 | networkdata | Big Allied and Dangerous (BAAD) | list | | |
| crime | networkdata | St. Louis Crimes | igraph | | |
| dnc_corecipient | networkdata | DNC Email (Corecipients) | igraph | | |
| dnc_temporalGraph | networkdata | DNC Email (Temporal) | igraph | | |
| dolphins_1 | networkdata | Dolphins (I) | igraph | | |
| dolphins_2 | networkdata | Dolphins (II) | igraph | | |
| eies_messages | networkdata | EIES (Messages) | igraph | | |
| eies_relations | networkdata | EIES (relations) | igraph | | |
| euroroad | networkdata | Road network Europe | igraph | | |
| f2f_hypertext | networkdata | Face-2-face contacts at Hypertext | igraph | | |
| f2f_infectious | networkdata | Face-2-face contacts at Infectious | igraph | | |
| ffe_elite | networkdata | French Financial Elite (elite) | igraph | | |
| ffe_friends | networkdata | French Financial Elite (friendships) | igraph | | |
| ffe_influence | networkdata | French Financial Elite (influence) | igraph | | |
| flo_business | networkdata | Florentine Families (Business) | igraph | | |
| flo_marriage | networkdata | Florentine Families (Marriage) | igraph | | |
| football_triad | networkdata | Simplified football results | list | | |
| fraternity | networkdata | Newcomb Fraternity | list | | |
| giraffe | networkdata | Giraffe Affiliation | igraph | | |
| glasgow129 | networkdata | Teenage Friends and Lifestyle Study data | list | | |
| got | networkdata | Game of Thrones Interactions | list | | |
| greys | networkdata | Grey's Anatomy Hook-ups | igraph | | |
| gss_egor | networkdata | General Social Survey 2004 Egocentric Network Data | egor | | |
| hall | networkdata | Residence Hall Friendship | igraph | | |
| hens | networkdata | Hens Pecking order | igraph | | |
| highschool_boys | networkdata | Friendships among High School Boys | igraph | | |
| ht_advice | networkdata | High-tech Managers (Advice) | igraph | | |
| ht_friends | networkdata | High-tech Managers (Friendships) | igraph | | |
| ht_reports | networkdata | High-tech Managers (Reports to) | igraph | | |
| insna | networkdata | INSNA Teacher Student | igraph | | |
| jazz | networkdata | Collaboration in Jazz | igraph | | |
| jpr | networkdata | Joint Senate Press Releases | igraph | | |
| kangaroo | networkdata | Kangaroo | igraph | | |
| karate | networkdata | Karate Club (binary) | igraph | | |
| karate_weight | networkdata | Karate Club (weighted) | igraph | | |
| knecht | networkdata | Knecht's School Data | list | | |
| law_advice | networkdata | Law Firm (Advice) | igraph | | |
| law_cowork | networkdata | Law Firm (Co-work) | igraph | | |
| law_friends | networkdata | Law Firm (Friendship) | igraph | | |
| literary | networkdata | Swedish Literary Criticism | igraph | | |
| maayan_faa | networkdata | FAA Preferred Routes | igraph | | |
| maayan_pdzbase | networkdata | Maayan Pdzbase | igraph | | |
| macaque | networkdata | Macaque Dominance | igraph | | |
| mine | networkdata | Kapferer - Mine | igraph | | |
| miserables | networkdata | Les Miserables co-appearances | igraph | | |
| movie_1 | networkdata | 10 Things I Hate About You | igraph | | |
| movie_10 | networkdata | 2012 | igraph | | |
| movie_100 | networkdata | Big | igraph | | |
| movie_101 | networkdata | Bear in the Big Blue House | igraph | | |
| movie_102 | networkdata | Big Fish | igraph | | |
| movie_103 | networkdata | The Big Lebowski | igraph | | |
| movie_104 | networkdata | The Big White | igraph | | |
| movie_105 | networkdata | The Birds | igraph | | |
| movie_106 | networkdata | Birthday Girl | igraph | | |
| movie_107 | networkdata | The Black Dahlia | igraph | | |
| movie_108 | networkdata | Black Rain | igraph | | |
| movie_109 | networkdata | Black Snake Moan | igraph | | |
| movie_11 | networkdata | 44 Inch Chest | igraph | | |
| movie_110 | networkdata | Blade | igraph | | |
| movie_111 | networkdata | Blade II | igraph | | |
| movie_112 | networkdata | Blade Runner | igraph | | |
| movie_113 | networkdata | Blade: Trinity | igraph | | |
| movie_114 | networkdata | Blast from the Past | igraph | | |
| movie_115 | networkdata | Blood Simple. | igraph | | |
| movie_116 | networkdata | Blow | igraph | | |
| movie_117 | networkdata | Blue Velvet | igraph | | |
| movie_118 | networkdata | Bodies, Rest & Motion | igraph | | |
| movie_119 | networkdata | Body Heat | igraph | | |
| movie_12 | networkdata | 48 Hrs. | igraph | | |
| movie_120 | networkdata | Body of Evidence | igraph | | |
| movie_121 | networkdata | The Bodyguard | igraph | | |
| movie_122 | networkdata | Bones | igraph | | |
| movie_123 | networkdata | The Bonfire of the Vanities | igraph | | |
| movie_124 | networkdata | Bonnie and Clyde | igraph | | |
| movie_125 | networkdata | Boogie Nights | igraph | | |
| movie_126 | networkdata | The Book of Eli | igraph | | |
| movie_127 | networkdata | The Boondock Saints II: All Saints Day | igraph | | |
| movie_128 | networkdata | The Boondock Saints | igraph | | |
| movie_129 | networkdata | Bottle Rocket | igraph | | |
| movie_13 | networkdata | (500) Days of Summer | igraph | | |
| movie_130 | networkdata | Bound | igraph | | |
| movie_131 | networkdata | The Bounty Hunter | igraph | | |
| movie_132 | networkdata | The Bourne Identity | igraph | | |
| movie_133 | networkdata | The Bourne Ultimatum | igraph | | |
| movie_134 | networkdata | Braveheart | igraph | | |
| movie_135 | networkdata | Brazil | igraph | | |
| movie_136 | networkdata | Breakdown | igraph | | |
| movie_137 | networkdata | The Breakfast Club | igraph | | |
| movie_138 | networkdata | Brick | igraph | | |
| movie_139 | networkdata | Bringing Out the Dead | igraph | | |
| movie_14 | networkdata | 8MM | igraph | | |
| movie_140 | networkdata | Broadcast News | igraph | | |
| movie_141 | networkdata | Broken Arrow | igraph | | |
| movie_142 | networkdata | Broken Embraces | igraph | | |
| movie_143 | networkdata | Bruce Almighty | igraph | | |
| movie_144 | networkdata | Buffy the Vampire Slayer | igraph | | |
| movie_145 | networkdata | Bull Durham | igraph | | |
| movie_146 | networkdata | Burlesque | igraph | | |
| movie_147 | networkdata | Burn After Reading | igraph | | |
| movie_148 | networkdata | The Butterfly Effect | igraph | | |
| movie_149 | networkdata | The Cable Guy | igraph | | |
| movie_15 | networkdata | A Few Good Men | igraph | | |
| movie_150 | networkdata | Capote | igraph | | |
| movie_151 | networkdata | Casino | igraph | | |
| movie_152 | networkdata | Cast Away | igraph | | |
| movie_153 | networkdata | Catch Me If You Can | igraph | | |
| movie_154 | networkdata | The Cell | igraph | | |
| movie_155 | networkdata | Cellular | igraph | | |
| movie_156 | networkdata | Changeling | igraph | | |
| movie_157 | networkdata | Spin City | igraph | | |
| movie_158 | networkdata | Charade | igraph | | |
| movie_159 | networkdata | Charlie's Angels: Full Throttle | igraph | | |
| movie_16 | networkdata | Above the Law | igraph | | |
| movie_160 | networkdata | Chasing Sleep | igraph | | |
| movie_161 | networkdata | Cherry Falls | igraph | | |
| movie_162 | networkdata | Chinatown | igraph | | |
| movie_163 | networkdata | The Chronicles of Narnia: The Lion, the Witch and the Wardrobe | igraph | | |
| movie_164 | networkdata | The Cider House Rules | igraph | | |
| movie_165 | networkdata | The Cincinnati Kid | igraph | | |
| movie_166 | networkdata | Cinema Paradiso | igraph | | |
| movie_167 | networkdata | Citizen Kane | igraph | | |
| movie_168 | networkdata | City of Joy | igraph | | |
| movie_169 | networkdata | Clash of the Titans | igraph | | |
| movie_17 | networkdata | Absolute Power | igraph | | |
| movie_170 | networkdata | Clerks. | igraph | | |
| movie_171 | networkdata | Cliffhanger | igraph | | |
| movie_172 | networkdata | Cobb | igraph | | |
| movie_173 | networkdata | Code of Silence | igraph | | |
| movie_174 | networkdata | Cold Mountain | igraph | | |
| movie_175 | networkdata | Collateral Damage | igraph | | |
| movie_176 | networkdata | Confessions of a Dangerous Mind | igraph | | |
| movie_177 | networkdata | Confidence | igraph | | |
| movie_178 | networkdata | Constantine | igraph | | |
| movie_179 | networkdata | The Cooler | igraph | | |
| movie_18 | networkdata | The Abyss | igraph | | |
| movie_180 | networkdata | Copycat | igraph | | |
| movie_181 | networkdata | Coraline | igraph | | |
| movie_182 | networkdata | Cradle 2 the Grave | igraph | | |
| movie_183 | networkdata | Crank | igraph | | |
| movie_184 | networkdata | Crash | igraph | | |
| movie_185 | networkdata | Crime Spree | igraph | | |
| movie_186 | networkdata | Crouching Tiger, Hidden Dragon | igraph | | |
| movie_187 | networkdata | Croupier | igraph | | |
| movie_188 | networkdata | The Crow: Salvation | igraph | | |
| movie_189 | networkdata | The Crow | igraph | | |
| movie_19 | networkdata | Ace Ventura: Pet Detective | igraph | | |
| movie_190 | networkdata | The Crow: City of Angels | igraph | | |
| movie_191 | networkdata | Cruel Intentions | igraph | | |
| movie_192 | networkdata | The Crying Game | igraph | | |
| movie_193 | networkdata | The Curious Case of Benjamin Button | igraph | | |
| movie_194 | networkdata | The Damned United | igraph | | |
| movie_195 | networkdata | Dances with Wolves | igraph | | |
| movie_196 | networkdata | Dark Star | igraph | | |
| movie_197 | networkdata | Darkman | igraph | | |
| movie_198 | networkdata | Date Night | igraph | | |
| movie_199 | networkdata | Day of the Dead | igraph | | |
| movie_2 | networkdata | 12 | igraph | | |
| movie_20 | networkdata | Adaptation. | igraph | | |
| movie_200 | networkdata | The Day the Clown Cried | igraph | | |
| movie_201 | networkdata | The Day the Earth Stood Still | igraph | | |
| movie_202 | networkdata | Days of Heaven | igraph | | |
| movie_203 | networkdata | Dead Poets Society | igraph | | |
| movie_204 | networkdata | Death at a Funeral | igraph | | |
| movie_205 | networkdata | Death to Smoochy | igraph | | |
| movie_206 | networkdata | Deep Cover | igraph | | |
| movie_207 | networkdata | Deep Rising | igraph | | |
| movie_208 | networkdata | The Deer Hunter | igraph | | |
| movie_209 | networkdata | Defiance | igraph | | |
| movie_21 | networkdata | The Addams Family | igraph | | |
| movie_210 | networkdata | The Departed | igraph | | |
| movie_211 | networkdata | Detroit Rock City | igraph | | |
| movie_212 | networkdata | Devil in a Blue Dress | igraph | | |
| movie_213 | networkdata | The Devil's Advocate | igraph | | |
| movie_214 | networkdata | Die Hard | igraph | | |
| movie_215 | networkdata | The Distinguished Gentleman | igraph | | |
| movie_216 | networkdata | Disturbia | igraph | | |
| movie_217 | networkdata | Do the Right Thing | igraph | | |
| movie_218 | networkdata | Dog Day Afternoon | igraph | | |
| movie_219 | networkdata | Donnie Brasco | igraph | | |
| movie_22 | networkdata | The Adventures of Buckaroo Banzai Across the 8th Dimension | igraph | | |
| movie_220 | networkdata | The Doors | igraph | | |
| movie_221 | networkdata | Double Indemnity | igraph | | |
| movie_222 | networkdata | Drag Me to Hell | igraph | | |
| movie_223 | networkdata | Dragonslayer | igraph | | |
| movie_224 | networkdata | Drop Dead Gorgeous | igraph | | |
| movie_225 | networkdata | A Dry White Season | igraph | | |
| movie_226 | networkdata | Dumb & Dumber | igraph | | |
| movie_227 | networkdata | Dune | igraph | | |
| movie_228 | networkdata | Eagle Eye | igraph | | |
| movie_229 | networkdata | Eastern Promises | igraph | | |
| movie_23 | networkdata | Affliction | igraph | | |
| movie_230 | networkdata | Edtv | igraph | | |
| movie_231 | networkdata | Ed Wood | igraph | | |
| movie_232 | networkdata | Eight Legged Freaks | igraph | | |
| movie_233 | networkdata | Election | igraph | | |
| movie_234 | networkdata | The Elephant Man | igraph | | |
| movie_235 | networkdata | Elizabeth: The Golden Age | igraph | | |
| movie_236 | networkdata | Enemy of the State | igraph | | |
| movie_237 | networkdata | The English Patient | igraph | | |
| movie_238 | networkdata | Entrapment | igraph | | |
| movie_239 | networkdata | Erin Brockovich | igraph | | |
| movie_24 | networkdata | After Life | igraph | | |
| movie_240 | networkdata | Escape from L.A. | igraph | | |
| movie_241 | networkdata | Escape from New York | igraph | | |
| movie_242 | networkdata | Eternal Sunshine of the Spotless Mind | igraph | | |
| movie_243 | networkdata | Even Cowgirls Get the Blues | igraph | | |
| movie_244 | networkdata | Event Horizon | igraph | | |
| movie_245 | networkdata | The Evil Dead | igraph | | |
| movie_246 | networkdata | Evil Dead II | igraph | | |
| movie_247 | networkdata | Excalibur | igraph | | |
| movie_248 | networkdata | Extract | igraph | | |
| movie_249 | networkdata | The Fabulous Baker Boys | igraph | | |
| movie_25 | networkdata | Agnes of God | igraph | | |
| movie_250 | networkdata | Face/Off | igraph | | |
| movie_251 | networkdata | Fair Game | igraph | | |
| movie_252 | networkdata | The Family Man | igraph | | |
| movie_253 | networkdata | Fantastic Four | igraph | | |
| movie_254 | networkdata | Fantastic Mr. Fox | igraph | | |
| movie_255 | networkdata | Fargo | igraph | | |
| movie_256 | networkdata | Fast Times at Ridgemont High | igraph | | |
| movie_257 | networkdata | Fatal Instinct | igraph | | |
| movie_258 | networkdata | Fear and Loathing in Las Vegas | igraph | | |
| movie_259 | networkdata | Babette's Feast | igraph | | |
| movie_26 | networkdata | Air Force One | igraph | | |
| movie_260 | networkdata | Ferris Bueller's Day Off | igraph | | |
| movie_261 | networkdata | The Fifth Element | igraph | | |
| movie_262 | networkdata | Fight Club | igraph | | |
| movie_263 | networkdata | Final Destination | igraph | | |
| movie_264 | networkdata | Final Destination 2 | igraph | | |
| movie_265 | networkdata | Five Easy Pieces | igraph | | |
| movie_266 | networkdata | Fletch | igraph | | |
| movie_267 | networkdata | The Flintstones | igraph | | |
| movie_268 | networkdata | Forrest Gump | igraph | | |
| movie_269 | networkdata | Four Feathers | igraph | | |
| movie_27 | networkdata | Airplane! | igraph | | |
| movie_270 | networkdata | Four Rooms | igraph | | |
| movie_271 | networkdata | Fracture | igraph | | |
| movie_272 | networkdata | French Kiss | igraph | | |
| movie_273 | networkdata | Frankenstein | igraph | | |
| movie_274 | networkdata | Freddy vs. Jason | igraph | | |
| movie_275 | networkdata | The French Connection | igraph | | |
| movie_276 | networkdata | Frequency | igraph | | |
| movie_277 | networkdata | Friday the 13th | igraph | | |
| movie_278 | networkdata | Friday the 13th Part VIII: Jason Takes Manhattan | igraph | | |
| movie_279 | networkdata | From Dusk Till Dawn | igraph | | |
| movie_28 | networkdata | Airplane II: The Sequel | igraph | | |
| movie_280 | networkdata | Cashback | igraph | | |
| movie_281 | networkdata | Frozen River | igraph | | |
| movie_282 | networkdata | The Fugitive | igraph | | |
| movie_283 | networkdata | Funny People | igraph | | |
| movie_284 | networkdata | G.I. Jane | igraph | | |
| movie_285 | networkdata | G.I. Joe: The Rise of Cobra | igraph | | |
| movie_286 | networkdata | Game 6 | igraph | | |
| movie_287 | networkdata | The Game | igraph | | |
| movie_288 | networkdata | Gamer | igraph | | |
| movie_289 | networkdata | Gandhi | igraph | | |
| movie_29 | networkdata | Ali | igraph | | |
| movie_290 | networkdata | Gang Related | igraph | | |
| movie_291 | networkdata | Gangs of New York | igraph | | |
| movie_292 | networkdata | Gattaca | igraph | | |
| movie_293 | networkdata | Get Carter | igraph | | |
| movie_294 | networkdata | The Getaway | igraph | | |
| movie_295 | networkdata | Ghost | igraph | | |
| movie_296 | networkdata | Ghost Rider | igraph | | |
| movie_297 | networkdata | Ghost Ship | igraph | | |
| movie_298 | networkdata | Ghost World | igraph | | |
| movie_299 | networkdata | Ghostbusters | igraph | | |
| movie_3 | networkdata | Twelve and Holding | igraph | | |
| movie_30 | networkdata | Alien 3 | igraph | | |
| movie_300 | networkdata | Ghostbusters II | igraph | | |
| movie_301 | networkdata | Ginger Snaps | igraph | | |
| movie_302 | networkdata | Gladiator | igraph | | |
| movie_303 | networkdata | Go | igraph | | |
| movie_304 | networkdata | The Godfather | igraph | | |
| movie_305 | networkdata | The Godfather: Part II | igraph | | |
| movie_306 | networkdata | Gods and Monsters | igraph | | |
| movie_307 | networkdata | Godzilla | igraph | | |
| movie_308 | networkdata | Gone in Sixty Seconds | igraph | | |
| movie_309 | networkdata | Good Will Hunting | igraph | | |
| movie_31 | networkdata | Alien Nation | igraph | | |
| movie_310 | networkdata | Gothika | igraph | | |
| movie_311 | networkdata | The Graduate | igraph | | |
| movie_312 | networkdata | Gran Torino | igraph | | |
| movie_313 | networkdata | Grand Theft Parsons | igraph | | |
| movie_314 | networkdata | The Green Mile | igraph | | |
| movie_315 | networkdata | Gremlins | igraph | | |
| movie_316 | networkdata | Gremlins 2: The New Batch | igraph | | |
| movie_317 | networkdata | The Grifters | igraph | | |
| movie_318 | networkdata | Grosse Pointe Blank | igraph | | |
| movie_319 | networkdata | Groundhog Day | igraph | | |
| movie_32 | networkdata | AVP: Alien vs. Predator | igraph | | |
| movie_320 | networkdata | The Grudge | igraph | | |
| movie_321 | networkdata | Hackers | igraph | | |
| movie_322 | networkdata | Halloween: The Curse of Michael Myers | igraph | | |
| movie_323 | networkdata | Hancock | igraph | | |
| movie_324 | networkdata | The Hangover | igraph | | |
| movie_325 | networkdata | Hannah and Her Sisters | igraph | | |
| movie_326 | networkdata | Hard to Kill | igraph | | |
| movie_327 | networkdata | Harold & Kumar Go to White Castle | igraph | | |
| movie_328 | networkdata | The Haunting | igraph | | |
| movie_329 | networkdata | He's Just Not That Into You | igraph | | |
| movie_33 | networkdata | Alien: Resurrection | igraph | | |
| movie_330 | networkdata | Heat | igraph | | |
| movie_331 | networkdata | Heathers | igraph | | |
| movie_332 | networkdata | Heavenly Creatures | igraph | | |
| movie_333 | networkdata | The Hebrew Hammer | igraph | | |
| movie_334 | networkdata | Hellbound: Hellraiser II | igraph | | |
| movie_335 | networkdata | Hellboy | igraph | | |
| movie_336 | networkdata | Hellboy II: The Golden Army | igraph | | |
| movie_337 | networkdata | Hellraiser | igraph | | |
| movie_338 | networkdata | Hellraiser III: Hell on Earth | igraph | | |
| movie_339 | networkdata | Hellraiser: Deader | igraph | | |
| movie_34 | networkdata | Aliens | igraph | | |
| movie_340 | networkdata | Hellraiser: Hellseeker | igraph | | |
| movie_341 | networkdata | Henry Fool | igraph | | |
| movie_342 | networkdata | High Fidelity | igraph | | |
| movie_343 | networkdata | Highlander | igraph | | |
| movie_344 | networkdata | Highlander: Endgame | igraph | | |
| movie_345 | networkdata | His Girl Friday | igraph | | |
| movie_346 | networkdata | The Hitchhiker's Guide to the Galaxy | igraph | | |
| movie_347 | networkdata | Hollow Man | igraph | | |
| movie_348 | networkdata | Honeydripper | igraph | | |
| movie_349 | networkdata | The Horse Whisperer | igraph | | |
| movie_35 | networkdata | All About Eve | igraph | | |
| movie_350 | networkdata | The Kingdom | igraph | | |
| movie_351 | networkdata | Hostage | igraph | | |
| movie_352 | networkdata | Hot Tub Time Machine | igraph | | |
| movie_353 | networkdata | Hotel Rwanda | igraph | | |
| movie_354 | networkdata | House of 1000 Corpses | igraph | | |
| movie_355 | networkdata | Hudson Hawk | igraph | | |
| movie_356 | networkdata | The Hudsucker Proxy | igraph | | |
| movie_357 | networkdata | Human Nature | igraph | | |
| movie_358 | networkdata | I Am Sam | igraph | | |
| movie_359 | networkdata | I Love You Phillip Morris | igraph | | |
| movie_36 | networkdata | All About Steve | igraph | | |
| movie_360 | networkdata | I Still Know What You Did Last Summer | igraph | | |
| movie_361 | networkdata | I'll Do Anything | igraph | | |
| movie_362 | networkdata | I, Robot | igraph | | |
| movie_363 | networkdata | The Ice Storm | igraph | | |
| movie_364 | networkdata | The Imaginarium of Doctor Parnassus | igraph | | |
| movie_365 | networkdata | In the Loop | igraph | | |
| movie_366 | networkdata | Independence Day | igraph | | |
| movie_367 | networkdata | Indiana Jones and the Last Crusade | igraph | | |
| movie_368 | networkdata | Indiana Jones and the Temple of Doom | igraph | | |
| movie_369 | networkdata | Raiders of the Lost Ark | igraph | | |
| movie_37 | networkdata | All the King's Men | igraph | | |
| movie_370 | networkdata | The Insider | igraph | | |
| movie_371 | networkdata | Insomnia | igraph | | |
| movie_372 | networkdata | Interview with the Vampire: The Vampire Chronicles | igraph | | |
| movie_373 | networkdata | Into the Wild | igraph | | |
| movie_374 | networkdata | Intolerable Cruelty | igraph | | |
| movie_375 | networkdata | Inventing the Abbotts | igraph | | |
| movie_376 | networkdata | The Invention of Lying | igraph | | |
| movie_377 | networkdata | The Island | igraph | | |
| movie_378 | networkdata | It's a Wonderful Life | igraph | | |
| movie_379 | networkdata | It's Complicated | igraph | | |
| movie_38 | networkdata | Almost Famous | igraph | | |
| movie_380 | networkdata | The Italian Job | igraph | | |
| movie_381 | networkdata | The Jacket | igraph | | |
| movie_382 | networkdata | Jackie Brown | igraph | | |
| movie_383 | networkdata | Jacob's Ladder | igraph | | |
| movie_384 | networkdata | Jason X | igraph | | |
| movie_385 | networkdata | Jaws | igraph | | |
| movie_386 | networkdata | Jaws 2 | igraph | | |
| movie_387 | networkdata | Jay and Silent Bob Strike Back | igraph | | |
| movie_388 | networkdata | Jennifer Eight | igraph | | |
| movie_389 | networkdata | Jennifer's Body | igraph | | |
| movie_39 | networkdata | Alone in the Dark | igraph | | |
| movie_390 | networkdata | Jerry Maguire | igraph | | |
| movie_391 | networkdata | JFK | igraph | | |
| movie_392 | networkdata | Jimmy and Judy | igraph | | |
| movie_393 | networkdata | John Q | igraph | | |
| movie_394 | networkdata | Juno | igraph | | |
| movie_395 | networkdata | Jurassic Park | igraph | | |
| movie_396 | networkdata | Jurassic Park III | igraph | | |
| movie_397 | networkdata | The Lost World: Jurassic Park | igraph | | |
| movie_398 | networkdata | Kalifornia | igraph | | |
| movie_399 | networkdata | Kate & Leopold | igraph | | |
| movie_4 | networkdata | 127 Hours | igraph | | |
| movie_40 | networkdata | Amadeus | igraph | | |
| movie_400 | networkdata | Kids | igraph | | |
| movie_401 | networkdata | Kill Bill: Vol. 1 | igraph | | |
| movie_402 | networkdata | Killing Zoe | igraph | | |
| movie_403 | networkdata | King Kong | igraph | | |
| movie_404 | networkdata | The King of Comedy | igraph | | |
| movie_405 | networkdata | The King's Speech | igraph | | |
| movie_406 | networkdata | Indiana Jones and the Kingdom of the Crystal Skull | igraph | | |
| movie_407 | networkdata | Klute | igraph | | |
| movie_408 | networkdata | Kramer vs. Kramer | igraph | | |
| movie_409 | networkdata | Kundun | igraph | | |
| movie_41 | networkdata | Amelia | igraph | | |
| movie_410 | networkdata | Kung Fu Panda | igraph | | |
| movie_411 | networkdata | L.A. Confidential | igraph | | |
| movie_412 | networkdata | The Ladykillers | igraph | | |
| movie_413 | networkdata | Lake Placid | igraph | | |
| movie_414 | networkdata | Land of the Dead | igraph | | |
| movie_415 | networkdata | The Last Boy Scout | igraph | | |
| movie_416 | networkdata | Last Chance Harvey | igraph | | |
| movie_417 | networkdata | The Last Flight of Noah's Ark | igraph | | |
| movie_418 | networkdata | The Last of the Mohicans | igraph | | |
| movie_419 | networkdata | The Last Samurai | igraph | | |
| movie_42 | networkdata | American Beauty | igraph | | |
| movie_420 | networkdata | The Last Station | igraph | | |
| movie_421 | networkdata | Law Abiding Citizen | igraph | | |
| movie_422 | networkdata | Leaving Las Vegas | igraph | | |
| movie_423 | networkdata | Legion | igraph | | |
| movie_424 | networkdata | Leviathan | igraph | | |
| movie_425 | networkdata | Liar Liar | igraph | | |
| movie_426 | networkdata | Life | igraph | | |
| movie_427 | networkdata | Life as a House | igraph | | |
| movie_428 | networkdata | The Life of David Gale | igraph | | |
| movie_429 | networkdata | Light Sleeper | igraph | | |
| movie_43 | networkdata | American Gangster | igraph | | |
| movie_430 | networkdata | The Limey | igraph | | |
| movie_431 | networkdata | Little Athens | igraph | | |
| movie_432 | networkdata | Little Nicky | igraph | | |
| movie_433 | networkdata | Living in Oblivion | igraph | | |
| movie_434 | networkdata | Lock, Stock and Two Smoking Barrels | igraph | | |
| movie_435 | networkdata | Logan's Run | igraph | | |
| movie_436 | networkdata | Lone Star | igraph | | |
| movie_437 | networkdata | The Long Kiss Goodnight | igraph | | |
| movie_438 | networkdata | Lord of Illusions | igraph | | |
| movie_439 | networkdata | The Lord of the Rings: The Fellowship of the Ring | igraph | | |
| movie_44 | networkdata | American History X | igraph | | |
| movie_440 | networkdata | The Lord of the Rings: The Return of the King | igraph | | |
| movie_441 | networkdata | Lord of War | igraph | | |
| movie_442 | networkdata | The Losers | igraph | | |
| movie_443 | networkdata | Lost Highway | igraph | | |
| movie_444 | networkdata | Lost Horizon | igraph | | |
| movie_445 | networkdata | Lost in Space | igraph | | |
| movie_446 | networkdata | Lost in Translation | igraph | | |
| movie_447 | networkdata | Love & Basketball | igraph | | |
| movie_448 | networkdata | Magnolia | igraph | | |
| movie_449 | networkdata | The Majestic | igraph | | |
| movie_45 | networkdata | American Pie | igraph | | |
| movie_450 | networkdata | Major League | igraph | | |
| movie_451 | networkdata | Malcolm X | igraph | | |
| movie_452 | networkdata | Malibu's Most Wanted | igraph | | |
| movie_453 | networkdata | The Man in the Iron Mask | igraph | | |
| movie_454 | networkdata | Man on Fire | igraph | | |
| movie_455 | networkdata | Man on the Moon | igraph | | |
| movie_456 | networkdata | Knight and Day | igraph | | |
| movie_457 | networkdata | The Man Who Knew Too Much | igraph | | |
| movie_458 | networkdata | The Man Who Wasn't There | igraph | | |
| movie_459 | networkdata | The Manchurian Candidate | igraph | | |
| movie_46 | networkdata | The American President | igraph | | |
| movie_460 | networkdata | Manhunter | igraph | | |
| movie_461 | networkdata | Margot at the Wedding | igraph | | |
| movie_462 | networkdata | Desperado | igraph | | |
| movie_463 | networkdata | Marley & Me | igraph | | |
| movie_464 | networkdata | Marty | igraph | | |
| movie_465 | networkdata | The Mask | igraph | | |
| movie_466 | networkdata | The Matrix Reloaded | igraph | | |
| movie_467 | networkdata | The Matrix | igraph | | |
| movie_468 | networkdata | Max Payne | igraph | | |
| movie_469 | networkdata | Meet Joe Black | igraph | | |
| movie_47 | networkdata | American Psycho | igraph | | |
| movie_470 | networkdata | Megamind | igraph | | |
| movie_471 | networkdata | Memento | igraph | | |
| movie_472 | networkdata | Men in Black | igraph | | |
| movie_473 | networkdata | The Men Who Stare at Goats | igraph | | |
| movie_474 | networkdata | Subway | igraph | | |
| movie_475 | networkdata | Miami Vice | igraph | | |
| movie_476 | networkdata | Midnight Cowboy | igraph | | |
| movie_477 | networkdata | Mighty Morphin Power Rangers: The Movie | igraph | | |
| movie_478 | networkdata | Milk | igraph | | |
| movie_479 | networkdata | Mimic | igraph | | |
| movie_48 | networkdata | American Shaolin | igraph | | |
| movie_480 | networkdata | Mini's First Time | igraph | | |
| movie_481 | networkdata | Minority Report | igraph | | |
| movie_482 | networkdata | Mirrors | igraph | | |
| movie_483 | networkdata | Mission: Impossible | igraph | | |
| movie_484 | networkdata | Mission: Impossible II | igraph | | |
| movie_485 | networkdata | Mission to Mars | igraph | | |
| movie_486 | networkdata | Monkeybone | igraph | | |
| movie_487 | networkdata | Moon | igraph | | |
| movie_488 | networkdata | Moonstruck | igraph | | |
| movie_489 | networkdata | Mr. Blandings Builds His Dream House | igraph | | |
| movie_49 | networkdata | American Splendor | igraph | | |
| movie_490 | networkdata | Mr. Brooks | igraph | | |
| movie_491 | networkdata | Mr. Deeds Goes to Town | igraph | | |
| movie_492 | networkdata | Her Majesty, Mrs. Brown | igraph | | |
| movie_493 | networkdata | Mulholland Dr. | igraph | | |
| movie_494 | networkdata | Mumford | igraph | | |
| movie_495 | networkdata | The Mummy | igraph | | |
| movie_496 | networkdata | Music of the Heart | igraph | | |
| movie_497 | networkdata | My Best Friend's Wedding | igraph | | |
| movie_498 | networkdata | My Girl | igraph | | |
| movie_499 | networkdata | My Mother Dreams the Satan's Disciples in New York | igraph | | |
| movie_5 | networkdata | 1492: Conquest of Paradise | igraph | | |
| movie_50 | networkdata | An American Werewolf in London | igraph | | |
| movie_500 | networkdata | Mystery Men | igraph | | |
| movie_501 | networkdata | Nashville | igraph | | |
| movie_502 | networkdata | Natural Born Killers | igraph | | |
| movie_503 | networkdata | Newsies | igraph | | |
| movie_504 | networkdata | Next | igraph | | |
| movie_505 | networkdata | Next Friday | igraph | | |
| movie_506 | networkdata | The Next Three Days | igraph | | |
| movie_507 | networkdata | Nick of Time | igraph | | |
| movie_508 | networkdata | A Nightmare on Elm Street | igraph | | |
| movie_509 | networkdata | Freddy's Dead: The Final Nightmare | igraph | | |
| movie_51 | networkdata | An Education | igraph | | |
| movie_510 | networkdata | The Nines | igraph | | |
| movie_511 | networkdata | Ninja Assassin | igraph | | |
| movie_512 | networkdata | Ninotchka | igraph | | |
| movie_513 | networkdata | Notting Hill | igraph | | |
| movie_514 | networkdata | Nurse Betty | igraph | | |
| movie_515 | networkdata | O Brother, Where Art Thou? | igraph | | |
| movie_516 | networkdata | Observe and Report | igraph | | |
| movie_517 | networkdata | Wicker Park | igraph | | |
| movie_518 | networkdata | Ocean's Eleven | igraph | | |
| movie_519 | networkdata | Ocean's Twelve | igraph | | |
| movie_52 | networkdata | Analyze That | igraph | | |
| movie_520 | networkdata | One Flew Over the Cuckoo's Nest | igraph | | |
| movie_521 | networkdata | Ordinary People | igraph | | |
| movie_522 | networkdata | Orphan | igraph | | |
| movie_523 | networkdata | The Other Boleyn Girl | igraph | | |
| movie_524 | networkdata | Out of Sight | igraph | | |
| movie_525 | networkdata | The Pacifier | igraph | | |
| movie_526 | networkdata | Panic Room | igraph | | |
| movie_527 | networkdata | The Patriot | igraph | | |
| movie_528 | networkdata | Pearl Harbor | igraph | | |
| movie_529 | networkdata | Peeping Tom | igraph | | |
| movie_53 | networkdata | Analyze This | igraph | | |
| movie_530 | networkdata | A Perfect World | igraph | | |
| movie_531 | networkdata | Pet Sematary | igraph | | |
| movie_532 | networkdata | Pet Sematary II | igraph | | |
| movie_533 | networkdata | Petulia | igraph | | |
| movie_534 | networkdata | Philadelphia | igraph | | |
| movie_535 | networkdata | Phone Booth | igraph | | |
| movie_536 | networkdata | Pi | igraph | | |
| movie_537 | networkdata | The Pianist | igraph | | |
| movie_538 | networkdata | The Piano | igraph | | |
| movie_539 | networkdata | Pineapple Express | igraph | | |
| movie_54 | networkdata | Anastasia | igraph | | |
| movie_540 | networkdata | Pirates of the Caribbean: The Curse of the Black Pearl | igraph | | |
| movie_541 | networkdata | Pitch Black | igraph | | |
| movie_542 | networkdata | Planet of the Apes | igraph | | |
| movie_543 | networkdata | Platinum Blonde | igraph | | |
| movie_544 | networkdata | Platoon | igraph | | |
| movie_545 | networkdata | Pleasantville | igraph | | |
| movie_546 | networkdata | Point Break | igraph | | |
| movie_547 | networkdata | Il Postino: The Postman | igraph | | |
| movie_548 | networkdata | The Power of One | igraph | | |
| movie_549 | networkdata | Precious | igraph | | |
| movie_55 | networkdata | Angel Eyes | igraph | | |
| movie_550 | networkdata | Predator | igraph | | |
| movie_551 | networkdata | Pretty Woman | igraph | | |
| movie_552 | networkdata | The Princess Bride | igraph | | |
| movie_553 | networkdata | The Private Life of Sherlock Holmes | igraph | | |
| movie_554 | networkdata | The Jack Benny Program | igraph | | |
| movie_555 | networkdata | The Prophecy | igraph | | |
| movie_556 | networkdata | The Proposal | igraph | | |
| movie_557 | networkdata | Psycho | igraph | | |
| movie_558 | networkdata | Public Enemies | igraph | | |
| movie_559 | networkdata | Pulp Fiction | igraph | | |
| movie_56 | networkdata | Angels & Demons | igraph | | |
| movie_560 | networkdata | Punch-Drunk Love | igraph | | |
| movie_561 | networkdata | Purple Rain | igraph | | |
| movie_562 | networkdata | Queen of the Damned | igraph | | |
| movie_563 | networkdata | The Queen | igraph | | |
| movie_564 | networkdata | Rachel Getting Married | igraph | | |
| movie_565 | networkdata | Raging Bull | igraph | | |
| movie_566 | networkdata | Raising Arizona | igraph | | |
| movie_567 | networkdata | Rambling Rose | igraph | | |
| movie_568 | networkdata | Rambo: First Blood Part II | igraph | | |
| movie_569 | networkdata | The Reader | igraph | | |
| movie_57 | networkdata | Annie Hall | igraph | | |
| movie_570 | networkdata | Real Genius | igraph | | |
| movie_571 | networkdata | Rear Window | igraph | | |
| movie_572 | networkdata | Red Planet | igraph | | |
| movie_573 | networkdata | The Relic | igraph | | |
| movie_574 | networkdata | Remember Me | igraph | | |
| movie_575 | networkdata | The Replacements | igraph | | |
| movie_576 | networkdata | Reservoir Dogs | igraph | | |
| movie_577 | networkdata | Resident Evil | igraph | | |
| movie_578 | networkdata | Revolutionary Road | igraph | | |
| movie_579 | networkdata | Ring | igraph | | |
| movie_58 | networkdata | The Anniversary Party | igraph | | |
| movie_580 | networkdata | RKO 281 | igraph | | |
| movie_581 | networkdata | The Road | igraph | | |
| movie_582 | networkdata | Robin Hood: Prince of Thieves | igraph | | |
| movie_583 | networkdata | The Rock | igraph | | |
| movie_584 | networkdata | RocknRolla | igraph | | |
| movie_585 | networkdata | Rocky | igraph | | |
| movie_586 | networkdata | The Rocky Horror Picture Show | igraph | | |
| movie_587 | networkdata | Romeo Juliet | igraph | | |
| movie_588 | networkdata | Ronin | igraph | | |
| movie_589 | networkdata | Gunsmoke | igraph | | |
| movie_59 | networkdata | Antitrust | igraph | | |
| movie_590 | networkdata | The Ruins | igraph | | |
| movie_591 | networkdata | Runaway Bride | igraph | | |
| movie_592 | networkdata | Rush Hour | igraph | | |
| movie_593 | networkdata | Rush Hour 2 | igraph | | |
| movie_594 | networkdata | Donnie Darko | igraph | | |
| movie_595 | networkdata | The Saint | igraph | | |
| movie_596 | networkdata | The Salton Sea | igraph | | |
| movie_597 | networkdata | The Sandlot | igraph | | |
| movie_598 | networkdata | Save the Last Dance | igraph | | |
| movie_599 | networkdata | Saving Private Ryan | igraph | | |
| movie_6 | networkdata | 15 Minutes | igraph | | |
| movie_60 | networkdata | Antz | igraph | | |
| movie_600 | networkdata | Scarface | igraph | | |
| movie_601 | networkdata | Schindler's List | igraph | | |
| movie_602 | networkdata | Scream | igraph | | |
| movie_603 | networkdata | Scream 2 | igraph | | |
| movie_604 | networkdata | Scream 3 | igraph | | |
| movie_605 | networkdata | Se7en | igraph | | |
| movie_606 | networkdata | The Searchers | igraph | | |
| movie_607 | networkdata | Semi-Pro | igraph | | |
| movie_608 | networkdata | Sense and Sensibility | igraph | | |
| movie_609 | networkdata | Serenity | igraph | | |
| movie_61 | networkdata | The Apartment | igraph | | |
| movie_610 | networkdata | Serial Mom | igraph | | |
| movie_611 | networkdata | Sex and the City | igraph | | |
| movie_612 | networkdata | Sex, Lies, and Videotape | igraph | | |
| movie_613 | networkdata | Sexual Life | igraph | | |
| movie_614 | networkdata | Shakespeare in Love | igraph | | |
| movie_615 | networkdata | Shallow Grave | igraph | | |
| movie_616 | networkdata | Shampoo | igraph | | |
| movie_617 | networkdata | The Shawshank Redemption | igraph | | |
| movie_618 | networkdata | She's Out of My League | igraph | | |
| movie_619 | networkdata | Sherlock Holmes | igraph | | |
| movie_62 | networkdata | Apocalypse Now | igraph | | |
| movie_620 | networkdata | The Shining | igraph | | |
| movie_621 | networkdata | The Shipping News | igraph | | |
| movie_622 | networkdata | They Came from Within | igraph | | |
| movie_623 | networkdata | Sideways | igraph | | |
| movie_624 | networkdata | The Siege | igraph | | |
| movie_625 | networkdata | Signs | igraph | | |
| movie_626 | networkdata | The Silence of the Lambs | igraph | | |
| movie_627 | networkdata | Silver Bullet | igraph | | |
| movie_628 | networkdata | S1m0ne | igraph | | |
| movie_629 | networkdata | Sister Act | igraph | | |
| movie_63 | networkdata | Apt Pupil | igraph | | |
| movie_630 | networkdata | The Sixth Sense | igraph | | |
| movie_631 | networkdata | Sleepless in Seattle | igraph | | |
| movie_632 | networkdata | Sleepy Hollow | igraph | | |
| movie_633 | networkdata | Sling Blade | igraph | | |
| movie_634 | networkdata | Slither | igraph | | |
| movie_635 | networkdata | Slumdog Millionaire | igraph | | |
| movie_636 | networkdata | Smokin' Aces | igraph | | |
| movie_637 | networkdata | Snatch | igraph | | |
| movie_638 | networkdata | Snow Falling on Cedars | igraph | | |
| movie_639 | networkdata | So I Married an Axe Murderer | igraph | | |
| movie_64 | networkdata | Army of Darkness | igraph | | |
| movie_640 | networkdata | Solaris | igraph | | |
| movie_641 | networkdata | Soldier | igraph | | |
| movie_642 | networkdata | Someone to Watch Over Me | igraph | | |
| movie_643 | networkdata | Something's Gotta Give | igraph | | |
| movie_644 | networkdata | Spanglish | igraph | | |
| movie_645 | networkdata | Spare Me | igraph | | |
| movie_646 | networkdata | Meet the Spartans | igraph | | |
| movie_647 | networkdata | Speed Racer | igraph | | |
| movie_648 | networkdata | Sphere | igraph | | |
| movie_649 | networkdata | Spider-Man | igraph | | |
| movie_65 | networkdata | As Good as It Gets | igraph | | |
| movie_650 | networkdata | Star Trek | igraph | | |
| movie_651 | networkdata | Star Trek II: The Wrath of Khan | igraph | | |
| movie_652 | networkdata | Star Trek: First Contact | igraph | | |
| movie_653 | networkdata | Star Trek: Generations | igraph | | |
| movie_654 | networkdata | Star Trek: Insurrection | igraph | | |
| movie_655 | networkdata | Star Trek: The Motion Picture | igraph | | |
| movie_656 | networkdata | Star Wars: Episode IV - A New Hope | igraph | | |
| movie_657 | networkdata | Star Wars: Episode II - Attack of the Clones | igraph | | |
| movie_658 | networkdata | Star Wars: Episode VI - Return of the Jedi | igraph | | |
| movie_659 | networkdata | Star Wars: Episode V - The Empire Strikes Back | igraph | | |
| movie_66 | networkdata | Assassins | igraph | | |
| movie_660 | networkdata | Starman | igraph | | |
| movie_661 | networkdata | Starship Troopers | igraph | | |
| movie_662 | networkdata | State and Main | igraph | | |
| movie_663 | networkdata | Station West | igraph | | |
| movie_664 | networkdata | Stepmom | igraph | | |
| movie_665 | networkdata | The Sting | igraph | | |
| movie_666 | networkdata | Stir of Echoes | igraph | | |
| movie_667 | networkdata | Storytelling | igraph | | |
| movie_668 | networkdata | Strange Days | igraph | | |
| movie_669 | networkdata | Strangers on a Train | igraph | | |
| movie_67 | networkdata | The Assignment | igraph | | |
| movie_670 | networkdata | Sugar | igraph | | |
| movie_671 | networkdata | Sugar & Spice | igraph | | |
| movie_672 | networkdata | Sunset Blvd. | igraph | | |
| movie_673 | networkdata | Sunshine Cleaning | igraph | | |
| movie_674 | networkdata | Superbad | igraph | | |
| movie_675 | networkdata | The Surfer King | igraph | | |
| movie_676 | networkdata | Suspect Zero | igraph | | |
| movie_677 | networkdata | The Sweet Hereafter | igraph | | |
| movie_678 | networkdata | Sweet Smell of Success | igraph | | |
| movie_679 | networkdata | Swingers | igraph | | |
| movie_68 | networkdata | At First Sight | igraph | | |
| movie_680 | networkdata | Swordfish | igraph | | |
| movie_681 | networkdata | Synecdoche, New York | igraph | | |
| movie_682 | networkdata | Taking Lives | igraph | | |
| movie_683 | networkdata | Taking Sides | igraph | | |
| movie_684 | networkdata | The Talented Mr. Ripley | igraph | | |
| movie_685 | networkdata | Taxi Driver | igraph | | |
| movie_686 | networkdata | The Terminator | igraph | | |
| movie_687 | networkdata | Terminator 2: Judgment Day | igraph | | |
| movie_688 | networkdata | Terminator Salvation | igraph | | |
| movie_689 | networkdata | Thelma & Louise | igraph | | |
| movie_69 | networkdata | Austin Powers: International Man of Mystery | igraph | | |
| movie_690 | networkdata | They | igraph | | |
| movie_691 | networkdata | The Thing | igraph | | |
| movie_692 | networkdata | Thirteen Days | igraph | | |
| movie_693 | networkdata | This Boy's Life | igraph | | |
| movie_694 | networkdata | Three Kings | igraph | | |
| movie_695 | networkdata | Kings of the Turf | igraph | | |
| movie_696 | networkdata | 3 Men and a Baby | igraph | | |
| movie_697 | networkdata | The Three Musketeers | igraph | | |
| movie_698 | networkdata | Thunderheart | igraph | | |
| movie_699 | networkdata | Timber Falls | igraph | | |
| movie_7 | networkdata | 17 Again | igraph | | |
| movie_70 | networkdata | Austin Powers: The Spy Who Shagged Me | igraph | | |
| movie_700 | networkdata | The Time Machine | igraph | | |
| movie_701 | networkdata | Tin Cup | igraph | | |
| movie_702 | networkdata | The Day After Tomorrow | igraph | | |
| movie_703 | networkdata | Titanic | igraph | | |
| movie_704 | networkdata | TMNT | igraph | | |
| movie_705 | networkdata | To Sleep with Anger | igraph | | |
| movie_706 | networkdata | Tombstone | igraph | | |
| movie_707 | networkdata | Tomorrow Never Dies | igraph | | |
| movie_708 | networkdata | Top Gun | igraph | | |
| movie_709 | networkdata | Total Recall | igraph | | |
| movie_71 | networkdata | Autumn in New York | igraph | | |
| movie_710 | networkdata | The Tourist | igraph | | |
| movie_711 | networkdata | Toy Story | igraph | | |
| movie_712 | networkdata | Traffic | igraph | | |
| movie_713 | networkdata | Trainspotting | igraph | | |
| movie_714 | networkdata | The Transformers: The Movie | igraph | | |
| movie_715 | networkdata | Tremors | igraph | | |
| movie_716 | networkdata | TRON | igraph | | |
| movie_717 | networkdata | Tropic Thunder | igraph | | |
| movie_718 | networkdata | True Lies | igraph | | |
| movie_719 | networkdata | True Romance | igraph | | |
| movie_72 | networkdata | Avatar | igraph | | |
| movie_720 | networkdata | The Truman Show | igraph | | |
| movie_721 | networkdata | Twilight | igraph | | |
| movie_722 | networkdata | New Moon | igraph | | |
| movie_723 | networkdata | Twin Peaks | igraph | | |
| movie_724 | networkdata | Dead Ringers | igraph | | |
| movie_725 | networkdata | Two for the Money | igraph | | |
| movie_726 | networkdata | U Turn | igraph | | |
| movie_727 | networkdata | Unbreakable | igraph | | |
| movie_728 | networkdata | Up | igraph | | |
| movie_729 | networkdata | Up in the Air | igraph | | |
| movie_73 | networkdata | The Avengers | igraph | | |
| movie_730 | networkdata | The Usual Suspects | igraph | | |
| movie_731 | networkdata | V for Vendetta | igraph | | |
| movie_732 | networkdata | Valkyrie | igraph | | |
| movie_733 | networkdata | Vanilla Sky | igraph | | |
| movie_734 | networkdata | The Verdict | igraph | | |
| movie_735 | networkdata | Very Bad Things | igraph | | |
| movie_736 | networkdata | Virtuosity | igraph | | |
| movie_737 | networkdata | Wag the Dog | igraph | | |
| movie_738 | networkdata | A Walk to Remember | igraph | | |
| movie_739 | networkdata | Walking Tall | igraph | | |
| movie_74 | networkdata | Awakenings | igraph | | |
| movie_740 | networkdata | Wall Street | igraph | | |
| movie_741 | networkdata | WALL-E | igraph | | |
| movie_742 | networkdata | Wanted | igraph | | |
| movie_743 | networkdata | War of the Worlds | igraph | | |
| movie_744 | networkdata | Warm Springs | igraph | | |
| movie_745 | networkdata | Watchmen | igraph | | |
| movie_746 | networkdata | We Own the Night | igraph | | |
| movie_747 | networkdata | What About Bob? | igraph | | |
| movie_748 | networkdata | What Lies Beneath | igraph | | |
| movie_749 | networkdata | When a Stranger Calls | igraph | | |
| movie_75 | networkdata | Babel | igraph | | |
| movie_750 | networkdata | While She Was Out | igraph | | |
| movie_751 | networkdata | White Christmas | igraph | | |
| movie_752 | networkdata | White Jazz | igraph | | |
| movie_753 | networkdata | White Squall | igraph | | |
| movie_754 | networkdata | Whiteout | igraph | | |
| movie_755 | networkdata | Who Framed Roger Rabbit | igraph | | |
| movie_756 | networkdata | Wild at Heart | igraph | | |
| movie_757 | networkdata | The Wild Bunch | igraph | | |
| movie_758 | networkdata | Where the Wild Things Are | igraph | | |
| movie_759 | networkdata | Wild Wild West | igraph | | |
| movie_76 | networkdata | Bachelor Party | igraph | | |
| movie_760 | networkdata | Willow | igraph | | |
| movie_761 | networkdata | Witness | igraph | | |
| movie_762 | networkdata | Wonder Boys | igraph | | |
| movie_763 | networkdata | The Woodsman | igraph | | |
| movie_764 | networkdata | The Wrestler | igraph | | |
| movie_765 | networkdata | The X Files | igraph | | |
| movie_766 | networkdata | X-Men | igraph | | |
| movie_767 | networkdata | X-Men Origins: Wolverine | igraph | | |
| movie_768 | networkdata | xXx | igraph | | |
| movie_769 | networkdata | Yes Man | igraph | | |
| movie_77 | networkdata | Backdraft | igraph | | |
| movie_770 | networkdata | You Can Count on Me | igraph | | |
| movie_771 | networkdata | You've Got Mail | igraph | | |
| movie_772 | networkdata | Youth in Revolt | igraph | | |
| movie_773 | networkdata | Zerophilia | igraph | | |
| movie_78 | networkdata | Bad Boys | igraph | | |
| movie_79 | networkdata | Bad Day at Black Rock | igraph | | |
| movie_8 | networkdata | One Eight Seven | igraph | | |
| movie_80 | networkdata | The Bad Lieutenant: Port of Call - New Orleans | igraph | | |
| movie_81 | networkdata | Bad Santa | igraph | | |
| movie_82 | networkdata | Badlands | igraph | | |
| movie_83 | networkdata | Bamboozled | igraph | | |
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