Background | Get Started | Installation | Examples | Extensibility and Composability | Future Development | Acknowledgement | Additional Resources

vaprox.split() Demonstration

Lotka-Volterra 2-D Demonstration

Dataset | Scatterplot: long vs depth | Histogram: Distribution of depth by 'long' bin | Comparing the estimation to data | Laplace approximation estimate | MCMC estimate | By-products of the estimation | References

Using SLGPs for discrete distributions | Compute normalized frequencies per long_bin | Histogram: Distribution of mag by 'long' bin | References

Introduction | Data | Anthropometric problems and their algorithmic solutions | Statistical methodologies | Applications | Discussion | Conclusions | Algorithm listings

Getting Started with birp | Input Data Format | Preparing your data | Estimating a Population Trend | Visualizing the result | Testing for Changes in Trends Using Sampling Designs | Before-After (BA) Design: Detecting a Change in Trend Over Time | Before-After (BA) Design with or without Step Changes | Control-Intervention (CI) and Before-After-Control-Intervention (BACI) Designs: Comparing Groups | Control-Intervention (CI) Design | Before-After-Control-Intervention (BACI) Design | Summary | Visualizing the Model Output | Combining multiple survey methods | Running a Negative-Binomial Model | Deciding between Poisson or Negative-binomial | Fitting a Stochastic Trend Model | Using additional covariates | Example: Adding a Detection Covariate | Additional covariates vs stratifying your data | Simulating Example Data | Overview of the birp modeling framework | 1. Population Trend Models | Deterministic Model | Stochastic Model | 2. Observation Model

What MuTATE does | The survival outcome naming convention | Fitting a tree | Summarizing the tree | Pruning | Scoring new data | Visualizing the tree | Tuning hyperparameters

Overview | Setup | Fitting the model | Creating a Markdown table | Customizing titles, descriptions, and citations | Inspecting a formula without a fitted model | The term dictionary | Interactive preview with tabulergm_view()

Overview | A Tutorial | Gaussian-based Model | A Creation of a model | Exploring a data set | Providing the set of parameters | Estimation | Refine estimation | Notes | Exponential-based Model | Exploring of a data set

1. Data Requirements | 1.1 Representing more complex prerequisite relationships | 1.2 Converting the notation to all ANDs | 1.3 Representing electives | 1.3.1 Electives as generic, standalone courses | 1.3.2 Electives as generic, but connected courses | 1.3.3 Customizing pathways with frequently taken electives | 2. Conducting simple curricular analytics | 2.1 Blocking Factor | 2.2 Delay Factor | 2.3 Cruciality | 2.4 Structural Complexity | 2.4.1 Structural complexity and the quarter system | 2.5 Getting a "scorecard" for complexity values | 3. Digging deeper into a plan of study | 3.1 Extracting course sequences | 3.2 Unbundling layers of requirements | 3.3 Other course-level metrics | 3.3.1 Deferment factor | 3.3.2 Bottleneck courses | 3.3.3 Reachability Factor | 3.4.1 Curriculum rigidity | 3.4.2 Topic cruciality | 4. Transfer-Sensitive Metrics | 4.1 Transfer Delay Factor | 4.2 Explained Complexity | 4.3 Inflexibility Factor

1. Curved clusters that defeat Gaussian mixtures | 2. A replication-setting analysis | Options worth knowing

Introduction | Methodological foundation | Workflow overview | Load the package | Input datasets | User-defined objects | Descriptive Zipf analysis | Z2: Multi-Group Power-Law Estimation and RIF Comparison | Z3: RIF Visualization | Advanced usage

Introduction | Methodological foundation | Workflow overview | Load the package | Input datasets | User-defined objects | Descriptive Zipf analysis | Z2: Group-by-Type Power-Law Estimation and RIF Comparison | Z3: RIF Visualization | Advanced usage

Introduction | Methodological foundation | Workflow overview | Load the package | Input dataset | User-defined objects | Descriptive Zipf analysis | Z1: Power-law Estimation and RIF Computation | Z3: RIF Visualization | Advanced usage | plot_zipf | export_rif_plots (custom) | rif_zipf | rif_fit_powerlaw | rif_analysis | z1$zipf | z1$rif_results | rif_compute_results | Exporting and importing Excel files

Introduction | Simulation settings | Part 1: Scalar continuous outcome | Data generation | Weight estimation | Diagnostic plots | Effect estimation (analytical CI) | Effect estimation with bootstrap CI | Binary outcome | Scalar outcome with a functional covariate | All four simulation settings | Part 2: Functional outcome | Effect estimation (no bootstrap) | Effect estimation with bootstrap | Session info

Read spectra | File structure | Deconvolute spectra | Visualize deconvoluted spectra | Align deconvoluted spectra

Load spectra | The pipeline, step by step | Step 1: Deconvolute | Step 2: Align (CluPA) | Step 3: Snap peaks to the reference | Step 4: Build the feature matrix | Step 5: Fit a random forest | The one-shot call | Predict held-out spectra | Tune the preprocessing

Build the package universe | Scan source code | Generate citations | Save scanner data in sysdata.rda

Introduction | Example dataset | Numeric summary | Missing value summary | Correlation analysis | Export numeric summary | Automated EDA | Session information

A landscape to downscale | One predictor | Several predictors | Several responses at once | The one-DEM shortcut | Mapping the local relationship | Where the fit is strong | A time series | Points and other spatial classes | The matrix engine | Practical guidance

A regression in every neighborhood | Constant cost with summed-area tables | Numerical and masking choices | Reporting fit quality | Carrying a time series | What the output preserves | Reference

1. Installation | 2. Authenticate | 3. Discover your devices | 4. Download readings | Working with error codes | 5. Reshape to wide format | 6. Keep a local copy up to date | Next steps

A reusable sync script | Option A — sync when you open a project (RStudio / .Rprofile) | Option B — daily on macOS / Linux (cron via cronR) | Option C — daily on Windows (Task Scheduler) | Tips

Save results so you can reuse them | Look at your data | See what's inside | Filter and sort | Quick summaries | A quick plot | Save and export | Getting help | Learn more R

Introduction | Installation | Authentication | Basics: Discover, download, analyze | Discover feeds | Step 2: Download feeds | Step 3: Analyze feeds with tidytransit | Common use cases | Finding feeds by location | Working with multiple feeds | Downloading feeds to local storage | Example workflow | Advanced features | Accessing archived feeds (datasets) | Check feed quality before downloading | Other feed types | Related packages

GTFS-Realtime data structure | How mobdb handles GTFS-Realtime | Parsing GTFS-Realtime with RProtoBuf | Parsing GTFS-Realtime with gtfsrealtime R package | GBFS data structure | How mobdb handles GBFS | Next Steps

Quick start | Defining a distribution | Uncertain parameters | Discretising | Combining distributions | Plotting | Sampling | Uncertain nonparametric distributions

Top stuff | Create a dataset | All numeric inputs | LOO cross-validation to set the span | Factor inputs

Overview | Quick Start | Interpretation | Advanced Usage: Random Forest | References

Introduction | Installation | Loading the package | Available functions | Meaning of the Ha argument | Choosing a function | Examples for all functions | One-Sample Z Test | Two-Sample Z Test | One-Sample t Test | Two-Sample t Test with Equal Variances | Two-Sample t Test with Unequal Variances | Paired t Test | F Test for Two Variances | Chi-Square Goodness-of-Fit Test | Chi-Square Test of Independence | Chi-Square Test of Homogeneity | Interpreting the graphical output | Assumptions and limitations | Help and citation | Author | Session information

Querying by CpG probe | Querying by gene or genomic region | Enrichment analysis | Plots | Rate limiting

1. The provenance record | 2. Resolving the live source (network) | 3. Synthetic fallback: the pipeline always runs | 4. Integrity and schema validation | 5. The manifest: account for everything | Where this sits in the MORIE family

Simulate a binary pilot | Exact Bernoulli CMR | Inspect the folded-binomial sufficient statistic

Shared-control multi-arm designs | Stratified designs | Raw unbounded outcomes

Simulate a pilot with two outcomes | Weighted-index CMR | Co-primary Outcome CMR | Compare index and co-primary designs

Planning inputs | Break-even pilot share | Activation thresholds | Viability band | Check a desired pilot size

Simulate proxy and primary outcomes | Choose a bridge constant | Proxy CMR | Simulated oracle comparison

Simulate a pilot | Compute the CMR assignment | Convert the share to integer counts | Compare confidence-set methods | Work from a precomputed rectangle

Introduction | Prevalence Study | Cohort Study | Case-Control Study | Diagnostic Sensitivity | Diagnostic Specificity | ROC AUC | Diagnostic Agreement | Working with Results | Summary

Introduction | Installation | Example Usage

Introduction | Setup Data | Title Settings | Canvas Settings | SPC Example | Funnel Example | Axis Settings | X Axis | SPC: Rotating Ticks | Funnel: Axis Label | Y Axis | Date Settings (SPC Only) | Line Settings | SPC: Main Line | Funnel: Limit Lines | Scatter Settings | Basic Point Styling | Point Label Styling (Funnel Only) | Label Settings | Conditional Formatting | Conditional Point Colours | Conditional Line Segments (SPC Only)

Overview | SPC Chart Types | Basic Charts | Run Chart | Individuals Chart (i-chart) | Individuals Chart with Ratios | Individuals Chart with Median Centerline (i_m) | Individuals Chart with Median Limits (i_mm) | Moving Range Chart (mr) | Proportion Charts | p-chart (Proportions) | p-prime Chart (pp) | Rate Charts | u-chart (Rates) | u-prime Chart (up) | Count Charts | c-chart (Counts) | Sample-Based Charts | xbar Chart (Sample Means) | s Chart (Sample Standard Deviations) | Event-Based Charts | g Chart (Geometric) | t Chart (Time Between Events) | Funnel Plot Types | PR - Proportion Funnel | PR Funnel with Overdispersion Adjustment | SR - Standardised Ratio Funnel | SR Funnel with Overdispersion Adjustment | RC - Rate Funnel | RC Funnel with Overdispersion Adjustment | Chart Selection Guide | SPC Chart Selection | Funnel Plot Selection | Overdispersion Adjustment | Data Requirements Summary | SPC Charts | Funnel Plots | Additional Resources

Getting Started | Constructing SPC Charts | Funnel Plot

Introduction | Outlier Detection Methods | Astronomical Points | Shift Detection | Trend Detection | Two-in-Three Detection | Improvement Direction and Flag Types | Improvement Direction | Process Flag Type | NHS Icons | Variation Icons | Assurance Icons | Example: Consistent Pass | Example: Consistent Fail | Example: Variable Performance | Combined Variation and Assurance Icons | Settings Reference | Outlier Detection Settings | NHS Icon Settings | Additional Resources

The modelling problem | Method selection | Data and preprocessing | Worked selection example | Caveats | References

Design principles | Method coverage | Worked validation surface | Validation boundaries | Visualization | Caveats | References

The data | Running preprocess and reading the diagnostics | What the flags mean | Cleaning non-stationary series with detrend | References

Data and preprocessing | Fitting the model | Reading the output | Visualizing the network | References

Data and preprocessing | Fitting the model | Reading the output | Visualizing the network | References

Data and preprocessing | Fitting the model | Reading the output | Visualizing the network | References

Data and preprocessing | Fitting the model | Reading the output | References

Data and preprocessing | Fitting the model | Reading the output | Visualizing the network | References

Data and preprocessing | Fitting the model | Reading the output | Visualizing the network | References

Data and preprocessing | Fitting the model | Reading the output | Visualizing the network | References

What a Gaussian graphical model is | The data | Fitting the network with psychnet() | Inspecting edges with summary() | Checking numerical optimality with certificate() | Describing node position with net_centralities() | Quantifying node predictability with net_predict() | Visualizing the fitted network with cograph::splot() | Sensitivity to the correlation estimator | Applying a substantive edge threshold | A nonparanormal sensitivity model | Reporting the analysis | Why the model removes weak connections | Mathematical foundations | Precision matrix and conditional independence | Penalized likelihood | EBIC model selection | KKT optimality conditions | Predictability from the precision matrix

What an information-filtering network is | The data | Building the correlation backbone with psychnet() | Inspecting the TMFG edges with summary() | Estimating conditional associations with LoGo | Checking the TMFG structure with certificate() | Checking the LoGo solution with certificate() | Describing node position with net_centralities() | Quantifying node predictability with net_predict() | Visualizing the conditional network with cograph::splot() | Sensitivity to rank correlations | Reporting the analysis | How information filtering works | Mathematical foundations | TMFG edge count | LoGo precision matrix | Numerical certificate | Predictability

What an Ising network is | The data | Fitting the network with psychnet() | Inspecting interactions with summary() | Checking numerical optimality with certificate() | Describing node position with net_centralities() | Evaluating binary predictability with net_predict() | Comparing an unregularized Ising estimator | Visualizing the network with cograph::splot() | Reporting the analysis | How the Ising network is estimated | Mathematical foundations

What a mixed graphical model is | The data | Fitting the network with psychnet() | Inspecting mixed edges with summary() | Checking numerical optimality with certificate() | Describing node position with net_centralities() | Evaluating predictability with net_predict() | Sensitivity to the edge-combination rule | Visualizing the network with cograph::splot() | Reporting the analysis | How the mixed network is estimated | Mathematical foundations

Using the Dynalytics event-data grammar | What a network from event data represents | The event log | Estimating one actor-level network with psychnet() | Inspecting actor-level edges with summary() | Checking numerical optimality with certificate() | Visualizing the actor-level network | Estimating one network per achievement level | Estimating a person-centered session network | Keeping the within- and between-actor networks | Reporting the analysis | How event counts and levels are constructed

What a relative-importance network is | The data | Fitting the network with psychnet() | Inspecting directed contributions with summary() | Checking the decomposition with certificate() | Reading incoming explanation with net_predict() | Reading outgoing contribution with net_centralities() | Visualizing direction with cograph::splot() | Sensitivity to rank correlations | Reporting the analysis | How relative importance is computed | Mathematical foundations

What stepwise model selection does | The data | Fitting the network with psychnet() | Inspecting the selected edges with summary() | Checking the fitted solution with certificate() | Comparing the selected model with the graphical lasso | Describing node position with net_centralities() | Quantifying node predictability with net_predict() | Visualizing the network with cograph::splot() | Sensitivity to rank correlations | Reporting the analysis | How stepwise selection works | Mathematical foundations | Gaussian graphical model | Information criterion | Constrained maximum-likelihood certificate | Predictability

Drawing a fitted network | The data and the fit | The default graph | Layouts | Nodes and labels | Edges | Bootstrap diagnostics | Themes | Group networks | Export | The splot arguments at a glance

Introduction | Input data for simulating the population | Creating MAP and QTL file | Creating a historical population | Assigning of population code and haplotype recording | Modelling the breeding program | Visualization of an admixed population | Haplotype plot | References

Introduction | Installation | Data structure | Step 1: Creating rate matrices | Step 3: Shapley decomposition | Step 4: Second‑order interaction indices | Step 5: Uncertainty propagation via parametric bootstrap | Conclusion

Why simPDF | The mental model | Text blocks | Tables and matrices | Plots and full-page figures | Structure: keep-together, spacers, rules, headers/footers | Measurement and quality checks | Authorship and signatures | Model flow diagrams | Using simPDF from your own package

Introduction | Arthritis data | Piston rings data | Alzheimer and smoking | Corporal punishment of children

Introduction | The strucplot framework | Shadings | Labeling | Spacing | Example: Ovarian cancer survival | Conclusion | Data sets

Overview | ID Conversion | Gene ID Conversion | Gene Annotation Tables | Working with Custom Databases

Overview | 1. Load the package | 2. Load the data set | 3. Fit a classical linear regression model | 4. Fit a Riemannian linear regression model using a formula | Formula specification options | Use one explanatory variable | List the explanatory variables explicitly | Use all the remaining variables | Exclude a variable | Include an interaction | Include a mathematical transformation | Include categorical variables | 5. Fit the model by passing the variables separately | 6. Inspect the Riemannian regression results with UMAP | 7. Convert UMAP Riemannian results to the original scale | 8. Fit a Riemannian linear regression model using ISOMAP similarities | 9. Inspect the Riemannian regression results with ISOMAP | 10. Convert ISOMAP Riemannian results to the original scale | 11. Fit a Riemannian linear regression model using DBSCAN similarities | 12. Inspect the Riemannian regression results with DBSCAN | 13. Convert DBSCAN Riemannian results to the original scale | 14. Compare the models | 15. Summary

Overview | 1. Create the data set | 2. Prepare the data for the analysis | 3. Compute UMAP similarities | 4. Compute the Rho matrix | 5. Compute Riemannian differences and distances | 6. Compute Riemannian covariance and correlation | 7. Compute Riemannian principal components | 8. Compute explained inertia | 9. Interpret the subjects in the reduced space | 10. Visualize the principal plane | 11. Visualize the correlation circle | 12. Visualize the biplot

Overview | 1. Load the data set | 2. Prepare the data | 3. Choose the number of neighbors | 4. Compute the UMAP similarities | 5. Compute the Rho matrix | 6. Compute the Riemannian vector differences | 7. Compute the UMAP-based Riemannian distance matrix | 8. Riemannian correlation matrix | 10. Riemannian covariance matrix | 10. Riemannian principal components | 11. Explained inertia | 12. Correlations between variables and components | Estan mal los resultado******************* Dan mal los negativos | 13. Visualizations | 13.1 Principal plane | 13.2 Correlation circle | 13.3 Biplot | 13.4 3D plot

Overview | 1. Load the package | 2. Create the student data set | 3. Prepare the data for the analysis | 4. Fit the Riemannian PCA model | 5. Inspect the eigenvalues | 6. Inspect the UMAP similarity matrix | 7. Inspect the Rho matrix | 8. Inspect the Riemannian differences | 9. Inspect the Riemannian distance matrix | 10. Inspect the Riemannian covariance matrix | 11. Inspect the Riemannian correlation matrix | 12. Inspect the individual coordinates | 13. Inspect the variable coordinates | 14. Plot individuals using the object interface | 15. Plot variables using the object interface | 16. Create a Riemannian biplot using the object interface | 17. Plot individuals using the manual interface | 18. Plot variables using the manual interface | 19. Summary

What Is CMCMC? | Quick Start | Backend Selection | Step Mode, Batch Mode, and Launch Tuning | Common Output Layout | Multivariate Normal | Hierarchical Beta-Binomial Rat Tumor Rates | User Kernel Interface | Model | CUDA and CPU Kernels | Running the Sampler | Posterior Checks | [E(\omega_j \mid y_j,n_j,\alpha,\beta) | References

Overview | Binomial | Poisson | Gaussian | Gamma | Output

Installation | References | Use of RoME function | Use of RoMEcc function | Graphical interface

Summary | Helper functions | Functions for data analyses | Example 1: an Aging Example on t-test, ANOVA, and Tukey's HSD | Example 2: A Simulated Data Example for t-test, ANOVA, and Games-Howell Pairwise Comparison | Example 3: Network Analysis with Ceiling/Floor Data

Introduction | Fuzzy rule-based systems | Package architecture and implementation details | Using the frbs package | Experimental study: Comparison with other packages | Other FRBS packages available on CRAN | Conclusions

Introduction | Input Data Format | Basic Usage | What the Function Does | Output | The 20 Models | References

Two half-circular clusters | The overlap map | An inscribed cluster | Choosing K and predicting new data | Compact overlapping clusters | Beyond classical K-means | Reference

Example 1. Nonlinear curves using kmeans+silhouette and Ball+BIC clustering with a fixed number of clusters | Example 2. Nonlinear curves and patterns using kmeans+silhouette and Ball+BIC clustering with a range for the number of clusters | Example 3. Using the partition around medoids clustering method | Example 4 Random patterns using kmeans+silhouette and Ball+BIC clustering with a range for the number of clusters. | Example 5. Multi-cluster random patterns using kmeans+silhouette and Ball+BIC clustering with a range for the number of clusters. | Example 6. Exclusive or.

Introduction | Model specification | Estimand and adjusted models | Package Functionality through Examples | Summary | G-computation procedure | Borrowing profiles | Additional Plots

Dataset | A familiar table | Output formats | LaTeX (default) | HTML | Custom standard errors via vcov | HC1-robust SEs | Industry-clustered SEs | Mixed SE types across columns | Cosmetic options | Table styles | Summary statistics

Standard error interface | Fixed effects with fixest | Multiple estimators and clustering: the gravity model | Non-linear models with alpaca

Dataset and models | Default output | Robust standard errors | Driscoll-Kraay standard errors | Adding formatting options

The FRAN hook | Scope | Which path to choose

Setup | Specifying a model | Data and estimation | Covariates and behavior co-evolution | Tuning the estimator | Beyond SAOM: ERGM, TERGM, STERGM, ALAAM | Validation

Introduction | Software | Data | Methods | Results | Conclusions | Licensing

Introduction | Simulating data | Fitting a Poisson model (no zero inflation) | Forecasting | AR(1) latent dynamics | Offset | A robust (Student-t) fit on the example data | Zero inflation and structural zeros | Conditional versus unconditional fits and replicates | A binomial model with known trials | Choosing and changing priors | References

The standard 2x2 crossover analysis | Testing SEQ against the correct error term | The 90% confidence interval of the geometric mean ratio | Least squares means of treatments | Notes

Why sasLM? | Procedure-to-function map | PROC GLM | Expected mean squares and random effects | PROC REG | PROC TTEST | PROC UNIVARIATE | 2x2 tables | Notes

A single 2x2 table | Stratified analysis by the MN score method | Inverse variance (meta-analysis) and CMH methods | Example: aspirin and coronary heart disease | Choosing a method

Overview | Key Features | When to Use MV-FMR | Installation | Example: Joint Estimation of Two Exposures | Step 1: Simulate Data | Step 2: Generate Outcome | Step 3: Functional Principal Component Analysis | Step 4: Joint Multivariable Estimation | Step 5: Visualize Time-Varying Effects | Step 6: Extract Coefficients | Step 7: Performance Metrics | Comparison: Joint vs. Separate Estimation | Performance Comparison | Instrument Strength Diagnostics | Binary Outcomes | Extending to More Than Two Exposures | Next Steps | Learn More | Citation | Session Info

Overview | When to Use U-FMR | Installation | Example: Single Exposure Analysis | Step 1: Simulate Data | Step 2: Generate Outcome | Step 3: FPCA for the Single Exposure | Step 4: Univariable Estimation | Step 5: Visualize Time-Varying Effect | Step 6: Extract Results | Step 7: Performance Metrics | Binary Outcomes | Advanced Topics | Available Effect Models | Bootstrap Inference | Two-Sample Design | Next Steps | Learn More | Citation | Session Info

--- 1. SETUP: LOAD LIBRARIES --- | ------------------------------------------------------------------- | --- 2. LOAD AND PREPARE TRAINING DATA --- | --- 3. PREPROCESS THE TEXT --- | Use the pre_process() function from our package to clean the raw text. | This step is done externally to the main pipeline, allowing you to reuse | the same cleaned text for multiple different models or analyses in the future. | --- 4. RUN THE MAIN TRAINING PIPELINE --- | This is the core of the package. We call the main pipeline() function | to handle the train/test split, vectorization, model training, and evaluation. | =================================================================== | --- 5. EVALUATE THE RESULTS | Get the AUC, ROC and Accuracy at Decile Threshold | --- 6. PREDICTION ON NEW, UNSEEN DATA --- | The training is complete. The 'result' object now contains our trained | model and all the necessary "artifacts" for prediction.

Introduction | Background | Spectral density and the DFT | The CGLASSO and deCGLASSO estimators | Step 1 — Generate a multivariate white noise process | Step 2 — Bandwidth selection via GCV | Step 3 — Compute the DFT and smoothed periodogram | Step 4 — Fit CGLASSO and compute the deCGLASSO estimator | Step 5 — Estimate the asymptotic variance | Step 6 — Entry-wise tests and confidence regions | Step 7 — FDR-controlled support recovery | Summary of functions | References

Simple chord diagram | Asymmetric flows | Faceting | Individual layers for more control

Preference-approvals | Distance between preference-approvals | The DIVA consensus | French Presidential Election (2002) | Sensitivity to the weighting parameter | Formula 1 World Championship (1950) | References

Overview | Setup | Inspect data | Define population targets | Rake | Inspect results | Design effect | Per-variable diagnostics | Tidy output | Augmenting the data | Advanced options | Weight bounding | Variable selection | Checking discrepancies directly

Introduction | Exploratory Spatial Data Analysis (ESDA) & Spatial autocorrelation | Moran's I | Exploring the data | Calculate the global Moran's I | Case 1: function moransI | Case 2: functions w.matrix and moransI.w | Local Moran's I | References

Introduction | Analysis | References

Introduction | Stochastic Model | Option Market Data | Pricing Options | Including Default Intensities | Including Term Structures | Fitting Term Structures of Volatility | Fitting Default Intensity | Daycount Conventions | References

Introduction | Scope | Data preparation | loading catchment data | Object model preparation | Calibration and evaluation of the new CemaNeige module | Comparison with the performance of the initial CemaNeige version | References

Introduction | Setup | Extracting JSON data and formatting to LGF model | Extracting the cytoband locations, and the frequency data | Turning CytoData into an S4 Object | Generating Graphs | Plotting Cytoband Data Along the Genome | Plotting Cytoband-Level Data Along One Chromosome | Idiograms | One Data Column | More Data Columns | Gallery | Appendix

Predictive margins (PM) | Other software | Load R packages | Linear model | Generate simulated data | Analysis with Stata | Regression analysis | Predictive margins | Define the desired margins | Calculate the predictive margins | Confidence intervals | Contrasts using the survey package | Multinomial logistic regression model with svyVGAM package | Symmetric confidence intervals | Asymmetric confidence intervals | More formal presentation of the variance estimation

1. Introduction | 2. Quick start with synthetic data | 2.1 Tuning mtry globally | 2.2 Optimal bandwidth | 2.3 Fitting the GRF | 2.4 Inspecting the fit | 2.5 Predicting at new locations | 3. Real-world example: Greek municipal income | 4. Practical tips | References

Introduction | Installation | Example: classo | Example: cglasso | Example: Sparse Spectral Precision Matrix Estimation | Step 1 — Generate a multivariate Gaussian white noise process: | Step 2 — Compute the DFT and smoothed periodogram: | Step 3 — Estimate sparse spectral precision matrix via \texttt{cglasso}: | Step 4 — Visualize the estimated spectral precision matrix: | Step 5 — Compare to the true precision matrix:

Introduction | Example Dataset | Running the Clustering Algorithm | Cluster Assignment | Cluster Quality | Prototype Interpretation | Xie-Beni Index Visualization | Conclusion

Overview | Load the package | Built-in synthetic kiosk demo | 1. Import Gazepoint biometric exports | 2. Inspect real-data readiness | 3. Run the full workflow | 4. EDA, GSR, and SCR examples | 5. Pulse, IBI, HRV, and respiration examples | 6. TTL alignment and model-ready data | 7. Reporting bundle | 8. Feature inventory | 9. Interpretation guardrails | 10. Private real-data smoke tests

All combinations | Preparing the data | Ground-truth labels | Defining the feature methods | Defining the clustering methods | One call to rule them all

Overview | The data | Step 1: reshape into long format | Step 2: extract rolling features | Step 3: normalize | Step 4: choose epsilon (visual heuristic) | Step 5: run DBSCAN | Step 6: evaluate

The pairwise coefficient | Example 1 | Example 2 | Example 3 | Deterministic reference grids | The unified interface: covercorr() | Joint dependence among many variables | A note on the K-variable fixed-grid case | Visualising the joint density on real data

Overview | 0. Load the package and sample data | 1. Key c4h_downscale arguments | 2. Downscaling methods (gridded data) | Method 1: Interpolation | Method 2: Interpolation plus bias correction | Method 3: Interpolation plus linear regression | Method 4: Analogs | 3. Downscaling to point locations | 4. Calibration | 5. Considerations for downscaling common variables | Interpolation-based methods | Specific recommendations for precipitation downscaling | General recommendations | Parameter selection

Overview | 0. Load the package | 1. c4h_get arguments | 2. c4h_get_help | 3. Specifying arguments in c4h_get() | Specifying temporal range | Specifying spatial extent | Specifying variables | Specifying the file path and name | 4. Obtain your Personal Access Token (PAT) | 5. Example downloads | 5.1. ERA5-Land data | 5.2. Hindcast data | 5.3. Forecast data

Overview | Glossary

Overview | Installation | Data requirements | clim4health structure | 1. Obtain input data | 2. Transform and process the data | 3. Prepare outputs | clim4health workflow | 1. Loading, inspecting, and preprocessing data | Dataset description | Data preprocessing | Plot the raw data | 2. Processing the data | Spatial aggregation | Downscaling | Verification and postprocessing | Masking data | Aggregating data temporally | Collapsing data across a dimension | 3. Outputting the data | Convert data from an s2dv_cube to a different data type | Save the data

Overview | 0. Load the package and sample data | 1. Key c4h_verify arguments | 2. Verification metrics | "BSS" - Brier Skill Score | "RPSS" - Ranked Probability Skill Score | "CRPSS" - Continuous Ranked Probability Skill Score | "AbsBiasSS" - Absolute Bias Skill Score | "MSE" - Mean Squared Error | "MSSS" - Mean Squared Error Skill Score | "RMSE" - Root Mean Squared Error | "RMSSS" - Root Mean Square Skill Score | "ROCSS" - Relative Operating Characteristic Skill Score | Statistical Significance | 3. Example skill assessment | Choosing arguments in c4h_verify | ref | brier_thresholds | prob_thresholds | sig_method and sig_method.type | alpha | ncores | N.eff | indices_for_clim | cross.val and clim.cross.val | weights_exp and weights_ref | comp_dim and limits | conf | na.rm | sign and pval | rocss_cat

Overview | 0. Load the package | 1. Forecasts, hindcasts, and reanalyses | 2. Climate data in clim4health | Loading multi-dimensional data with c4h_load | Loading example data | The forecast data is stored with dimensions | The hindcast data is stored with dimensions | The reanalysis data is stored with dimensions | Exploring the loaded data | Data dimensions in clim4health | Exploring the s2dv_cube structure | 3. Constructing an s2dv_cube from scratch | 1. Load the data | 2. Reshape the data | 3. Add dimensions, coordinates, and attributes | 4. Create the s2dv_cube object

Introduction | Example Data | Generate Strata | Evaluate Stratification | Summary | Visualization | Conclusion

Presentation of dataset: CRC Example | Preview of the data | 1) The abundance table | 2) The metadata | 3) The taxonomy | 4) The graph | Aim of this use case | Test if the default parameters of NeighborFinder are suitable for your species of interest & dataset | Apply NeighborFinder & look for Escherichia coli neighbors in CRC patients | Visualize the corresponding network | Apply NeighborFinder with covariate option | Look at the intersection of neighbors found in the 3 subgroups

Why use NeighborFinder? | How to use it? | Input dataframe format | What is behind apply_NeighborFinder() ? | 1) Pre-processing: Counts & Normalization | a) Prevalence filter & shotgun pre-treatment | b) Normalization | 2) Regularized linear regressions | a) Simple case: no covariates | b) Handling covariates | 3) Post-processing | a) Filtering the results | b) Increasing robusteness | How to calibrate the parameters values ?

Overview | Create the R Markdown template | Build your Shiny application

1 Introduction | 2 Object Families | 2.1 Representations of Plots and Stands | 2.2 Species Coding System | 2.3 Yield Tables | 3 Low Level Methods | 4 High Level Methods | 5 Expert Methods | 6 Technical Specifics | 7 Acknowledgments | References

1. Introduction | 2. Where to find things and what they are good for | 2.1 The species master table | 2.2 Specific species codings | 2.2.1 General setup | 2.2.2 Species groups and hierarchical codings | 2.2.3 Non-tree codes | 2.2.4 Implemented codings | 2.2.5 A field-ready coding table | 3. Usage | 3.1 Creating a species code vector | 3.2 Display options | 3.3 Species code conversions | 3.4 Practical examples | 4. Information for developers | 4.1 The data behind the codings | 4.2 How to update the master table or an existing coding | 4.3 Adding a brand-new coding | 4.3.1 Copy the R-source file of an existing coding and adapt it | 4.3.2 Add a species coding cast function to each other coding | 4.3.3 Document the new coding | 4.3.4 Add your new coding to the automated tests for species codings | 4.4 Never touch the source file fe_species_helper_functions.R | References

Overview | Creating memory-mapped objects | Creating objects associated to new files | Creating objects in memory | Conversion to an R object | Mapping pre-existing files | Descriptor Files | Basic usage | Compatibility with bigmemory | Restoring Broken Pointers | Copying objects | Data manipulation | Changing dimensions | Accessing values | Assigning values | Arithmetic Operations | There's no type promotion in houba | In-Place Arithmetic Operations | Row and columns operations | Sums and means | Applying Functions | Contributing to houba

Introduction | The Package in a Glance | General Formulation of the Univariate Stratification Problem \label | Dynamic Programming Technique as a Solution Procedure | Optimum Sample Sizes Using Neyman Allocation | Overview of Package Functionalities | The Function strata.data() | The Function strata.distr() | Application to Numerous Survey Populations | Stratification for a Survey Variable with Pareto Type II Distribution | MPP Formulation for Pareto Type II Distribution | DP Solution for Pareto Type II Distribution | A Numerical Example for Pareto Type II Distribution | Stratification for a Survey Variable with Triangular Distribution | DP Solution for Triangular Distribution | A Numerical Example for Triangular Distribution | Stratification for a Survey Variable with Right-Triangular Distribution | DP Solution for Right-Triangular Distribution | A Numerical Example for Right-Triangular Distribution | Stratification for a Survey Variable with Weibull Distribution | DP Solution for Weibull Distribution | A Numerical Example for Weibull Distribution | Stratification for a Survey Variable with Gamma Distribution | DP Solution for Gamma Distribution | A Numerical Example for Gamma Distribution | Stratification for a Survey Variable with Exponential Distribution | DP Solution for Exponential Distribution | A Numerical Example for Exponential Distribution | Stratification for a Survey Variable with Uniform Distribution | DP Solution for Uniform Distribution | A Numerical Example for Uniform Distribution | Stratification for a Survey Variable with Normal Distribution | DP Solution for Normal Distribution | A Numerical Example for Normal Distribution | Stratification for a Survey Variable with Log-Normal Distribution | DP Solution for Log-Normal Distribution | A Numerical Example for Log-Normal Distribution | Stratification for a Survey Variable with Cauchy Distribution | DP Solution for Cauchy Distribution | A Numerical Example for Cauchy Distribution | References

How to cite | Software needed to run iSTAY in R | Installing and loading iSTAY | FIVE MAIN FUNCTIONS | Datasets Provided with the 'iSTAY' package | Data Conversion | Single metacommunity dataset | Converting metacommunity data to community data | Single community dataset | Hierarchical structure data | Example 1: Comparing stability profiles for two selected individual plots | Example 2: Assessing diversity-stability relationships based on 76 individual plots | Example 3: Comparing stability profiles for two selected individual populations | Example 4: Assessing diversity-stability relationships based on 462 individual populations | Example 5: Comparing gamma, alpha, and beta stability profiles, and synchrony profiles in two selected communities | Equal-weighted analysis | Biomass-weighted analysis | Example 6: Assessing relationships between diversity and gamma, alpha, and beta stability, as well as synchrony, across 76 communities | Example 7: Comparing gamma, alpha, and beta stability profiles, and synchrony profiles for two selected metacommunities | Example 8: Assessing relationships between diversity and gamma, alpha, and beta stability, as well as synchrony, across 20 metacommunities | Example 9: Plotting stability and synchrony profiles at each hierarchical level | References

Reading Data | Threshold selection | Estimation of EVI, scale and T-year levels

Reading Data | Threshold selection | Estimation of spline ML model | High Quantiles

Overview | Measuring dependence with distance correlation | Robust transformations | Independent component analysis with dcorICA() | Serial dependence diagnostics | Normalized HSIC | Overview over Functions | References

Introduction | Installation | Quick Start Example | Load Demo Data | Run Analysis | View Results | Understanding the Output | Advanced Features | Choosing a Weighting Method | Outcome Model | Cross-fitting | Bootstrap Options | Adjust Bootstrap Sample Size | Fast Point Estimates (No Bootstrap) | Parallel Bootstrap | Diagnostic Warnings | Low ESS Warning | Extreme Weights Warning | Positivity Violations | Common Workflows | Standard Analysis Workflow | Method Comparison Workflow | Sensitivity Analysis Workflow | Working with Weights Only | Getting Help

What this tutorial shows | Main idea | 1. Load packages and example data | 2. Introduce the learning and prediction wrappers | Learning step: emulator_learn() | Prediction step: emulator_predict() | 3. Fit the emulator and predict new PDE outputs | What is returned? | 4. Visualize the true PDE solution | 5. Compare FFBS emulation with the PDE solution | 6. Check predictive uncertainty | Practical tips | Summary | References

Package Structure | Regularized Estimator | Quick Start | Installation | Synthetic Data | Example | Real Dataset | Gene Network Construction | Variable Screening | Training Phase | Pathway Analysis | Testing Phase | Session Info

Overview | Data | Fitting the Model with PMLE | Results | Dependence between relapse and death | Regression coefficients for relapse (non-terminal event) | Regression coefficients for death (terminal event) | Comparison: PMLE vs Simultaneous MLE | Reference

Overview | The Texas data | Method 1: bin_means() with known cutpoints | Method 2: bin_means() with cutpoints estimated from data | Method 3: HETOP MLE on a 50-district subsample | Method 4: HETOP Bayes on the full dataset | Summary | References

Quick Start | Background | Methods at a Glance | The aa_init() Function | Supplying Your Own Initialization | Comparing Initializer Biases | Increasing the Number of Archetypes | When Initialization Matters | Nonlinear Geometries | Conclusions and Recommendations | Initialization Caveats | Method Reference | random | dirichlet | Furthest First ("furthest_first") | k-means++ ("kmeans_pp") | Furthest Sum ("furthest_sum") | Batched Coreset-Style Initialization | AA++ ("aa_pp") | Batched AA++ | Hull-outmost ("hull_outmost") | Session Information | References

What is Archetypal Analysis? | Relationship to other methods | The yaap package | A first example: the toy dataset | The archetypes result object | Visualizing the fit | Archetype positions in feature space | Working with "real" data: Fisher's iris | Naming archetypes from their defining observations | Archetype feature profiles | Sample compositions | Predicting reconstructions and compositions for new observations | Practical considerations | Choosing K | Scaling | Multiple restarts | Convergence | Variants of Canonical AA and their implementation in yaap | Relaxing the convex hull constraint with delta | Robust fitting | Missing data | Algorithm details | Algorithmic approaches: NNLS vs PGD | PGD Algorithm | Key arguments | Loss tracking | Conclusion | Limitations and challenges | Summary | Session info | References

One simplex, several geometries | Metric Gaussian AA | Recipe: known measurement-error metric | Functional AA | Kernel AA | Probabilistic AA | Recipe: binary responses | Recipe: multinomial counts | Directional AA | Practical checklist | References

Overview | Using Archetypes in Recipes | Comparing step_archetypes() with step_pca() | Machine Learning Note | Declaring Tunable Parameters

title: "Spatial Queries"output: rmarkdown::html_vignettevignette: >%\VignetteIndexEntry{Introduction to arcpullr}%\VignetteEncoding{UTF-8}%\VignetteEngine{knitr::rmarkdown}editor_options:markdown:wrap: 72 | URL's for examples | get watershed layer for Cook Creek | get_layer_by_point | get_layer_by_polygon | get_layer_by_envelope | Combining Spatial and SQL Queries | Spatial Relationship | Lookup Tables | The valid_sp_rel Function

Introduction | Installation | Example 1: Real Data (mpdta) | Example 2: Simulation --- Homogeneous Effects (TWFE Should Win) | Example 3: Simulation --- Substantial Heterogeneous Effects (ETWFE Should Win) | Example 4: Simulation --- Very Mild Heterogeneity with Small Sample | Monte Carlo Validation | Appendix | Cochran's Q Test | The Heterogeneity Fraction ($I^2$) | Empirical Bayes Shrinkage | Confidence Intervals on the Event Study Plot

Introduction | Methodology | Unsupervised models | Preparing the data | Semi-supervised models | Encoding partial supervision | Fitting the models | Additional details | The $\Gamma$ parameter in possibilistic models | Retrieving intermediate results

Introduction | What is mc2d? | What is Two-Dimensional Monte-Carlo Simulation (briefly)? | A basic example | One-Dimensional Monte-Carlo Simulation | Two-Dimensional Monte-Carlo Simulation | Basic Principles and Functions | Preliminary Step | The mcnode Object | mcnode Object Structure | The mcstoc function | The mcdata function | Operations on an mcnode | The mcprobtree function | Other functions for constructing an mcnode | Specifying a correlation between mcnodes | The mc Object | The mc function | The mcmodel and evalmcmod functions | The mcmodelcut and evalmccut functions | Analysing an mc Object | The summary function | The hist function | The mcratio function | Other Functions and mc Objects | Multivariate Nodes | Multivariate Nodes for Multivariate Distributions | Multivariate Nodes as a Third Dimension for Multiple Options | Multivariate Nodes as a Third Dimension for Multiple Contaminants | Another Example: A QRA of Waterborne Cryptosporidiosis in France | Tap Water Consumption Model | The Dose-Response Model | The Model | As a Conclusion

Including Variability | Specifying Variability Distributions | Initial Contamination | Growth Parameters | Time-Temperature Profiles | Serving Size | Applying the Model | Final Estimate | Including (a Part of the) Uncertainty | Specifying Uncertainty | Prevalence

Overview | Installation | Dependencies | API Requirements | Launching the App | Interface Overview | Sidebar | Main Panel Tabs | Available Models | Elicitation Models | Stan Code Generation Models | Supported Distribution Families | Next Steps

Setup | Step 1 — Define the Clinical Context | Step 2 — Specify the Model and Parameters | Step 3 — Configure Experts | Step 4 — Elicit Priors | Step 5 — Review Expert Responses | Generating Stan Code from an Expert Response | Step 6 — Inspect the Prior Summary | Step 7 — Pool Expert Opinions | Step 8 (Optional) — Delphi Iterative Elicitation | How it works | Running a Delphi session | Exporting Results | Stan Model | Downstream Validation | Troubleshooting

Description | Installation | Use

Overview | 1. Load DICErClust | 2. Download the UCI Heart Failure dataset | 3. Feature engineering | 4. Stratified train / test split | 5. Serialise data in DICErClust format | 6. Configure DICEr | Training stages explained | 7. Train the model | 8. Load the best checkpoint | 9. Evaluate cluster quality | Dynamic cluster labelling | Cluster outcome summary | AUC | Chi-squared test | 10. Figures | Figure 1 — Cluster outcome bar chart | Figure 2 — ROC curve | Summary of results | References

What is DICErClust? | Installation | Data format | Quick start | Loading results | Hyperparameters at a glance | Output directory structure | Full worked example

Connecting to DataRobot | Data | Lift Chart | ROC Curve Data | Threshold operations | Word Cloud

Overview | Example datasets | 1. Reading dendrometer and climate data | read.dendrometer() | read.climate() | 2. Looking for jumps or gaps in dendrometer data | reso_dm() | jump.locator() and i.jump.locator() | dm.na.interpolation() and its plot methods | 3. Resample and truncate the dataset | dendro.resample() | dendro.truncate() | 4. Smoothing | smooth_dm() | 5. Daily approach and daily climate functions | daily.data() and plot.daily_output() | dm_daily_clim() and dm_join_daily_clim() | dm_add_climate() for daily outputs | 6. Stem-cycle approach and associated functions | phase.sc() and plot.SC_output() | dm_subdaily_clim(), dm_join_subdaily_clim(), dm_join_phase_clim(), and dm_add_climate() | 7. Zero-growth approach and associated functions | phase.zg(), twd.maxima(), and plot.ZG_output() | Climate joins and climate-phase plots | 8. Superposed epoch and associated functions | dm_event_times(), dm_epoch_extract(), dm_epoch_test(), and method dispatch | 9. Event-based climate functions | 10. Growth fit and evaluation | dm.fit.gompertz() | dm.growth.fit(), plot.dm_growth_fit(), and print/summary methods | dm.growth.fit.double() and dm.growth.evaluate() | 11. Detrending and associated functions | dm_standardize() and plot.dm_standardized() | 12. Moving correlations and associated functions | 13. Harsh climate using clim.twd() and associated functions | 14. Network interpolation | network.interpolation() and plot.network_interpolation() | 15. Wavelet analysis and associated functions | dm_wavelet(), summary.dm_wavelet(), and plot.dm_wavelet() | dm_wavelet_reconstruct() and its methods | dm_wavelet_coherence() and plot.dm_wavelet_coherence() | Closing note | For suggestions, comments and questions please contact : [email protected]

What's new in dendRoAnalyst 0.1.6 | 1. Core phase engines are now more modular | 2. Dedicated plotting methods for phase outputs | 3. Expanded data import, checking, and quality control | 4. New and upgraded gap-handling tools | 5. Data conditioning tools are broader and clearer | 6. Daily summaries are now easier to compute and inspect | 7. Climate joining helpers support both daily and subdaily workflows | 8. Event-based phase and climate tools were added | 9. Growth modelling is more flexible and easier to evaluate | 10. Detrending is now better linked to growth fitting | 11. Superposed epoch analysis now has a fuller toolkit | 12. Wavelet tools were added for time-frequency analysis | 13. Moving climate–growth correlations were upgraded | 14. Harsh-climate analysis is more complete | 15. Package design now emphasizes compute-first, plot-second workflows | Heads-up on namespaces | In short

Overview | 1. Unconstrained parametrisation | 2. Model fitting | 3. Admissible interval and boundary conditions | 4. Optimal cutoff | 5. Local identifiability diagnostic | 6. Asymptotic variance and Wald confidence interval | 7. ROC curve and AUC | 8. Reproducing Table 3 (Scenario SN1, n = 200)

Introduction | Data | One Learner Library For All Causes | One Learner Library Per Cause

Methods | Implementation overview | Method choice | Implementation details

Introduction | Workflow | Examples for case a): We already know which method to apply | Examples for case b): We are looking for a method to apply

General | Usage | Arguments | Dating Considerations | Output | Output Functions | Negative Correlations? | Model | Iterative Validity Estimation | Example | Bootstrapping | References

Inverse probability weighted estimators | Plotting survival and hazard estimates | Assumption checking and model diagnostics | Testing quasi-independent truncation with covariates | Sensitivity analysis for positivity violations | Stratified Cox models | Time-varying covariates

Doubly truncated data | Nonparametric maximum likelihood estimation | Testing for event time differences across multiple groups | Assumptions required for the NPMLE | Testing for quasi-independent truncation | Testing for ignorable sampling bias

Abstract | Interpreting the magnitude of agreement coeeficients | References:

Abstract | Computing Agreement Coefficients | Computing agreement Coefficients from Contingency tables | Computing agreement coefficients from the distribution of raters by subject & category | Computing agreement coefficients from raw ratings | References:

Abstract | The different weights | Weighted Agreement Coefficients | Weighting ratings from a contingency table | Weighting for a dataset of raw ratings | Weighting when input data is the distribution of raters by subject and rating | References:

Introduction | The selectSpecies( ) function takes the following arguments: | The plotProbs() function takes the following arguments: | Part 1. Examples using one trait with known structure (evenly spaced integers) | Example 1: Derive species assemblage with the following trait profile: | Example 2: Derive species assemblage with the following trait profile: | Example 3: Derive species assemblage with the following trait profile: | Example 4: Derive species assemblage with the following trait profile: | Part 2. Examples using two traits with known structure (evenly spaced integers) | Example 5: Derive species assemblage with the following trait profile: | Example 6: Derive species assemblage with the following trait profile: | Part 3. An example using a real dataset | Example 7: Derive species assemblage for a California serpentine grassland with the following trait profile:

Introduction | Computation method | Definition of a mathematical excursion | Toy examples | A study case | Optimal excursion, the local score | Sub optimal excursions | With a reverse lecture of the protein | What about the excursion realising the local score

Citation | Introduction | In brief | Case of I.I.D. integer score sequence | Generating an I.I.D. score sequence for this example | Graphical representation via the Lindley process | Finding the maximal sum subsequence and all strictly positive sum subsequences | Calculating p-value of local score | Case of markov integer score sequence | Generating a markov score sequence for this example | Case of various dependent structure sequence via Monte-Carlo approach | Generating a complex dependent structure score sequence for this example | Calculating p-value of local score by Monte Carlo simulation | Case of $i$th excursion (sequential order) | Calculating $p$-value of $i$th excursion | Local Score computation methods | A first example: function ``localScoreC()'' | Example with real scores | Example of alphabetical sequence associated to a scoring function | $p$-Value computation methods | Simulating computation: functions "monteCarlo()" | A mixed method: functions "karlinMonteCarlo()" | Exact method for integer scores: function ``daudin()'' | How to use the exact method for real scores | Approximate method of Karlin et al.: function ``karlin()'' | An improved approximate method: function ``mcc()'' | An automatic method: function ``automatic_analysis()'' | Markovian model of the sequence : function exact_mc() | Other Functions | Lindley Process: to visualize optimal and suboptimal segments | Record times: gives the record times of a sequence | Score Loading Function | Empirical distribution: function "scoreSequences2probabilityVector()" | Case study | Medium sequence | Local score computation and parameter model setings | Parameter model setings | Exact method | Approximated method | Improved approximation | Monte Carlo | Result and time computation comparison | Short sequence | Results | Large sequence | Several sequences | A larger example with a SCOP data base | File Formats | Sequence Files | Score Files | Transition Matrix Files

What is an hl-rhotrix? | Creating hl-rhotrices | Dimension 2 | Dimension 4 | Dimension 6 | Determinant | Adjoint | Inverse | Eigenvalues | Full summary | Rhomboidal layout (ggplot2) | References

Description | How to install | Example 1 | Example 2 | Boxplots | Example 3 | Example 4 | Approximation of the true causal measure | Plot | Computation of the Confidence Interval for Win Ratio | References

Recommended DIA-NN export settings | Runnable toy example (no DIA-NN files required) | Headline stability at 1% | Tail support and stability versus threshold | Local boundary support | Threshold elasticity (list sensitivity to changing alpha) | Equal-chance plausibility by q-band | Real DIA-NN parquet workflow | Interpretation notes

Runnable toy example (no external files required) | Headline stability at 1% | Tail support and stability versus threshold | Local boundary support and elasticity | Equal-chance plausibility by q-band | Application to Spectronaut

Runnable toy example (no external files required) | Headline stability at 1% | Tail support and stability versus threshold | Local boundary support and elasticity | Equal-chance plausibility by q-band | Real mzIdentML workflow (.mzid) | Optional: pseudo-p-values from the decoy score tail | Interpretation notes / common pitfalls

Runnable toy example (no external files required) | Headline stability at 1% | Decoy tail support and stability proxy | Local boundary support and elasticity | Equal-chance plausibility by q-band | Real MaxQuant workflow (msms.txt) | Optional: other way to get p-value / pseudo-p-value diagnostics from MaxQuant scores | Interpretation notes

Runnable toy example (no external files required) | Headline stability at 1% | Decoy tail support and stability proxy | Local boundary support and elasticity | Equal-chance plausibility by q-band (internal check) | PEP reliability and expected errors (ΣPEP) | Real mokapot workflow (targets + decoys text exports) | Interpretation notes

Introduction | Main function of the package | References

Overview | Typical workflow | Example dataset | Computing OJIP parameters | Key parameters calculated | Multivariate visualization | Heatmaps | 3D plots | Biolyzer export workflow | FluorPen workflow | Validation resources | Shiny interface | Summary | References

What is climatehealth? | Installation | From CRAN | From GitHub (latest development version) | Optional dependencies | Package workflow | Your first analysis: temperature and mortality | Adding covariates | Pooling across regions with meta-analysis | The six indicators | Air pollution | Wildfires | Mental health (suicides and heat) | Water-borne diseases (diarrhoea) | Vector-borne diseases (malaria) | Descriptive statistics | Saving outputs | Error handling | Next steps

Overview | Object flow | End-to-end workflow example (2 H3 resolutions) | Required input structure | Assumptions | The strata classification | Why multiscale feature evaluation is needed | Summary

1. Introduction | 2. Getting started | 2.1. Installation | 2.2. Load required packages and data | 2.3. Prepare Variables and Function parameters | 2.4. Run the main algorithm | 2.5. Other results | 2.5.1. Naive approach | 2.5.2. Oracular results | 3. Play with results | Appendix. Understanding the Algorithm Internally | Preliminary Step: Data Checks and Cross-Fitting Folds | Step 1: Estimate and Save Nuisance Parameters | Step 2: Policy optimization

Introduction | Setup | Step 1: Data Preparation | Step 2: Genomic Feature Annotation | Step 3: Integration Site Analysis | Common Integration Sites (CIS) | Chromosome Distribution | Gene Set Overlap | Step 4: Longitudinal Analysis (PMD) | Step 5: Clonal Dominance Analysis | Step 6: Visualization | Treemap | Region Counts | Ideogram

Introduction | Example 1: Binary Data | Data | Full Workflow Call | Load and Inspect Precomputed Results | Parallel Analysis | Bootstrap Stability Diagnostics | ALSI Computation | Category Projections | Example 2: Ordinal Data | Ordinal ALSI Computation | Example 3: Continuous Data | Continuous ALSI Computation and Domain Contributions | Comparison with SEPA Plane-Wise Summaries | Session Information

1. Introduction to the n-TARP clustering method | 1.1 Installing the package and running the core n-TARP function | 1.1 Getting the optimal solution from the nTARP function | 1.2 Using the results of nTARP as a classifier | 1.3 Checking the clusterability of the dataset | 1.4 The last outputs of the nTARP function | 2. Extending nTARP to form multiple clusters

Load the package and example data | Convert to a binary data frame | Observed variance components | Statistical Power analysis | Bootstrap confidence intervals | Resample observations | Iteration variance components | Extract confidence intervals | Bias and accelerated corrected confidence intervals | Jackknife confidence intervals | Plotting confidence intervals | Bar plot | Box plot

Load the package and example data | Convert to a binary data frame | Observed variance components | Statistical Power analysis | Bootstrap confidence intervals | Resample observations | Iteration variance components | Extract confidence intervals | Bias and accelerated corrected confidence intervals | Jackknife confidence intervals | Plotting confidence intervals | Bar plot | Box plot

Load the package and example data | Convert to a binary data frame | Observed variance components | Statistical Power analysis | Bootstrap confidence intervals | Resample observations | Iteration variance components | Extract confidence intervals | Bias and accelerated corrected confidence intervals | Jackknife confidence intervals | Plotting confidence intervals | Bar plot | Box plot

Overview | Quick Start | 1. Define the Multistate Structure | 2. Simulate Data | 3. Prepare Multistate Data | 4. Aalen-Johansen Nonparametric Baseline | 5. Fit Random Forest Model | 6. Model Summary | 7. Feature Importance | 8. Predict for New Patients | 9. Diagnostics | 10. Transition Diagram | Methodology | Product-Integral Framework | Aalen-Johansen Estimator (Nonparametric Baseline) | Random Forest Multistate Approach | Diagnostics | References

Introduction | Data Aquisition | Quick temperature overview | Heat Index computation | Visualisation

Promoting dominant function patterns | Demoting non-dominant function patterns | Robust to change in low expected count | Lung transplant surgery type and outcome | A contingency table showing similar testing results | How to cite this document | References

Multi-objective Markov decision processes | Transition probabilities and rewards | Decision rules and policies | Value functions and preference patterns | An example | References

TLDR | Example report preview | Overview | Bundled example files | Read raw header lines | Parse a VCF header | Inspect parsed content | Inference from the header | Structural variant example | Generate an HTML report | Bundled example HTML report | Summary

VectorSurv | Data Retrieval | Write Data to file | Sample Data | Data Processing | Subsetting | Filtering and subsetting in 'dplyr' | Grouping and Summarising | Pivoting | Calculations | Abundance | Abundance Anomaly (comparison to 5 year average) | Infection Rate | Vector Index | Tables | Styling Dataframes with 'kable' | Data using 'datatables'

Contents | Loading data | Distribution of baseline p-values for continuous variables | Matches of baseline summary statistics | Matches of summary statistics in different cohorts | Comparing means between groups | Distribution of a single categorical variable | Distributions of numbers of participants in trial groups | Distributions of all categorical variables | Comparing reported and calculated p-values for categorical variables | Distributions of p-values for categorical variables | Distribution of final digits | Graph maker | Final comment

The Data | Forest Plots using gg_forest(). | Using Individual geom_ Functions. | Subgroup Analysis

Introduction | Global parameters of Ravages | Simulations based on allelic frequencies and GRR | Calculation of frequencies in each group of individuals | Simulation of genotypes | Simulations based on haplotypes | Power calculation

Introduction | Global parameters of Ravages | Example of analysis using LCT data | Defining genomic regions | Rare variant selection | Rare variant association tests | Genetic score for burden tests | CAST | WSS | Other genetic scores | Regressions | SKAT | RAVA-FIRST (RAre Variant Analysis using Functionally-InfoRmed STeps) | Data management

Introduction | Methods | Results | Discussion | Appendix: R code

Introduction | Overview | Key Features | Priority-Elastic Net | Example 1: Gaussian Family with Simulated Data | Example 2: Cox Family with Simulated Data | Example 3: Binomial Family with Simulated Glioma Data | Example 4: Multinomial Family with Simulated Data | Advanced Features | Block-wise Penalization | Handling Missing Data | Cross-Validation and Model Selection | Using the Shiny App for Threshold Optimization | Utility Functions | Extracting Coefficients | Making Predictions | Priority-Adaptive Elastic Net | Example 1: Gaussian Model | Example 2: Cox Model | Example 3: Binomial Model | Example 4: Multinomial Model | Conclusion

Introduction | TDA Built-in Visualizations of the Rips Diagram | Mercator Visualizations of the Underlying Data and Distance Matrix | Dimension Zero | Using iGraph | Community Structure | Visualizing Features | Significance | Zero-Cycles (Connected Components) | One-Cycles (Loops) | Two-Cycles (Voids)

Introduction | TDA Built-in Visualizations of the Rips Diagram | Mercator Visualizations of the Underlying Data and Distance Matrix | Dimension Zero | Using iGraph | Community Structure | Visualizing Features | Significance | Zero-Cycles (Connected Components) | One-Cycles (Loops) | Two-Cycles (Voids)

Introduction | TDA Built-in Visualizations of the Rips Diagram | Mercator Visualizations of the Underlying Data and Distance Matrix | Dimension Zero | Using iGraph | Community Structure | Visualizing Features

Overview | Data Input Format | Main Functions: Block Partitioning, Sire Imputation and Phasing of a Half-Sib Family | Auxiliary Functions | Parallel Data Analysis (para) | Visualisation | Diagnostic Tools | Pedigree Reconstruction | Imputation | Quick Guide | How to Cite the hsphase

SeqFeatR discovers feature - sequence associations | The core of SeqFeatR: Fisher's exact test | Graphical output | Input: sequences and features | Multiple comparison correction | Hints | Advanced Feature: Bayes Factor in SeqFeatR | Advanced Feature: Discovering associations between mutation tuples and features | Tartan plot: visual comparison of different associations of features and sequence position tuples

Introduction

Introduction | Loading the package | Sample data set | Preprocessing | Selecting variables and observations | Standardization of taxon names | Genus to species conversion | Data pooling | Biodiversity measures | Measures of species abundance | Total abundance | Abundance | Measures of species richness | Species richness | Margalef's index of diversity | Rygg's index of diversity | Hurlbert's $\mathrm{E}(S_n)$ | Measures of heterogeneity/evenness | Simpson's Measure of Concentration | Hurlbert's Probability of Interspecific Encounter (PIE) | Shannon's Index | Hill's Diversity Numbers | Measures of species sensitivity | AZTI Marine Biotic Index (AMBI) | Infaunal Trophic Index (ITI) | Calculating multiple biodiversity measures in one go | Advanced topics | Number of pool runs and species richness | References

Overview | Example data | Basic Archipelago plot | Using colour themes | Fully customised plot | Output files | Summary

Introduction | Study-level bias specification and prior simulation | Study-level posterior inference | Probability of exceeding a clinically or policy meaningful threshold | Evidence synthesis via posterior mixture models | Relationship to the Shiny application | References

License | Description | Installation | CRAN | Latest development version | Usage | A simple example | Use a real-world dataset from TCGA | Cancer-associated genes | Highlight genes of interest | Use an ExpressionSet object and add significance asterisks | Large dataset | Full map | Filtering | Filter by tree branch height | Filter by correlation threshold | Filter by noise level and hierarchy

Overview | Why Quantile-on-Quantile Regression? | The Original Application: Oil Prices and Stock Returns | Installation | Quick Start | Basic Usage | Summary Statistics | Visualization | 3D Surface Plot | Available Color Scales | Heatmaps | Contour Plot | Quantile Correlation | Detailed Example: Simulating Asymmetric Relationships | Working with Results | Extracting Results | Exporting Results | Customizing Quantile Grids | Methodology Details | The QQ Model | Estimation | Comparison with Standard Methods | OLS vs Quantile Regression vs QQ | When to Use QQ Regression | References | Session Info

Background and introduction | The network class | Using the network class | C-language API | Final comments

Goal GORIC(A) | GORIC(A) output | GORIC(A) values | GORIC(A) weights and ratios | Hypotheses sets | Interpretation output | General | One informative hypothesis | vs Complement | Example | vs Unconstrained | Multiple informative hypotheses | Complement as failsafe | Unconstrained as failsafe | No failsafe | Note: Hypothesis specification | Special cases | Equal fit | Overlapping hypotheses | Example: Subset true | Example: Non-overlapping part true | Follow-up exploratory analysis for non-overlapping part | Support for boundary | Example: $H_1$ versus its complement | Follow-up exploratory analysis for boundary | Example: $H_1$, $H_2$, and the unconstrained | Notes | Just below maxmimum fit | Equality restriction (=) | About-equality restrictions | Not highest fit | Example: Correct hypothesis | Example: Incorrect hypothesis | Note on sample size | Remarks Bayesian model selection | Example: Prior sensitivity bain | Example: Support for incorrect equality | N = 100 | N = 1000 | bain | GORIC | N = 10000

Introduction | GORIC(A) weights benchmarks | How to use benchmarks | Labelling | Use minimum effect | Sensitivity analysis | Defaults | General R code | Examples | Example 1 (ANOVA): $H_1$ vs its complement | Footnote | Example 2 (ANOVA): Overlapping hypotheses | Log-likelihood benchmarks | Example 3 (ANOVA): Border is true | Higher sample size | Example 1 (ANOVA) Ctd.

Packages | Example 1: Confirmatory Factor Analysis | Example 2: Multiple Group CFA | Measurement invariance | Hypothesis evaluation using GORICA | Example 3: Structural equation modeling (SEM) | Example 4: Multilevel SEM | Example 5: linear growth model with a time-varying covariate | Example 6: Mediation

Examples with R code | Example 1: Relationship previous experience and trust | Data preparation | Data for evSyn | Hypotheses | GORICA Evidence Synthesis | Example 2: Comparing surgical and non-surgical treatments for periodontal disease | Background Information & Data | Set 1 | Set 2 | Set 3 | Specifying sets of hypotheses in evSyn | Types of input for evSyn | using Estimates and Covariance Matrix | using GORIC(A) Values | using GORIC(A) Weights | Note | using Log-likelihood and Penalty Values

Overview | Example data set 1 | Input | Data files | Input options | Calculations and visualizations | Visualizing the sequences | EHH | EHHS | "Genome-wide" scan | Furcations and haplotype length | Example data set 2 | References

About the package | Background | Changes between versions 2.X and 3.X | Example files | Input files | R objects | Terminology | Overview | Data input | Haplotype data file | Marker information file | Loading data files: the function data2haplohh | Example 1: reading haplotype file in standard format | Example 2: reading haplotype file in transposed format (SHAPEIT2--like) | Example 3: reading haplotype file in fastPHASE output format | Example 4: reading vcf files | Example 5: reading ms output | Subset data | Computing EHH, EHHS and their "integrals" iHH and iES | Definition and computation | The (allele-specific) Extended Haplotype Homozygosity (EHH) | The integrated EHH (iHH) | The (site-specific) Extended Haplotype Homozygosity (EHHS) | The integrated EHHS (iES) | The function calc_ehh() | The function calc_ehhs() | The function scan_hh() | Computing iHS, Rsb and XP-EHH | The iHS within-population statistic | Definition | The function ihh2ihs() | The Rsb pairwise population statistic | Definition | The function ines2rsb() | The XP-EHH pairwise population statistic | Definition | The function ies2xpehh() | Delineating regions with "outliers": the function calc_candidate_regions() | Visualization of the statistics | Un-standardized iHS: the function freqbinplot() | Rsb vs. XP-EHH comparison | Distribution of standardized values: the function distribplot() | Genome wide score plots: the function manhattanplot() | Genome wide score plots: the function manhattan() of package qqman | Visualization of the haplotype structure | The function plot.haplohh() | The functions calc_furcation() and plot.furcation() | The functions calc_haplen() and plot.haplen() | Data considerations | Multi-allelic markers | Dealing with gaps | Dealing with missing data | Dealing with multiple markers at the same position | Dealing with unphased data | Dealing with unpolarized data | Differences to the program hapbin | About estimating homozygosity | References

Overview | Installation | Required files | Allele reference panels | The header-translation table | Configuration file | Step-by-step guide to run a QC | Step 1: make sure the package is installed correctly | Step 2: check R environment | Step 3: download the standard allele-frequency reference datasets | Step 4: get the header-translation table | Step 5: get the configuration file | Step 6: modify the parameters in the configuration file | Step 7: run the QC function | Test run

Introduction | Installing the Package | Analysis | Loading the Package | Input Data | Fitting Lactation Curve Models and Generating Ensembles | Available Weighting Methods | Visualizing Model Ranking | Plotting Actual vs. Predicted Daily Milk Yield | Customizing Model Selection | Evaluating Model Ranks | Customizing Model Parameters | Calculating Resilience Indicators | Imputation of missing records | Identification of milk loss events and estimation of resilience indicators

Overview | Background | Univariate Example | Using Distribution Functions | Multivariate Example

Conditional autoregressive (CAR) models | ICAR model | Proper CAR model | Disconnected graphs and isolated nodes | Scaling (Sørbye–Rue)

Overview | Step 1: Obtain road data for a city | Step 2: Clean, validate, and inspect geometries | Step 3: Write the dataset to GeoJSON (raw source) | Step 4A: Save as .rds (recommended for user projects) | Step 4B: Convert GeoJSON to .rda (recommended for R packages) | Step 5: Use the dataset in trafficCAR | Organizing multiple city datasets | Example directory layout (package development) | Example data-raw script | Using external GeoJSON files directly | Summary

Overview | Fetch by place name | Fetch by bounding box | Filtering the OSM query

Load data and build segment adjacency | Simulate an outcome and fit a CAR model | Prepare a plotting-ready fit object | Plot observed vs fitted values | Plot MCMC diagnostics | Plot spatial predictions | Tips for applied workflows

Purpose | CAR and ICAR precision matrices | A small graph example | Proper CAR | ICAR | Sampling from a sparse precision matrix | Zero-mean sampling | Nonzero mean (linear term) | Notes on ICAR sampling | A road-like graph example | Option 2: Synthetic road-like graph (fallback) | CAR/ICAR sampling on the road graph | Summary

Residual diagnostics | Moran’s I on residuals | Posterior predictive checks | Practical workflow | Limitations

Introduction | Load the package and example data | Build a road network graph | Spatial weights from the adjacency matrix | CAR and ICAR precision matrices | Sampling from a CAR precision matrix | Interactive road visualizations | Takeaways | Further directions

Space-Time Permutation example: NYC fever data | Poisson spatio-temporal example: New Mexico brain tumor data | Bernoulli purely spatial example: North Humberside leukemia and lymphoma

Overview | Description | Basic object | The cost function | The output function | Termination | Notes about optimization methods | Kelley's stagnation detection | O'Neill's factorial optimality test | Method of Spendley et al. | Method of Nelder and Mead | Box's complex algorithm | User-defined algorithm | User-defined termination | Specialized functions | fminsearch | Direct grid search | Examples | Example 1: Basic use | Example 2: Customized use | Example 3: Optimization with bound constraints | Example 4: Optimization with nonlinear inequality constraints | Example 5: Passing data to the cost function | Example 6: Direct grid search | Dependencies of fminsearch | References

Introduction | Preliminary notice | What's new? | How to obtain scaRabee | Installation and dependencies | Credits | Reporting bugs | Analysis types | Simulation | Estimation | Direct grid search | Getting started | Creation of a new analysis folder | Creation of new models in an on-going analysis | Editing of the data file | Editing of the parameter file | Editing of the model file | $ANALYSIS | $DERIVED | $IC | $SCALE | $LAGS | $ODE | $DDE | $OUTPUT | $VARIANCE | $SECONDARY | Editing of the master scaRabee script | Execution of the master scaRabee script | Scope of analysis | Design information | Solvers of differential equations | Implementation of dosing history for model defined with differential equations | Implementation of dosing history for model defined with algebraic equations | Analysis examples | Example 1: Simulation of a model defined with algebraic equations at the population level | Example 2: Simulation of inputs into a model defined with ordinary differential equations | Example 3: Simulation of a model defined with ordinary differential equations at the population level | Example 4: Simulation of a model defined with delay differential equations at the population level | Example 5: Estimation of a model defined with algebraic equations at the | Example 6: Simulation of a model defined with ordinary differential equations at the subject level | Example 7: Estimation of a model defined with algebraic equations at the subject level | Example 8: Direct grid search for a model defined with delay differential equations | Network of scaRabee functions | References

Overview | Motivating example: a clinical trial with multiple endpoints | A. one sample mean: primary endpoint | B. two sample comparison: secondary endpoint | C. binary clinical endpoint: safety outcome | D. continuous biomarker association | A unified evidential summary | Reporting multiple endpoints | Interpretation | Scope | References

Probability Weighted Iterative Generalised Least Squares for Two-level Multilevel Model and Two-level Multivariate Multilevel Model | 1. Introduction | 2. Estimation method for multilevel complex survey data | 2.1 PWIGLS estimation for two-level random coefficients models | 2.2 Estimation of fixed effects | 2.3 Estimation of Random effects | 2.4 Initial estimates | 2.5 Variances | 2.6 Residuals | 2.7 Scaled weights | 3. PWIGLS Estimation for multivariate multilevel models | 4. Functions | 5. References

Load Package | Generalized True Random Effects Model | GTRE Results

Background | Installation | System requirements | Running the code | Downloading all the PubChem we need | note that running this line of code may occupy your R session for 2-3 few hours | Building metabolite (CID) - lowest common ancestor (LCA) relationships | note that running this line of code may will take about an hour of computer time, with 8 threads, 64 GB RAM | Creating PubChem BIO metabolome database. | note that running this line of code may will take about one hour of computer time, with 8 threads, 64 GB RAM | Creating taxon-specific metabolite databases | running this line of code may will take about a minute of computer time, with 8 threads, 64 GB RAM | Meta-metabolomes | Taxon-scored metabolite database structure | Filtering your database. | Exporting your custom database | The future | Workflow in brief

Description | Installation | System requirements | Install and load | Quick start | 1) Calibrate the tuning parameters for eNAP prior | 2) Construct NAP priors | NAP | mNAP with a fixed weight of 0.5 | eNAP without direct data $D_{E,C_2}$ provided | eNAP with direct data $D_{E,C_2}$ provided | 3) Simulate operating characteristics | 4) Calculate posterior | Core functions | JAGS model specification | Tips & diagnostics | Citing | Maintainer

Introduction | Safety data set | Original Format | ADaM format | Calculate Summary Statistics | Original Data Format | ADaM data format | Convert to XML | Output | Manual Upload

About mrangr | Basic workflow | Installing the package | Input maps | Species interactions | Community initialisation | Running the simulation | Visualisation | Over space | Over time | Virtual ecologist | Summary

Motivation | Basic usage | Alternative combinations of inputs | Infeasible systems | Discussion | References

Introduction and overview | What problem does S3VS solve? | Model families supported | S3VS in one picture | Use of package functions with examples | Installation | The main function S3VS() | Example 1: Linear model | Example 2: Binary classification model | Example 3: Survival model | Advanced usage: building blocks and customization | Choosing leading variables: get_leadvars*() | Constructing leading sets: get_leadsets() | Selection within a set: VS_method*() | Aggregating selections: select_vars() | Removing variables: remove_vars() | Response updating: update_y*() | Stopping rule: looprun() | Practical guidance and troubleshooting | Choosing method_sel and method_rem | Highly correlated predictors: | Computational tips for AFT: | Reproducibility

Load packages | Prepare data | Train a recforest model | Predict on new data

Introduction | Installation (GitHub) | Load the package | Example

Overview | Example

The Name of the Game | Representations | Trying it Out | Using GMP Rational Arithmetic | Linear Programming | Redundant Row Elimination | Faces | Image and Preimage

Introduction | Maximum entropy bootstrap | Applications | Concluding remarks

Overview | Distance Metrics | Example

Introduction | Installation | Retrieving data structure from GPML file | Edges | Nodes | Groups | Anchors | Converting Pathways into igraph objects | Bundling the Process | Finding cycles

Introduction | Running the Pipeline | Running the Full Pipeline | Analyzing your results | Additional Options

Overview | Load OmicNetR | 1. Generate example data | 2. Align samples | 3. Fit sparse CCA | 4. Convert loadings to an interaction network | 5. Visualize results | Bipartite network | Feature importance circle plot | Correlation heatmap | Notes for CRAN

Generalized Bayesian Optimal Phase II Design (G-BOP2) | 1. Installation | 2. Summary of functions | 3. Study Objective: sample size minimization | 4. Study Objective: power maximization | 5. Reference

Overview | 1. Load the example dataset | 2. Build the binary evidence matrix | 3. Run quantbayes core | 4. Evidence sufficiency plots | Global posterior distribution | Overlay of top candidate densities | Evidence matrix | Observed evidence proportions | Posterior credible intervals | Combined panel | 5. Highlighting variants of interest | 6. File based input | 7. Saving output tables | 8. Saving plots | 9. Customising plot themes | 10. Flagship overlay plot | Core results example | Clinical genetics example | Report text (printed automatically) | Summary

Binary Classifier | Multiclass-classifier

Overview | Prerequisite | Inputs | Required information for running revert | BAM file preparation | Outputs | Examples | Acknowledgements

Motivation | A glimpse on Multi-Stage Clonal Expansion Models | General cautions on fitting incidence data with Multi-Stage Clonal Expansion Models | A fitting approach | References

Introduction | Function Dependencies | Part 1: Generating Decision Tables | Creating Design Objects | Generating Decision Tables for Individual Designs | Comparing Decision Tables Across Designs | Saving Decision Table Plots | Other Designs | Part 2: Running Simulations | Setting Up Simulations | Running Simulations for Individual Designs | Multiple Scenario Simulations | Using Pre-defined Scenarios | Comparing Operating Characteristics | Saving Operating Characteristic Plots | Modifying Simulation Parameters

ODBC Concepts | Basic Usage | Writing to a Database | Data types | Schemas and Catalogs | Internationalization Issues | Excel Drivers | DBMS-specific tidbits | Installation | Specifying DSNs | Internals

0. Introduction to gpyramid package | 1. Set up | 2. Prepare data | 2.1 Gene data | 2.2 Position data | 2.3 Preprosessing | Generate haplotype dataframe from row data | Generate recombination probability matrix from raw data | 3. Find parent sets from candidate lines (cultivars) | 4. Calculate the number of necessary individuals and generations | 4.1 Fig 4a (Servin et al., 2004) | 4.2 Fig 4b (Servin et al., 2004) | 4.3 Fig 4c (Servin et al., 2004)

1. Set up | 2. Prepare data | 2.1 Gene data | 2.2 Position data | 2.3 Preprosessing | Generate haplotype dataframe from row data | Generate recombination probability matrix from raw data | 3. Find parent sets from candidate lines (cultivars) | 4. Calculate the number of necessary individuals and generations | 4.1 Calculate cost of all the crossing schemes | 4.2 Plot cost of each crossing scheme | 4.3 Select the most cost-effective crossing strategy | 4.4 Another example

Introduction | Loading data | Preparation of functions inputs | InputsModel object | RunOptions object | InputsCrit object | CalibOptions object | Criteria | Calibration | Control | Simulation | Simulation run | Results preview | Efficiency criterion | Features diagram | References

Introduction | Scope | Standard Least Squares (SLS) Bayesian inference | MCMC algorithm for Bayesian inference | Estimation of the best-fit parameters as a starting point | Running 3 chains for convergence assessment | MCMC diagnostics and visualisation tools | Exploring further possibilities

Introduction | Scope | Definition of the necessary function | Local optimization | Global optimization | Differential Evolution | Particle Swarm | MA-LS-Chains | Results | Multiobjective optimization | caRamel

Introduction | Scope | Model description | Calibration of the upstream subcatchment | Calibration of the downstream subcatchment | Creation of the InputsModel objects | Calibration with upstream flow observations | Calibration with upstream simulated flow | Calibration with upstream simulated flow and parameter regularisation | Discussion | Identification of Velocity parameter | Value of the performance criteria with theoretical calibration | Parameters and performance of each subcatchment for all calibrations | References

URL's for examples | the mke_county polygon is available as an exported object in arcpullr | Querying Spatially via ArcGIS Feature Service | Querying via SQL | Raster Layers | Plotting Layers

URL's for examples | WI Landcover Type URL | WI Leaf-off Aerial Imagery URL | the wis_poly polygon is available as an exported object in arcpullr | Image Layers

Load Packages | Prepare data | Compute power values through Bootstrapping | Instead of using default Wilcoxon test | PoweREST_gene | PoweREST_subset | Fit the power surface using smoothing splines | Visualize the power surface | Create interactive visualization result | Fit local power surface with XGBoost | Visualize the local power surface

Introduction | Quick example: Efficacy and acceptability of 12 Antidepressants | Input dataset | Two types of layouts: row-column and upper-left lower-right | SUCRA values | Save the plot | Plotting parameters | Different types of outcomes | Ordering treatments | Controlling symbols | Changing plate (circle) size | Changing text size | Abbreviating long treatment names | Changing colors to indicate treatment rankings | Changing colors for texts and plate circles | Changing background colors for upper and lower diagonal parts | Adding the title | Additional examples | NMA results with missing values | Large NMA results (number of treatments is 22)

Chick Weights | UCB Admissions | References

Chick Weights | Ornstein Data | Multi-panel Display: UCBAdmissions

Chick Weights | Ornstein Data

statisR Package version 1.0 | Oldemar Rodríguez R. | Installing the package | CRAN | STATIS Method | How to read a Table from a CSV file? | Principal Functions | statis | plot.statis.circle | plot.statis.plane | Example 1: Article on Wine Evaluation by Experts | Apply statis without any selection and save the result | Plot Correlation Circle of all the tables | Plot Correlation Circle of all variables evolution | Plot Principal Plane of Average Individuals | Plot Principal Plane of the Evolution of Individuals | Apply statis with specific selections and save the result | Selecting tables 1 and 3 | Selecting rows 1 and 5 | Selecting rows 3 and 4 | Selecting tables (1,3) and rows (1,4) | Example 2: Tarcoles River Basin of Costa Rica | Read csv files and load data | Statis without selections | Selecting only table 2 | Selecting only row 3 | Selecting table 2 and row 3 | STATIS-DUAL Method | statis.dual | plot.statis.dual.circle | plot.statis.dual.trajectories | select.super.variables | Example 1: Sugarcane in Costa Rica | Read csv files and apply STATIS DUAL | Use plot.statis.dual.circle to get the Interstructure graph | Use plot.statis.dual.circle to get the Correlation Circle for all variables | Use plot.statis.dual.circle to get the Correlation Circle for the selected variables | Use plot.statis.dual.trajectories to get the trajectories graph for the selected variables | Use plot.statis.dual.trajectories to get the trajectories graph for all variables | Example 2: airquality (base R) ⇒ K = 5 = months | Data | Apply STATIS DUAL | Interstructure graph by Month | Correlation Circle for all variables | Correlation Circle with Selected Variables | Variable Trajectories by Month | Trajectory of All Variables

Introduction | Computation of moments | The trunmnt package | Summary

Introduction | Installation instructions | Food composition datasets | Dietary assessment tools | Preparing the input | Nutrient calculator | Visualization tools | Generate plot | Adjust font size | Shiny app | References

Overview | Installation | Input Preparation | Step by step guide to running the package | Annotation steps of the top GWAS variants | Package functions: | demo_annotation() | getConfigFile() | run_annotation() | findProxy() | findPairwiseLD() | findGenomicPos() | findRSID() | run_stringdb_annotation()

Installation | Citation | Example | 1. Preprocessing | 2. Analysis

1. Select a wrapper | 2. Check arguments | 2.1. Code string | Package name | Data name | Target variable(s) | Level variable(s) | 2.2. Input data | 2.3. Test results or named list results | 3. Create an instance | 4. Modify the instance | 5. Include the instance into the overarching data_analysis instance | 6. Write JSON-LD | Example I: group comparison | Example II: algorithm evaluation | Example III: an all-in-one wrapper for anova

1. Load a DTR schema | 2. Create an instance | Fields | Most common types of input | Strings | Numeric | Data frames and tuples | More than one input in a field | Nested structure | General remarks on writing an instance | 3. Convert the instance into JSON-LD format | 4. Example: reporting data analysis with dtreg | Choose parts of analysis to address and schemata to load | Prepare data frames with test results | Find out which schemata parts can be reused | Write all instances with the information specified above | Write the data_analysis instance and convert into JSON-LD format | Additional comments

Reference | Shiny | Note

Load required packages for the vignette | We explicitly reference fmi:: functions for clarity | Apply a mean_shift of 1.0 to FPC 1 for Group B | Check data structure

Unscaled version | Scaled version | Unscaled version with nutrient dynamic

ACKNOWLEDGEMENT | INTRODUCTION | MODEL | EXAMPLE | REFERENCES

1. System requirements | 2. Overview of CSTools structure | 3. Data storage recommendations | 4. CST_Load example | 5. CST_Start example | Managing big datasets and memory issues

Weather regime analysis | 1- Required packages | 2- Retrive data from files | 3- Daily anomalies based on a smoothed climatology | 4- Weather regimes in observations | 5- Visualisation of the observed weather regimes | 6- Visualisation of the observed regime persistence | 7- Weather regimes in the predictions | 8- Visualisation of the predicted weather regimes

Overview | Installation

Overview of the data instances | Overview of the data parameters | Basic instantiation | Advanced instantiation | Digging into the models | Tumor model | Sampling model | Sequencing noise model | Summary

Overview | Background | Univariate Example | Using Distribution Functions | Multivariate Example | Special Cases | Approximation Quality | References

Overview | Installation and loading | Gene list functional enrichment analysis with gost | Enrichment analysis | Multiple queries | Visualization | Data sources and versions | Custom data sources with upload_GMT_file | Creating a Generic Enrichment Map (GEM) file for EnrichmentMap | Gene identifier conversion with gconvert | Mapping homologous genes across related organisms with gorth | SNP identifier conversion to gene name with gsnpense | Accessing archived versions or the beta release with set_base_url | Supported organisms and identifier namespaces | Citation | Need help? | References

License | R | Introduction | Examples

Overview | GHRmodel formula helper functions | 0. Prepare data | Load libraries | Data pre-processing | Spatial data and graphs | Pre-process covariates | Lagged covariates | Define priors | Example 1: GHRmodel helper functions | 1. Model development | Select variables | Linear covariates | Non-linear covariates | Non-linear covariates replicated by group | Covariates for multivariate models | Add a covariate to all covariate lists | Interacting covariates | Varying covariates | Varying vs. Replicated Effects in INLA | Write INLA-compatible model formulas | 2. Model fitting | 3. Model evaluation | Interaction effects | Varying linear coefficients | Replicated nonlinear coefficients | Example 2: INLA-compatible formulas | References

Overview | Example: DLNMs in GHRmodel | 0. Prepare data | Load libraries | Data pre-processing | Spatial data and graphs | 1. Model development | One-dimensional basis matrix | Cross-basis matrix | Model formulas including DLNM terms | 2. Fit DLNMs with INLA | 3. DLNM output | One-basis terms | Cross-basis terms | References

Overview | Installation | Data requirements | Methodology | GHRmodel structure | 1. Model development | 2. Model fitting | 3. Model evaluation | GHRmodel workflow | 0. Data | Dataset description | Data pre-processing | Spatial data and graphs | Create lagged covariates | Write covariates | Write formulas | Rank models | Posterior predictive checks | Goodness-of-fit metrics | Fitted vs. Observed | Covariate effects | Evaluate random effects | 4. Iterative model selection | Subset models | Extract covariates | Add an additional covariate | Fit new models | Combine models | Evaluate combined models | References

Introduction | Input Data | Model Estimation and Presentation of Results | Default Settings | Advanced Settings | Computation of Confidence Intervals | Estimation using Two-Parameter Box-Cox Transformation | Print and getRI Function Arguments | Plot Function Arguments | References

Introduction | Description of VeRUS | Application of VeRUS | Possible Inputs of the verifyRI Function | Interpretation of the Results | Quantification of the Similarity of Two Reference Intervals | Advanced Applications | Invisible Output of the verifyRI Function | Non-Default Parameters to Define Uncertainty Margins | Customization of the Plot | Advanced Parameters for getRISimilarity | Alternative Verification Method | References

Introduction to EpidigiR: Epidemiological Analysis and Visualization | Setup | Datasets | Examples | Summary Statistics | SIR Epidemic Model | Spatial map | Logistic Model | Random Forest with Clinical Data | Global Health Burden (DALY) | SNP Association | Age Standardization | Machine-learning-logistic | Survival Analysis | NLP-keyword Extraction | K-means Clustering | SVM-Modelling | Diagnostic Tests | boxplot-visualization | Scatter-visualization | Conclusion | License

Overview | Data requirements | Covariates, case counts, and incidence rates | Spatial and temporal aggregations | Color palettes | Plot types | Time series | Dual-axis time series | Heatmap | Seasonality | Map | Bivariate | Correlation | Compare

Introduction | Basic Usage | Computing GMC(Y|X) | Computing GMC(X|Y) | Feature Ranking | References

Overview | Example use case: Cluster sampling for a proportion

1 Introduction | 2 Yield Table Representation in ForestElementsR | 2.1 Metadata | 2.2 Actual Yield Table Data | 3 Visualizing Yield Table Contents | 4 Practical Work With Yield Tables in ForestElementsR | 4.1 Site Indexing | 4.2 Extracting Values From Yield Tables | 5 Importing Yield Table Data and Make them fe_yield_table Objects | References

Before starting | Installing cancerradarr Package | Creating the Cancer RADAR Input File | Entering Data in the Input File | Computing Cancer RADAR Summary Statistics | Visualizing the Summary Statistics | Sending Cancer RADAR Summary Statistics

Installation: | Steps | Step 1: Prepare LDSC input-format data for multiple traits. | Step 2: Compute pleiotropic heritability with bias correction | Leave-category-out analysis instruction

Introduction | Installation | Quick Start Example | Creating Example Data | Running FracFixR | Understanding the Output | Visualizing Fraction Proportions | Differential Proportion Testing | Interpreting Results | Creating a Volcano Plot | Data Requirements | Session Info | References

License | R | The Delta Method: Old Way and New Way | Example I: No Random Effects | Example II: With Random Effects | Summary

Basic example with FAMD | Differences between mcacontour and famdcontour | Dataset with both input class and interval variables

Comparing algorithms | Tuning representation | Real dataset fitting under different algorithms

Outliers with respect to plot dimensions | Outliers with respect to model fit

Using external predictions | Colors and titles | Plotting over selected dimensions

1. Package loading | 2. Data | 3. Estimate model parameters | 4. Plot time-series model and measured CH data | 5. Time-series model | 6. Reference

Installation | Dataset | BICAM Output

Covariance Structures | Example | 1. Unstructured | 2. Exponential Decay | 3. Tree | 4. Scaled Tree | 5. Multi-Level Tree

Introduction | Methodology | The FARS package | Illustration of FARS package functionalities | Summary and discussion

Introduction | Ada-Plot | Example 1 | Example 2 | Example 3 | Uda-Plot | Example 4 | Example 5 | Example 6 | Robust version of Uda-Plot | Example 7 | Example 8 | Reference

Introduction | Example Dataset

1. Introduction | 2. Setup | Installing and loading the lab2clean package | 3. Function 1: Clean and Standardize results | 4. Function 2: Validate results | 4. Function 3: Clean and Standardize units of measurement: | 5. Function 4: Harmonize results to reference units | 6. Customization

R Markdown

R Markdown | Including Plots

Using the fdesigns package | Examples | FLM with one profile factor | FLM with one profile factor and roughness penalty | FLM with one profile factor and three scalar factors | FGLM with one profile factor and quadrature approximation | FGLM with one profile factor depending on main effect and MC approximation

Framing the Problem: A Statistical Approach | Framing the Solution: Using SampStop as guidance for stopping sampling | Example | Conclusion

Setting the working directory to where we have the Models folder if needed | Importing an existing model from a .cmpx file | Creating an empty csv file template with all the networks and nodes in the model | The dataset csv is manually prepared and filled in outside the R environment | Creating batch cases, this function creates a new .json model file in the working directory with dataSets representing all the rows in the input data | It is possible to import the new model file back to R to work with it | Now model_with_cases is an R model object containing both the dataSets already existing in the model and a new dataSet for each row in the input data and it is ready to be used for calculation purposes | Running the local API batch calculate function to update the model object with all the results

Setting the working directory to where we have the Models folder if needed | Importing an existing model from a .cmpx file | Defining the dataSet of interest ("mercedes"), and finding the relevant dataSet object in the model | Assigning certain nodes in the network so that it is easier to manipulate them (such as entering new observation) | Entering new observations to the selected nodes | Login to the agena.ai cloud servers and calculating the model (with correct credentials) | Accessing and displaying information which now is included in the model object

Setting the working directory to where we have the Models folder if needed | Importing an existing model from a .cmpx file | Defining the dataSet of interest ("mercedes"), and finding the relevant dataSet object in the model | Assigning certain nodes in the network so that it is easier to manipulate them (such as entering new observation) | Entering new observations to the selected nodes | Calculating the model using local agena.ai developer API | Accessing and displaying information which now is included in the model object

Setting the working directory to where we have the Models folder if needed | Importing an existing model from a .cmpx file | Creating a new dataSet in the model for sensitivity analysis | Creating a sensitivity analysis config object which uses all nodes for sensitivity analysis on the node total_cost, to calculate mean and variance results | Login to the agena.ai cloud servers and running the sensitivity analysis

Setting the working directory to where we have the Models folder if needed | Importing an existing model from a .cmpx file | Creating a new dataSet in the model for sensitivity analysis | Creating a sensitivity analysis config object which uses all nodes for sensitivity analysis on the node total_cost, to calculate mean and variance results | Running the sensitivity analysis using local agena.ai developer API

Setting the working directory where the local api will be cloned and installed if needed | Creating the local agena.ai developer API environment. Requires git, Java, and maven.

Overview | Quick Start | hdsvm() | cv.hdsvm() | nc.hdsvm() | cv.nc.hdsvm() | Methods

Introduction | Getting Started | Clustering Sequences | Recoding | Clusters | Aligning Clustered Sequences | Alignments By Cluster | Consensus Sequences | De Bruijn Graphs | Appendix

Analysis of the Minimum Doscriminant Information Statistic (mdis) | Data | The Minimum Discrinant Information Statistic | Symmetry | Marginal Homogeneity | Quasi-symmetry

Checking Whether the Margins are Ordered | Testing Marginal Homogeneity | Using Cliff's d-test | Clayton's Marginal Location Test | Agresti's Mann-Whitney Test | Agresti's weighted difference: Agresti_w_diff() | McCullagh's Logistic Model: McCullagh_logistic_model() | Conclusions

Null Model | Uniform Association Model | Rows and Columns as Special Cases of Model I | Decomposing the Association | Model II | Reference

What's Unique about Rater Agreement? | The Data | The basic main effects model | Regular Log-linear Models | Main Effects Model | The Weight by Response Category model | Unequal Weights on the Diagonal | Agresti's Model for Ordinal Agreeement | References

Tests for Square Tables | Data | Symmetry | Mariginal Homogenity | Quasi-Symmetry | Variations of Quasi-Symmetry

Overview | Theoretical Background | Derivation of Decision Boundaries | Why these thresholds? | Example 1: Binomial Data

MCMC through the function xdnuts | Set algorithm's features using the set_parameters function | Model diagnostic through the print, summary and plot functions | print | summary | plot | Posterior sampling post-processing through the xdtransform, xdextract functions | xdtransform | xdextract | Practical examples | Example with both continuous and discontinuous components | Example with only discontinuous components | Example with only continuous components | Bibliography

########################################################################### | Getting started | Installation | Dataset | Data integrity | Basic pipeline | Prepare | #--------------------------------------------------------------------------- | Create | Quality | Statistics | Subgroups | Compare | Preprocessing and adjustment | Multiple datasets | Large datasets and maps | Balancing sample counts | Terminology | Build information

Introduction | Exponential factor copula model | Data Preparation | Create Spatial Data Objects | Model Fitting | Model Summary and Diagnostics | Goodness-of-Fit | Simulation from the Fitted Model | Summary | References

Introduction | Demonstration | Advanced Examples | Using Named Columns and ELSE Clause | Working with Numeric Data and Auto-Quoting | Creating IN Lists with Duplicate Removal | Bulk UPDATE Statements | Sample Data | Function Reference | casewhen() - CASE Statement Generator | inlist() - IN Statement Generator | updatetable() - UPDATE Statement Generator | Common Parameters Explained | Installation | Acknowledgments | Citation and License

Optional Step 0: | Step 1: read in vcf file as 'vcfR' object | Step 2: quality filtering | Note: | Step 3: set missing data per sample cutoff | Set arbitrary cutoff for missing data allowed per sample. | Step 4: set missing data per SNP cutoff | Set arbitrary cutoff for missing data allowed per SNP. | Step 5: quality unaware filters | Step 6: write out files for downstream analysis

Basic use | Heart data | Exploration using basic functions | Dual categorical variables | Comparisons involving continuous variables | All variables at once | Customizing AssocBin | Stop criteria | Splitting functions | Customizing plots | Drawing conclusions about data from AssocBin

Introduction | RCAG-EP Test | RCAG-DD Test | Real World Example | References

Introduction | RIG-EP Test | RIG-DD Test | Real World Example | References

Introduction | Launching CALMs | 1. Web Access (No Installation Required) | 2. Local Access (Installation Required) | A. Open R or RStudio | B. Install the CALMs Package | C. Run the CALMs Package | Built-in Dataset | Providing Your Own Data | Features Overview | Example Workflow | Step 1: Load Data | Step 2: View Data | Step 3: Check Group Equivalency | Step 4: Propensity Score Analysis Setup | Step 5: Propensity Score Analysis | Step 6: Measuremenent Invariance Tests | Step 7: Metric Invariance Tests | Step 8: Scalar Invariance Tests | Step 9: Structural Invariance Tests | References

1. Introduction | 2. Installation | 3. Running ZicoSeq | 3.1 Loading the example data | 3.2 Running ZicoSeq function | 3.3 Some notes on using ZicoSeq | 3.3.1 Winsorization | 3.3.2 Posterior sampling | 3.3.3 Omnibus testing | 3.3.4 Reference selection | 3.3.5. Error control | 3.4 ZicoSeq output | 3.4.1 p-values | 3.4.2 R^2 (percentage of variance explained) | 3.5 ZicoSeq output visualization | 3.6 More examples | 3.6.1 Proportion data | 3.6.2 General data types | 3.6.3 Random effect model - within-subject comparisons | 4. References | 5. Session info

Introduction | Loading and using the software | Modelling faecal egg count reduction | Example data analysis | Discussion

The "compositions" package | Compositional classes, old and new | Subsetting and transformations | Compositions as columns | Embedding in S4 objects

The Package | Installing glmnetr | Data requirements | An example data set | Performance of cross validation (CV) informed relaxed lasso model | The CV informed relaxed lasso model fit | Nested cross validation (NCV) for multiple models | Further model assessment | Model comparisons with simulated and observed data | Model replicability and model comparisons | Bootstrap assessment of model performance | References

The Package | An Example Analysis | Linear Calibration | A First Visual | Calibration Using Spline Fits and Resampling | A Binomial Model | A Binomial Model Calibrated Using Bootstrap | Bootstrap Calibration for a Random Forest | A Normal (Gaussian Errors) Model | Perspective | References

Introduction | An example dataset | Fitting an Elastic Net model | A tabular summary of model performances | A graphical summary of model performances | Graphical presentations of cross validation deviances | Graphical presentations of beta estimates | Numerical values for beta and predicteds

The matter | An example dataset | Impact of repeats in the predicteds

The functions | Installing glmnetr | Data requirements | An example dataset | Working with torch tensors | Performing calibrations with the neural network models | Transfer learning from linear models to neural network models | Comparison of lasso and neural network models | Calibration plots based upon hold out data | Internal implementaiton of the ``transfer learning'' | Further extensions of ``transfer learning'' | Transfer learning and the gradient boosting machine

The Package | Data requirements | An example dataset | Cross validation (CV) informed stepwise model fit | Nested cross validation

Introduction | Analyzing bullet hole data | Additional functionality | References

What is the SymLink Tool? | Give Me a High-Level Overview | Is This for Me and My Team? | Give Me a Little More Detail | Demonstration | Set Your Output Folder | Make the Symlink Tool | New Folder | Produce Model Results | Mark Best | New Pipeline Runs | Mark New Best | Mark Keep | Mark Remove | Delete Folders | Reports

What is the SymLink Tool? | Assumptions | SymLink Tool Intro | SymLink Tool Use | Mark Best | Inspect Logs | Reports | Create New Folders | Unmark | Deletion | Mark remove | Delete the Folder | Other Features | Mark Keep | Reports pt 2 | Roundups | roundup_remove | roundup_by_date | Make new log | Internal State | Clean Up

Regression analysis using the individual participant data (IPD) | Pool the estimated spline parameters based on the IPD with literature-based (LB) estimates | Plot the IPD, LB and pooled spline estimates

Introduction | Installation | Quick start | 1. Import 10x output into VDJ dataframe with the VDJ_build() function | Summary | Parameters | Example | 2. Construct lineage trees in repertoire-wide manner with the Af_build() function | Examples | 1. Default | 2. ML | 3. IgPhyML | 3. Visualize lineage trees with the Af_plot_tree() function | Maximum Likelihood | With internal nodes | Without internal nodes | 4. Compare tree construction methods with the Af_compare_methods() function | Summary | Parameters | Af_compare_methods() | 5. Quantify evolution within repertoires with the Af_compare_within_repertoires() function | Af_metrics() | Af_compare_within_repertoires() | Af_cluster_metrics() | Af_cluster_node_features() | Af_distance_boxplot() | 1. Euclidean distance | 2. Laplacian Spectral Density | 6. Compare antibody lineage trees across repertoires with the Af_compare_across_repertoires() function | Af_compare_across_repertoires() | Boxplot | 7. Assessing evolutionary likelihood of somatic hypermutation with Protein Language Models (PLM) | Af_PLM_dataframe() | Af_plot_PLM() | Example1 | Example2 | Substitution Rank | 8. Investigate the evolution of antibody 3D structure along the lineage trees | VDJ_3d_properties() | Binding residues Antibody-Antigen | Full antibody | i. Run IgPhyML on VDJ dataframe and import IgPhyML trees into AntibodyForests object | j. Integrate bulk RNA-seq sequences | Contributions

Introduction | 1. Viewing Available Palettes | 2. Usage with ggplot2 | 3. Plotting Trees and Cladograms | Phylogenetic Tree | Cladogram | 4. Generating Custom Palettes

Prepare your data | add_var() | Usage | norm | add_summary() | add_overall | continuous_format | norm_continuous_format | unnorm_continuous_format | categorical_format | binary_show | add_p() | asterisk | add_method | add_statistic_name | add_statistic_value

Age-Period (AP) data | Data preparation | Data supplied as a data-frame | Data supplied as a 3D data array | Data supplied as a data matrix | Running the model | Forecast | Analyzing the output | Convergence diagnostics | Age-period-product (APP) data | Plot the output | Appendix A: full list of age-period models considered | Appendix B: full list of age-period-product models considered | References

Introduction | How to describe and interpret a categorical variable automatically? | When the categorical variable is explicit | When the categorical variable is latent

Installation | Basic Usage | 1. Loading the package | 2. Retrieving data for specific species | 3. Understanding the returned data | 4. Formatting data with specialized functions | Synonyms | Distribution information | Higher taxonomic classifications | Subspecies information | Common names | Reproduction information | Type specimens | Diagnosis information | Comments | Etymology | References | Handling Special Cases | 1. Trinomial names (subspecies) | 2. Incomplete species names | Conclusion

Introduction | Main Functions for Local Searches | Practical Examples | Exact Search for Species Names | Partial Match Search | Flexible Search with Additional Options | Obtaining Subspecies Information | Recommended Workflow | Conclusions

Overview of Functions | Using format_all_attributes() | Syntax | Parameters | Return Value | Example | Using format_selected_attributes() | Working with the Formatted Data | Data Structure | Tips for Effective Use

Introduction | Installation of R | Installation of RStudio | Installation of litteR | Updating litteR | Using litteR

Introduction | Loading the litteR-package | User interface | Create a new project | Perform litter analysis | Input | Type file | Data file | Settings file | Data Quality Control | Output | Report | Settings | Outlier analysis | Descriptive statistics | Regional descriptive statistics | Trend analysis | Regional trend analysis | Statistical summary file | Log-file | Troubleshooting | References | Appendix

Vignette Accompanying "Justify Your Alpha: A Primer on Two Practical Approaches" | Installation | Minimizing Error Rates | Balancing Error Rates | Relative costs of Type 1 and Type 2 errors | Prior probabilities of H0 and H1 | Sample Size Justification | Specifying power functions | Avoiding the Lindley Paradox

wintime Package Guide | Installation | Load Required Packages | Example Data | wintime Function | Win time ratio (wtr) | Restricted win time ratio (rwtr) | Pairwise win time (pwt) | Expected win time against reference (ewtr) | Expected win time (ewt) | EWTR-composite max test (max) | Restricted mean survival in favor of treatment (rmt) | Resampling | ewt with 10 Resamples | wtr with 5 Resamples | Resampleing Cont. | Survival Models | Model Functions | Parameters | markov Function | km Function

Introduction | General Framework | Data generating equation and the seed | Repro samples | Exchangeable statistic $T$ | p-values and confidence sets | Main Functions | p_value | Inputs | Returns | get_CI | Inputs (only ones that have not been mentioned before) | confidence_grid | grid_projection | plot_grid | Example Workflows | Regular Normal | Regular Normal: The 'p_value' function | Regular Normal: The 'get_CI' function | Regular Normal: The 'confidence_grid' function | Regular Normal: The 'plot_grid' function | The 'grid_projection' function | DP Normal | DP Normal: The 'p_value' function | DP Normal: The 'get_CI' function | DP Normal: The 'confidence_grid' function | DP Normal: The 'plot_grid' function | References

Outline | Part I: Basic Usage | Starting Estimation | Resuming Earlier Estimation | Using Built-in Stopping Times | Computing Lower Confidence Bounds | Inspecting Output | Part II: Advanced Usage | Sampling Batches | Resuming Based on Earlier Printed Results | Custom Stopping Times

Introduction | Accessing Input Files | Strip the sequence | Extract modifications information | Match peptide sequence with provided sequence and calculate positions | Quantify matched peptide sequences | Plotting peptide in whole provided sequence | Launching the Shiny App

Package background | References: | What can be inferred from the survcompare results? | Why the CoxPH-SRF ensemble and not just SRF? | Package installation | Examples | Example 1. Linear data | Example 2. Non-linear data with interaction terms | Example 3. Applying survcompare to GBSG data

Preparing the data | Calculating fluxes | From fluxes to function | Sensitivity to input parameters

Overview | Functions | PALM()

Overview | Helper Functions for Reports | Mock Data for Demonstration | Complete Quarto Document Template | Basic Template Structure | Example Report Sections | Dose Selection Visualization | Risk-Benefit Analysis | Parameterized Reports | Setting Up Parameters | Batch Report Generation | Conditional Content | Best Practices for Quarto Reports | 1. Document Structure | 2. Chunk Management | 3. Reproducible Workflows | 4. Output Formatting | Template Library | Clinical Protocol Report Template | Regulatory Submission Template | Troubleshooting Common Issues | Package Availability Issues | Memory and Performance Issues | Conclusion

Overview | Key Features | Mock Data Setup | Helper Functions for Table Creation | Basic Table Creation | Operating Characteristics Table | Design Parameters Table | Custom Table Styling | Enhanced Operating Characteristics Table | Professional Clinical Table | Scenario Comparison | Multiple Design Comparison | Advanced Formatting | Design Parameters with Categories | Saving Tables | Export Options | Integration Tips | Best Practices for Clinical Tables | Troubleshooting | Common Issues and Solutions | Conclusion

Overview | Key Approach | Basic Workflow | Step 1: Run Simulation | Step 2: Extract and Format Data | Manual Data Extraction | Working with Different Design Types | Custom Analysis Examples | Dose Selection Analysis | Safety Analysis | Efficacy-Toxicity Trade-off | Creating Visualizations | Creating Summary Tables | Operating Characteristics Table | Design Parameters Table | Enhanced Summary Output | Exporting Data | Best Practices | 1. Consistent Workflow | 2. Data Validation | 3. Reproducible Analysis | 4. Custom Utility Functions | Advanced Analysis Examples | Sensitivity Analysis | Comparative Analysis Framework | Conclusion

Installation | Spatially-resolved expression of triple negative breast cancer tumor biopsies | Creating an STList (Spatial Transcriptomics List) | Exploring variation between spatial arrays | Transformation of spatially-resolved transcriptomics data | Visualization of gene expression from spatially-resolved transcriptomics data | Spatial interpolation of gene expression | Unsupervised spatially-informed clustering (STclust) | Association between spatial heterogeneity and sample-level variables | References

RSDA Package version 3.3 | Oldemar Rodríguez R. | Installing the package | CRAN | Github | How to read a Symbolic Table from a CSV file with RSDA? | Symbolic Data Frame Example in RSDA | How to generated a symbolic data table from a classic data table in RSDA? | Example 1 | Example 2 | Example 3 | Example 4 | Converting a SODAS 1.0 *.SDS files to RSDA files | Converting a SODAS 2.0 *.XML files to RSDA files | Basic statistics | Symbolic Mean | Symbolic median | Variance and standard deviation | Symbolic correlation | Radar plot for intervals | Distances for intervals | Gowda-Diday | Ichino | Hausdorff | Linear regression for intervals | Training | Prediction | Testing | LASSO regression for intervals | RIDGE regression for intervals | PCA for intervals | Example 5 | Symbolic Multiple Correspondence Analysis | Symbolic UMAP | Ejemplo Oils | Ejemplo Cardiological | Length of intervals | PCA Histogram | Hardwood Data | Weighted Center Matrix | Bin Matrix | PCA | Plots | KS

Data frames | Functions to calculate eGFR by different equations | Examples | Example for a single patient | Example for a cohort of patients | Advantages of the kidney.epi package functions | References

Introduction | Algorithmic complexity for short strings | The acss packages | Applications | Relationship to complexity based model selection | Conclusion

Introduction | Required Packages and Data Sets | Install and load the package gpcomp | Data sets | Data Visualization | Resampling Statistics with Permutations | Results and Interpretation | Other Tests

Introduction | Required Packages and Data Sets | Install and load the package gpcomp | Data sets | Data Visualization | Resampling Statistics with Bootstrap | Results and Interpretation | Other Tests

Introduction | Required Packages and Data Sets | Install and load the package gpcomp | Data sets | Data Visualization | Statistical Tests | Results and Interpretation | Tests for Statistics Other Than the Mean

Listing and previewing available colour palettes | Plotting examples | Discrete colour palette | Continuous colour palette | Conclusions

Introduction | Overview of Functions | References

Index | 1. RRmorph overview | 2. Preparing the data | 3. The analysis of shape | 4. rate.map | 5. conv.map | 6. interpol.mesh | References

0. About Rgff | 1. Check the consistency and order of a GFF file | 2. Getting the stats | 3. Extracting the feature organization of the GFF file | 4. Sorting GFF files | 5. Convert a GFF file to a SAF file | 6. Convert a GTF to a GFF3 file

Introduction | Package Workflow | Usage | Usage recommendation and things to consider | Step 1: Data Acquisition and Preparation | Retrieve Load Data: | Fill Missing Data: | Decomposition of Load Data | Step 2: Long-Term Trend Modeling | Retrieve Historical Data: | Add Macroeconomic Covariates: | Derive Long-Term Prediction Models: | Generate Future Predictions: | Step 3: Mid-Term Seasonality Modeling | Add Holidays: | Retrieve Weather Data: | Derive Mid-Term Prediction Models: | 1. Temperature Transformation Method | 2. Spline Method | Step 4: Short-Term Seasonality Modeling | Derive Short-Term Prediction Models: | Step 5: Combine All Models | Generate Future Forecasts: | All-in-One Function

Introduction | Pre-specified models | Mean-model functions | Exponential decay (first order) | Exponential decay (first order) with plateau | Exponential decay (second order) | Gompertz model | Four-parameter Hill (4PL) model | Five-parameter Hill model | Hill quadratic model | Hill switchpoint model | Beta model | User defined mean model | Weight/Variance functions | Tukey bi-weight | Huber weights | User-defined weights | Calling optim_fit | OLS and WLS | MLE | IRWLS | Robust model fit | Multiple starting values | User-defined model | Functions of parameters | Detecting outliers with ROUT | Summary | References

Preliminaries | Overview of models | Overview of functions

A quick go through | Creating a model | Generating synthetic food webs (if needed) | Creating a specific ATN model | Running the population dynamics | The food web generative functions | Examples | effect of temperature on species persistence | Effect of predator-prey body mass ratio and temperature on species persistence | Paradox of enrichment | Common mistakes, things to not do | Not updating model parameters properly in the model object | Updating key parameters without creating a new model object | Changing the dimensions of vectors and matrix fields in a model object without doing it consistently. | Shallow copying models | Modifying a model object in a *apply function

Introduction | Required Data | Laspeyres Index | Paasche Index | Fisher Index (Geometric Mean) | HMTS Index (Advanced) | Summary | References

Preparation | Prediction grid | National indices | Growth rates | Indices by group

Overview | Input files | convert "TTN" genotype data into a rNeighborGWAS format | simulate "fake_nei" dataset using nei_simu() | Association mapping | Binary phenotype | References

Introduction | Loading TARA Ocean datasets | Multiple kernel computation | Individual kernel computation | Combined kernel computation | Exploratory analysis: Kernel Principal Component Analysis (KPCA) | Perform KPCA | Assessing important variables | Selecting relevant variables | References | Session information

Introduction | Overview of discoverMotifs | Common Parameters | Motif Simulation | Examples

Package summary | What is multi-view stacking? | Why package mvs? | Overview of package functionality | Model fitting | Parallelization | Model generalizations | View importance | Handling missing data | Using mvs step-by-step | Installation | Fitting a basic model | Random forests | Parallel computing | Fitting a model with more than two levels | Fitting a model with missing data | Acknowledgements | References

Easy Study of Patient DICOM Data in Oncology | Input data | DICOM Handling | Patient overview | 2D Display | 3D Display | Geometry tools | Binary and weight objects | Meshes | Histograms | Radiotherapy indices | Spatial similarity metrics | References

📦 Introduction | 🧪 Sample Dataset | 📐 Langmuir Isotherm | 📐 Freundlich Isotherm | 📐 BET Isotherm | 📐 Temkin Isotherm | 🔁 Non-linear Isotherm Modeling | Non-linear Langmuir | Non-linear Freundlich | Non-linear BET | Non-linear Temkin | 📝 Conclusion

Introduction | Data Preparation | Computing graph metrics | Degrees and Hubs

Introduction | Data Preparation | Plots

Table of Contents | Computation of Fisher information matrix | Example 1: GLM Fisher information matrix | Example 2: MLM Fisher information matrix | Applications in paid research studies | Example 3: trial_data & constrained sampling with GLM | Example 4: trauma_data & constrained sampling with MLM | References

Specifing the wrong bounding box | Sanity checks | EPSG:4326 and CRS:84

Extent from Polygon | Extent from Bounding Box | Extent from Radius | Extent from National Grid Code

Polygons with Popups | Get Data from the API | Define the Map | Add a Basemap | Add the Data | Add Bounding Box

Simple Features Data Frame | maptiles and tmap

1. Getting Started | Importing the osdatahub package | Importing your OS Data Hub API Key | Import the osdatahub package | 2. Discovering Collections | 3. Loading NGD Data into a Spatial Data Frame | Requesting NGD Data | Importing into a spatial data frame | 4. Adding Filters | Specifying an Extent (Bounding Box) | Custom Paging Parameters | Applying CQL Filters | 5. Conclusion

Introduction | Scope | Loading catchment data | Settings for data assimilation | Ensemble size | Enable/disable the assimilation via EnKF or PF | Model uncertainties | Definition of DA settings | Ensemble meteorological forcings | Discharge observations for DA | DA-based discharge simulations | EnKF-based discharge simulations | PF-based discharge simulations | Comparative performance assessment | References

Index | 1. Introduction | 2. Formatting the data | 3. Running ENphylo_modeling | ENphylo_modeling outputs | 4. Running ENphylo_prediction | References

Index | 1. Introduction | 2. Background area definition | 3. eucop_data_preparation on Ursus spelaeus walkthrough | a. Load the occurrence of the target species | b. Minimum Convex Polygon and buffer area creation | c. Absence points | eucop_data_preparation outputs | References

Index | 1. Introduction | 2. Formatting data | 3. Run RRphylogeography | 4. RRphylogeography outputs | References

Dataframes | Functions | Calculate KDPI and KDRI with ktx.kdpi.optn | Show years available in the OPTN mapping table with ktx.kdpi.optn.show.years | References

💼 Services | 🔗 Connect | 🧐 Check | 🔬 Investigate | 📊 Visualize | ✍️ Write | 🌍 Communicate | 🛠 Develop | 💶 Fund | 🤝 Work

Functions | Arguments | Details | Example 1: Two Continuous Predictor Variables | Example 2: Two Predictor Variables (1 Continuous, 1 Categorical) | References

Introduction | Theory of GLARMA models | Modelling | Diagnostics | Examples

Introduction | Simulating tumor clonal data | Topology parameter k | Simulate a tumor with k=0: | Simulate a tumor with k=1: | Create a Phylotree class object for each tumor: | Plot both trees | Sequencing noise | The Phylotree S4 class | Instantiation of Phylotree class objects | Comparing and combining phylogenetic trees | The equals method | The find_common_subtrees method | The combine_trees method | Session information

Calculate OS | Master Prep Data Function | Fit path model | Team | Individual | Partial correlation matrix | Partial from uni approach

Spatial Blind Source Separation Framework | Spatial Blind Source Separation Model | Local Covariance Matrices and Spatial Kernel Functions | How the estimate the unmixing matrix? | Spatial Blind Source Separation with the function sbss | Functions for class 'sbss' | Alternative local scatter matrix | Computing spatial kernel matrices | Computing local covariance matrices | Anisotropic Local Covariance Matrices

1. Introduction | 2. Substitution Methods | 3. Outcome Data: Normal or Log-Normal Distribution | 3-1. Simulated Dataset 15 | 3-2. Marginal Modeling (Mean Regression Model) | 3-3. Time-Dependent Covariates | 4. Outcome Data: Unknown Distribution | 4-1. Marginal Modeling (Quantile Regression Model) | 4-2. Time-Dependent Covariates | 5. References

Introduction | Installation | Example Usage

Introduction to MariNET | Installation | Loading Data | Linear Mixed effects Model network | Network visualization | Comparison between models | Additional information | References

1. Introduction | 2.1 subscreencalc Input | 2.1.1. data | 2.2 subscreencalc Output | 2.3 subscreenvi | 2.3 subscreenshow | 3. Subgroup Explorer | 3.1 Upload | 3.2 Explorer | 3.2.1 Diagram | 3.2.2 Lists | 3.2.3 Interaction Plot | 3.2.4 Options | 3.3 Comparer | 3.4 Mosaic | 3.5 ASMUS

Introduction | Evaluation of non-standard content (Non-Standard Evaluation) | Download and explore CRAN | crandb_down() | crandb_comp() | crandb_fromto() | Search in crandb | s_crandb() | s_crandb_list() | s_crandb_PTD() | s_crandb_AM() | s_crandb_tvdb() | Search with the sos package

Introduction | (Down)Load crandb and extract packages | Explore the selected packages | HTML and PDF formats | Table format | p_table2() prints in the console | p_deps() prints in the console | p_vers() prints in the console | p_vers_deps() prints in the console | p_display7() opens the browser | p_table7pdf() prints in a pdf file (table style) | Text format | p_text() prints in a txt file | p_text2md() prints in a txt file with md extension | p_text2pdf() prints in a pdf file (article style) | Download the documentation | p_down() | p_down0()

Introduction | Package dependencies | p_vers_deps() lists the package dependencies in a proper order | p_graphF() generates the graph of the package dependencies | Subset the data.frame of the package dependencies | p_inst() installs the packages

Introduction | Task views | View, download, load and explore task views | crandb_down(), tvdb_down(), crandb_load(), tvdb_load() | tvdb_vec(), tvdb_dfr() | tvdb_list(), tvdb_pkgs() | Counting the number of referred packages | Task view maintenance | s_crandb_tvdb() | Visualize the unreferred packages | p_table2(), p_display5() | p_page(), p_pdfweb()

Introduction | Explore the web | The generic function | R related links | Direct links | Links with keywords and options | Suggest new search engines

Overview | Usage | Example | References

Introduction | Installation | Data generation | Model fitting | Prediction | References

Input Data | Extant Data | Phylogenetic Trees | Paleoclimate Data | Fossil Data | Set Up Analysis | Bounds | Control | Run PPGM | Many Trees - No Fossils - Brownian Motion | Many Trees - No Fossils - Ornstein Uhlenbeck | PPGM results | Climate Envelope | Treedata Objects | Viewing climate envelope occupancy geographically - MESS | A PPGM with Fossils

Overview | Distance Motivation | Optimal Transport | Background papers | Basic Idea | Balanced OT | Unbalanced OT | Simulated Example | Gene-Gene Similarity | Simulate mutated gene statuses | Euclidean distance | OT distance | Code between two samples | Code between all sample pairs | Dendrograms | Kernel Regression and Association | Models | Distance to Kernel | Hypothesis Testing | Session Info | References

Introduction | Aim of this document | Simulate a whole dataset | Preliminaries | Default dataset | Customising options | Available subsample definitions | Tensile tests | Bending tests | Simulated dataset with data from specific countries | Different subsample sizes | Own specification of means and standard deviations | Further available options | Add simulated values to a dataset | simbase_covar without transformation | simbase_covar with log-transformed $f$ | simbase_covar with log-transformed $f$ and derived on a grouped dataset | Simulate a whole dataset, based on a simbase_list object | Conclusions | References

Discrimination tests | Global statistics | Matched statistics | callback rates | Total callback shares | exclusive callback shares

Difference estimators | Components models | Application | Standard case | \begin{align*}\left( \begin{array}{c}p_{m0}-p_{m1}\p_{f0}-p_{m1}\p_{f1}-p_{m1}\end{array}\right) | Candidates grouping | References

Introduction | Description of the metaumbrella package | Example 1: "Ioannidis" classification | Example 2: "GRADE" classification | Example 3: "Personalized" classification | Example 4: "Personalized" classification with multilevel data

Aim | Data | Score distribution estimation | Visualize and compare the results | Find cocktails with higher score

Software | Definitions | Copy Number | Cellular prevalence | Variant Allele Frequency | Simulation | Optimization | Known configuration | Unknown configuration | Downstream Applications | Session Info | References

Acknowledgment

Introduction | Usage | Output | Details | Example | Interpretation of Results | References | Acknowledgment

Introduction | Usage | Arguments | Output | Details | Example | Interpretation of Results | References | Acknowledgment

Introduction | Usage | Arguments | Output | Details | Example | Interpretation of Results | References | Acknowledgment

Introduction | Usage | Arguments | Output | Details | Example | Interpretation of Results | Acknowledgment

Package installation | Build the dataset | Accounting for correlations between individual GWAS | Global meta-analysis procedures | Check the candidates | Manhattan plot and heatmap | Score local | Tests for GxE interactions | Tests of contrast | Tests of meta-regression

expertsurv | Installation | Expert Opinion on Survival at timepoints | Expert Opinion using Penalized Maximum Likelihood | Expert Opinion on Survival of a comparator arm | Expert Opinion on Survival Difference | Compatability with underlying packages survHE and flexsurv | Model Diagnostics | General Population Mortality | Setting Initial values to estimate models (Particularly Gompertz) | Technical note on the impact of priors | Potential Future Updates | References

References

title: Introduction to working with code listsauthor: Jan van der Laancss: "style.css" | Locale | Looking up codes based on label | Assignment of codes | Hierarchies | Safety

Introduction | Authentication | Fetching Tables | Fetching rows | Fetching images/attachments | Conclusion

imanr: Identificador de Maíz Nativo en R | Introduction | Usage | find_racial_complex() | impute_data() | Contact | imanr package installation | References

Data loading | Understanding rainfall-runoff modelling | The role of model components and parameters | On the need to perform a model warm-up | Model calibration | Manual calibration | Automatic calibration | How to evaluate model calibration? | Streamflow transformation for model calibration | Impact of objective functions | Model evaluation and robustness | Split-sample test | Differential split-sample test | Other applications | References

Example | References

Introduction | References

Introduction | Example | References

Introduction | Example

Introduction | Example | References

Introduction | Example | References

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Introduction | Ad-Plot | Example 1 | Example 2 | Example 3 | Ud-Plot | Example 4 | Example 5 | Example 6 | Reference

Analyze text | Load library | Get sample text | Get tokens | Get bigrams | Get trigrams

Contents

Programming environment | Data preprocessing | Quality control | Identifying placental aging

Introduction | How To Generate a Precision Profile | Plotting Precision Profiles | Functional Sensitivity and More | C5 and C95 for Qualitative Tests

1 Introduction | 2 Functions | 3 ICAR Model Summary | 4 Example: Objective ICAR Inference | 5 Example: Objective Model Selection for Areal Data | References

Introduction | Application | Relsurv Approach | dMrs Approach | Net-survival | Session information | References

Installation | SifiNet method | Create SifiNet object | Section A: Construct the gene co-expression network | Section B: Calculate gene connectivity and find feature gene sets | Session information

CMHSU | Installation | Sample Simulated Real World Data (SampleRWD): The following simulated dataset is used in the CMHSU R package examples: | Example 1 | Example 2 | Example 3 | Example 4

Introduction | Data handling | spec.denoise() | Colour Vision Models | The Basics | Chittka (1992) Colour Hexagon | CTTKmodel() | Endler & Mielke (2005) | EMmodel() | Receptor Noise Limited Models (Vorobyev & Osorio 1998; Vorobyev et al. 1998) | RNLmodel() | RNLachrom() | RNLthres() | Generic model | GENmodel() | Chromaticity distances ($\Delta$S) | deltaS | plot(model) for threshold values | radarplot(model) | References

Programming environment | Binary outcome (or class-wise multinomial outcome) | 1 - Categorical predictors and binary outcome without probability | 2 - Categorical predictors and binary outcome with probability | 3 - Categorical with single numerical predictors and binary outcome with probability | Continuous outcome | 4 - Categorical predictors and continuous outcome | 5 - Categorical with single numerical predictors and continuous outcome

Introduction | How to install MuPETFlow | How to run MuPETFlow | Description | Peaks | Regression | Summary | References

Introduction | Data Preparation | Usage of catalytic | Step 1: Initialization | Step 2.1: Choose Method(s) - Estimation with Fixed tau | Step 2.2: Choose Method(s) - Estimation with Selective tau | Cross-validation (risk_estimate_method = "cross_validation") | Step 2.3: Bayesian Posterior Sampling with Fixed tau | 2.4 Bayesian Posterior Sampling with Adaptive Tau | References

Introduction | Data Preparation | Usage of catalytic | Step 1: Initialization | Step 2.1: Choose Method(s) - Estimation with fixed tau | Step 2.2: Choose Method(s) - Estimation with Selective tau | Cross-validation (risk_estimate_method = "cross_validation") | Bootstrap (risk_estimate_method = "parametric_bootstrap") | Steinian Estimate (risk_estimate_method = "steinian_estimate") | Recommendations for Choosing risk_estimate_method and discrepancy_method | Automatic Parameter Selection | Step 2.3: Choose Method(s) - Bayesian Posterior Sampling with Fixed Tau | Step 2.4: Choose Method(s) - Bayesian Posterior Sampling with Adaptive Tau | Step 2.5: Choose Method(s) - Special Approaches for Binomial Models | Gibbs Sampling | Setting Higher Tau Lower Bound | Incorporating theta (1/tau) | Incorporating theta (1/tau) with Adaptive Alpha | References

Introduction | Data Preparation | Usage of catalytic | Step 1: Initialization | Step 2.1: Choose Method(s) - Estimation with Fixed tau | Step 2.2: Choose Method(s) - Estimation with Selective tau | Cross-validation (risk_estimate_method = "cross_validation") | Bootstrap (risk_estimate_method = "parametric_bootstrap") | Mallowian Estimate (risk_estimate_method = " mallowian_estimate") | Recommendations for Choosing risk_estimate_method and discrepancy_method | Automatic Parameter Selection | Step 2.3: Choose Method(s) - Bayesian Posterior Sampling with Fixed tau | Step 2.4: Choose Method(s) - Bayesian Posterior Sampling with Adaptive Tau | References

Info data | Import data | Mixed Model Approach | Two-Stage Summary Statistics approach: flip2sss (= Second-level, Group-level Analisys)

Introduction | Modeling framework | References

Introduction | Example Data | Estimate and Correct GC Bias | Estimating Normal Karyotype Range | Estimating feature score | run_RCNA

Main function and parameters | Get CSV outputs | Showing CSV outputs | Get tsv outputs | Showing tsv outputs | TSV and CSV should be identical | Get rda outputs | Showing rda outputs | RDA and loaded CSV hold the same values

moments_graph function | skr_graph function | skr_param_graph function | SKR graph when skew-non-uniform distribution | Output PNG, JPEG or SVG graphs

Use Multiprocessing ? | Run with Single Core processing | Run with Multiprocessing | When you have finished | Running the TAD Analysis

The category_blur utility