Package: LRErdd 0.1.0

LRErdd: Regression Discontinuity Designs as Local Randomized Experiments

A set of functions for the design and analysis of Regression Discontinuity Designs as local randomized experiments within the potential outcome approach as formalized in Li, Mattei and Mealli (2015) <doi:10.1214/15-AOAS809>. A subset of functions implements the design phase of the study, where the focus is on the selection of suitable subpopulations for which valid causal inference can be drawn. These functions provide summary statistics of pre- and post-treatment variables by treatment status and select suitable subpopulations around the threshold where pre-treatment variables are well balanced between treatment groups, using randomization-based tests with adjustment for multiplicities. Functions for a visual inspection of the results are also provided. Finally, the package includes a set of functions for drawing inference on causal effects for the selected subpopulations using randomization-based modes of inference. Specifically, the Fisher Exact p-value and Neyman approaches are implemented for the analysis of both sharp and fuzzy Regression Discontinuity designs. The approach is illustrated in a study concerning the effects of university grants on student dropout.

Authors:Ibon Tamayo [aut, cre], Alessandra Mattei [aut], Fabrizia Mealli [aut], Marie-Abele Bind [aut]

LRErdd_0.1.0.tar.gz
LRErdd_0.1.0.zip(r-4.7-any)LRErdd_0.1.0.zip(r-4.6-any)LRErdd_0.1.0.zip(r-4.5-any)
LRErdd_0.1.0.tgz(r-4.6-any)LRErdd_0.1.0.tgz(r-4.5-any)
LRErdd_0.1.0.tar.gz(r-4.7-any)LRErdd_0.1.0.tar.gz(r-4.6-any)
LRErdd_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
LRErdd/json (API)

# Install 'LRErdd' in R:

install.packages('LRErdd', repos = c('https://itamuria.r-universe.dev', 'https://cloud.r-project.org'))

Datasets:

grants - Italian university grants and student dropout

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

2.70 score 47 exports 43 dependencies

Last updated from:33c94af105. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel	OK	151
source / vignettes	OK	236
linux-release	OK	164
macos-release	OK	164
macos-oldrel	OK	157
windows-devel	OK	123
windows-release	OK	87
windows-oldrel	OK	106
wasm-release	OK	116

Exports:all_binom2num cutting_range da.bin da.bin.h0 da.bin.h02 da.bin2 da.gauss da.gauss.h0 da.gauss.h02 da.gauss2 E.step.bin E.step.bin2 E.step.gauss E.step.gauss2 EM.bin EM.bin2 EM.gauss EM.gauss2 fuzzy_fep_bw fuzzy_fep_numeric1sided fuzzy_fep_numeric2sided fuzzy_fep1sided fuzzy_fep2sided fuzzy_neyman fuzzy_neyman_bw M.step.bin M.step.bin2 M.step.gauss M.step.gauss2 mcmc.bin mcmc.bin.h0 mcmc.bin.h02 mcmc.bin2 mcmc.gauss mcmc.gauss.h0 mcmc.gauss.h02 mcmc.gauss2 open_LRErdd_framework plus rand_pajd rand_pajd_bw RegressionDiscontinuityClass sharp_fep sharp_fep_bw sharp_neyman sharp_neyman_bw Tave

Dependencies:base64enc bslib cachem cli commonmark cowplot cpp11 digest farver fastmap fontawesome fs ggplot2 glue gtable gtools htmltools httpuv isoband jquerylib jsonlite labeling later lifecycle magrittr memoise mime otel promises R6 rappdirs RColorBrewer Rcpp rlang S7 sass scales shiny sourcetools vctrs viridisLite withr xtable

LRErdd: Regression Discontinuity Designs as Local Randomized Experiments

Rendered fromLRErdd.Rmdusingknitr::rmarkdown

Last update: 2026-08-06
Started: 2026-08-06

Help page	Topics
From binomial to numerical	all_binom2num
Selection of the cutting range	cutting_range
Data Augmentation (DA) step of Markov Chain Monte Carlo (MCMC) algorithm to derive the posterior median of the Complier Average Causal Effect (CACE) for binary outcomes	da.bin
Data Augmentation (DA) step of Markov Chain Monte Carlo (MCMC) algorithm to derive the posterior median of the Complier Average Causal Effect (CACE) for a binary outcome under the sharp null hypothesis of no effect	da.bin.h0
Data Augmentation (DA) step of Markov Chain Monte Carlo (MCMC) algorithm to derive the posterior median of the Complier Average Causal Effect (CACE) for a binary outcome under the sharp null hypothesis of no effect	da.bin.h02
Data Augmentation (DA) step of Markov Chain Monte Carlo (MCMC) algorithm to derive the posterior median of the Complier Average Causal Effect (CACE) for binary outcomes	da.bin2
Data Augmentation (DA) step of Markov Chain Monte Carlo (MCMC) algorithm to derive the posterior median of the Complier Average Causal Effect (CACE) for Gaussian outcomes	da.gauss
Data Augmentation (DA) step of Markov Chain Monte Carlo (MCMC) algorithm to derive the posterior median of the Complier Average Causal Effect (CACE) for a Gaussian outcome under the sharp null hypothesis of no effect	da.gauss.h0
Data Augmentation (DA) step of Markov Chain Monte Carlo (MCMC) algorithm to derive the posterior median of the Complier Average Causal Effect (CACE) for a Gaussian outcome under the sharp null hypothesis of no effect	da.gauss.h02
Data Augmentation (DA) step of Markov Chain Monte Carlo (MCMC) algorithm to derive the posterior median of the Complier Average Causal Effect (CACE) for Gaussian outcomes	da.gauss2
Expectation step of the Expectation-Maximization (EM) algorithm to calculate the MLE of the Complier Average Causal Effect (CACE) for binary outcomes	E.step.bin
Expectation step of the Expectation-Maximization (EM) algorithm to calculate the MLE of the Complier Average Causal Effect (CACE) for binary outcomes	E.step.bin2
Expectation step of the Expectation-Maximization (EM) algorithm to calculate the MLE of the Complier Average Causal Effect (CACE) for Gaussian outcomes	E.step.gauss
Expectation step of the Expectation-Maximization (EM) algorithm to calculate the MLE of the Complier Average Causal Effect (CACE) for Gaussian outcomes	E.step.gauss2
Maximum Likelihood Estimate (MLE) of the Complier Average Causal Effect (CACE) for binary outcomes	EM.bin
Maximum Likelihood Estimate (MLE) of the Complier Average Causal Effect (CACE) for binary outcomes	EM.bin2
Maximum Likelihood Estimate (MLE) of the Complier Average Causal Effect (CACE) for Gaussian outcomes	EM.gauss
Maximum Likelihood Estimate (MLE) of the Complier Average Causal Effect (CACE) for Gaussian outcomes	EM.gauss2
Bandwidth selection for Fuzzy - FEP bandwidth	fuzzy_fep_bw
Fuzzy - FEP 1 sided numerical	fuzzy_fep_numeric1sided
Fuzzy - FEP 2 sided numerical	fuzzy_fep_numeric2sided
Fuzzy - FEP approach for binary one sided	fuzzy_fep1sided
Fuzzy - FEP binary two sided	fuzzy_fep2sided
Fuzzy - neyman approach	fuzzy_neyman
Bandwidth selection for Fuzzy - neyman bandwidth	fuzzy_neyman_bw
Italian university grants and student dropout	grants
Maximization step of the Expectation-Maximization (EM) algorithm to calculate the MLE of the Complier Average Causal Effect (CACE) for binary outcomes	M.step.bin
Maximization step of the Expectation-Maximization (EM) algorithm to calculate the MLE of the Complier Average Causal Effect (CACE) for binary outcomes	M.step.bin2
Maximization step of the Expectation-Maximization (EM) algorithm to calculate the MLE of the Complier Average Causal Effect (CACE) for Gaussian outcomes	M.step.gauss
Maximization step of the Expectation-Maximization (EM) algorithm to calculate the MLE of the Complier Average Causal Effect (CACE) for Gaussian outcomes	M.step.gauss2
Posterior median of the Complier Average Causal Effect (CACE) for binary outcomes	mcmc.bin
Posterior distributions of the parameter vector and the compliance status of each unit for a binary outcome under the sharp null hypothesis of no effect	mcmc.bin.h0
Posterior distributions of the parameter vector and the compliance status of each unit for a binary outcome under the sharp null hypothesis of no effect	mcmc.bin.h02
Posterior median of the Complier Average Causal Effect (CACE) for binary outcomes	mcmc.bin2
Posterior median of the Complier Average Causal Effect (CACE) for Gaussian outcomes	mcmc.gauss
Posterior distributions of the parameter vector and the compliance status of each unit for a Gaussian outcome under the sharp null hypothesis of no effect	mcmc.gauss.h0
Posterior distributions of the parameter vector and the compliance status of each unit for a Gaussian outcome under the sharp null hypothesis of no effect	mcmc.gauss.h02
Posterior median of the Complier Average Causal Effect (CACE) for Gaussian outcomes	mcmc.gauss2
Example of 'LRErdd' in shiny version	open_LRErdd_framework
Randomization-based tests adjusted for multiple testing	rand_pajd
Bandwidth selection for Randomization-based tests adjusted for multiple testing	rand_pajd_bw
Class providing object with RegressionDiscontinuityClass	RegressionDiscontinuityClass
Calculate Fisher Exact p-value	sharp_fep
Bandwidth selection for Fisher Exact p-value	sharp_fep_bw
SHARP RDD: RANDOMIZATION-BASED INFERENCE - NEYMAN APPROACH	sharp_neyman
Bandwidth selection for Sharp - neyman approach	sharp_neyman_bw

Package: LRErdd 0.1.0

LRErdd: Regression Discontinuity Designs as Local Randomized Experiments

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