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.
| Version: |
0.1.0 |
| Depends: |
R (≥ 3.5) |
| Imports: |
ggplot2, cowplot, gtools, R6, shiny, stats |
| Suggests: |
knitr, rmarkdown, bslib, shinycssloaders, DT, openxlsx, testthat (≥ 3.0.0) |
| Published: |
2026-08-06 |
| DOI: |
10.32614/CRAN.package.LRErdd (may not be active yet) |
| Author: |
Ibon Tamayo [aut, cre],
Alessandra Mattei [aut],
Fabrizia Mealli [aut],
Marie-Abele Bind [aut] |
| Maintainer: |
Ibon Tamayo <itamuria at gmail.com> |
| License: |
MIT + file LICENSE |
| NeedsCompilation: |
no |
| Materials: |
README |
| CRAN checks: |
LRErdd results |
Documentation:
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