The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.

rdlearn: Safe Policy Learning under Regression Discontinuity Design with Multiple Cutoffs

Implements safe policy learning under regression discontinuity designs with multiple cutoffs, based on Zhang et al. (2022) <doi:10.48550/arXiv.2208.13323>. The learned cutoffs are guaranteed to perform no worse than the existing cutoffs in terms of overall outcomes. The 'rdlearn' package also includes features for visualizing the learned cutoffs relative to the baseline and conducting sensitivity analyses.

Version: 0.1.1
Depends: R (≥ 3.5.0)
Imports: nprobust, nnet, rdrobust, ggplot2, dplyr, glue, cli
Suggests: knitr, rmarkdown, testthat (≥ 3.0.0)
Published: 2025-01-29
DOI: 10.32614/CRAN.package.rdlearn
Author: Kentaro Kawato [cre, cph], Yi Zhang [aut], Soichiro Yamauchi [aut], Eli Ben-Michael [aut], Kosuke Imai [aut]
Maintainer: Kentaro Kawato <kentaro1358nohe at gmail.com>
BugReports: https://github.com/kkawato/rdlearn/issues
License: MIT + file LICENSE
URL: https://github.com/kkawato/rdlearn
NeedsCompilation: no
Materials: README NEWS
CRAN checks: rdlearn results

Documentation:

Reference manual: rdlearn.pdf
Vignettes: Replication by 'rdlearn' (source, R code)

Downloads:

Package source: rdlearn_0.1.1.tar.gz
Windows binaries: r-devel: not available, r-release: rdlearn_0.1.1.zip, r-oldrel: not available
macOS binaries: r-release (arm64): rdlearn_0.1.1.tgz, r-oldrel (arm64): not available, r-release (x86_64): not available, r-oldrel (x86_64): not available

Linking:

Please use the canonical form https://CRAN.R-project.org/package=rdlearn to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.