Package: rdlearn 0.1.1
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.
Authors:
rdlearn_0.1.1.tar.gz
rdlearn_0.1.1.zip(r-4.7-any)rdlearn_0.1.1.zip(r-4.6-any)rdlearn_0.1.1.zip(r-4.5-any)
rdlearn_0.1.1.tgz(r-4.6-any)rdlearn_0.1.1.tgz(r-4.5-any)
rdlearn_0.1.1.tar.gz(r-4.7-any)rdlearn_0.1.1.tar.gz(r-4.6-any)
rdlearn_0.1.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
rdlearn/json (API)
| # Install 'rdlearn' in R: |
| install.packages('rdlearn', repos = c('https://kkawato.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/kkawato/rdlearn/issues
Last updated from:98b1e257a7. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 197 | ||
| source / vignettes | OK | 183 | ||
| linux-release-x86_64 | OK | 196 | ||
| macos-release-arm64 | OK | 193 | ||
| macos-oldrel-arm64 | OK | 196 | ||
| windows-devel | OK | 130 | ||
| windows-release | OK | 152 | ||
| windows-oldrel | OK | 138 | ||
| wasm-release | OK | 111 |
Exports:plotrdestimaterdlearnsenssummary
Dependencies:clicpp11dplyrfarvergenericsggplot2gluegtableisobandlabelinglifecyclemagrittrMASSnnetnprobustpillarpkgconfigR6RColorBrewerrdrobustrlangS7scalestibbletidyselectutf8vctrsviridisLitewithr
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| ACCES Program | acces |
| Plot Cutoff Changes for rdlearn Objects | plot |
| RD Estimate Function | rdestimate |
| Safe Policy Learning for Regression Discontinuity Design with Multiple Cutoffs | rdlearn |
| Sensitivity Analysis for rdlearn Objects | sens |
| Simulation Data A | simdata_A |
| Simulation Data B | simdata_B |
| Summary function | summary |
