Personalized assignment to one of many treatment arms via regularized and clustered joint assignment forests as described in Ladhania, Spiess, Ungar, and Wu (2023) <doi:10.48550/arXiv.2311.00577>. The algorithm pools information across treatment arms: it considers a regularized forest-based assignment algorithm based on greedy recursive partitioning that shrinks effect estimates across arms; and it incorporates a clustering scheme that combines treatment arms with consistently similar outcomes.
Version: | 0.1.0 |
Depends: | R (≥ 3.5.0) |
Imports: | Rcpp, dplyr, tibble, magrittr, readr, randomForest, ranger, forcats, rlang (≥ 1.1.0), tidyr, stringr, MASS |
LinkingTo: | Rcpp, RcppArmadillo |
Suggests: | knitr, rmarkdown, testthat (≥ 3.0.0) |
Published: | 2024-11-11 |
DOI: | 10.32614/CRAN.package.rjaf |
Author: | Wenbo Wu [aut, cph], Xinyi Zhang [aut, cre, cph], Jann Spiess [aut, cph], Rahul Ladhania [aut, cph] |
Maintainer: | Xinyi Zhang <zhang.xinyi at nyu.edu> |
BugReports: | https://github.com/wustat/rjaf/issues |
License: | GPL-3 |
URL: | https://github.com/wustat/rjaf |
NeedsCompilation: | yes |
CRAN checks: | rjaf results |
Reference manual: | rjaf.pdf |
Package source: | rjaf_0.1.0.tar.gz |
Windows binaries: | r-devel: rjaf_0.1.0.zip, r-release: rjaf_0.1.0.zip, r-oldrel: rjaf_0.1.0.zip |
macOS binaries: | r-release (arm64): rjaf_0.1.0.tgz, r-oldrel (arm64): rjaf_0.1.0.tgz, r-release (x86_64): rjaf_0.1.0.tgz, r-oldrel (x86_64): rjaf_0.1.0.tgz |
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