rjaf: Regularized Joint Assignment Forest with Treatment ArmClustering
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: | 0.1.3 |
| 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: | 2025-04-10 |
| 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 |
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