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: |
0.1.1 |
| 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: |
2024-12-08 |
| 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 |