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 [issues need fixing before 2024-12-18] |