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 Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=rjaf to link to this page.
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