rjaf: Regularized Joint Assignment Forest with Treatment Arm Clustering

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

Documentation:

Reference manual:

rjaf.pdf

Downloads:

Package source:	rjaf_0.1.1.tar.gz
Windows binaries:	r-devel: rjaf_0.1.1.zip, r-release: rjaf_0.1.1.zip, r-oldrel: rjaf_0.1.1.zip
macOS binaries:	r-release (arm64): rjaf_0.1.1.tgz, r-oldrel (arm64): rjaf_0.1.1.tgz, r-release (x86_64): rjaf_0.1.1.tgz, r-oldrel (x86_64): rjaf_0.1.1.tgz
Old sources:	rjaf archive

Linking:

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