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rjaf
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Download and install rjaf package within the R console
Install from CRAN:
install.packages("rjaf")
Install from Github:
library("remotes")
install_github("cran/rjaf")
Install by package version:
library("remotes")
install_version("rjaf", "0.1.1")
Attach the package and use:
library("rjaf")
Maintained by
Xinyi Zhang
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
10.32614/CRAN.package.rjaf . https://github.com/wustat/rjaf/issues . https://github.com/wustat/rjaf . rjaf results . rjaf.pdf . rjaf_0.1.2.tar.gz . rjaf_0.1.1.zip . rjaf_0.1.1.zip . rjaf_0.1.2.zip . rjaf_0.1.1.tgz . rjaf_0.1.1.tgz . rjaf_0.1.1.tgz . rjaf_0.1.2.tgz . rjaf_0.1.2.tgz . rjaf_0.1.2.tgz . rjaf archive . https://CRAN.R-project.org/package=rjaf .
First Published: 2024-11-11
Latest Update: 2024-11-11
Description:
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.
How to cite:
Xinyi Zhang (2024). rjaf: Regularized Joint Assignment Forest with Treatment Arm Clustering. R package version 0.1.1, https://cran.r-project.org/web/packages/rjaf. Accessed 17 Feb. 2025.
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