Package: rjaf 0.1.2
Xinyi Zhang
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.
Authors:
rjaf_0.1.2.tar.gz
rjaf_0.1.2.tar.gz(r-4.5-noble)rjaf_0.1.2.tar.gz(r-4.4-noble)
rjaf_0.1.2.tgz(r-4.4-emscripten)rjaf_0.1.2.tgz(r-4.3-emscripten)
rjaf.pdf |rjaf.html✨
rjaf/json (API)
| # Install 'rjaf' in R: |
| install.packages('rjaf', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/wustat/rjaf/issues
- Example_data - Simulated randomized experiment data
Last updated 1 months agofrom:c0049e6010. Checks:3 OK. Indexed: yes.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Mar 18 2025 |
| R-4.5-linux-x86_64 | OK | Mar 18 2025 |
| R-4.4-linux-x86_64 | OK | Mar 18 2025 |
Exports:residualizerjaf
Dependencies:bitbit64clicliprcpp11crayondplyrfansiforcatsgenericsgluehmslatticelifecyclemagrittrMASSMatrixpillarpkgconfigprettyunitsprogresspurrrR6randomForestrangerRcppRcppArmadilloRcppEigenreadrrlangstringistringrtibbletidyrtidyselecttzdbutf8vctrsvroomwithr
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Simulated randomized experiment data | Example_data |
| Arbitrary residualization of outcomes | residualize |
| Regularized Joint Assignment Forest with Treatment Arm Clustering | rjaf |