evalITR: Evaluating Individualized Treatment Rules (original) (raw)
Provides various statistical methods for evaluating Individualized Treatment Rules under randomized data. The provided metrics include Population Average Value (PAV), Population Average Prescription Effect (PAPE), Area Under Prescription Effect Curve (AUPEC). It also provides the tools to analyze Individualized Treatment Rules under budget constraints. Detailed reference in Imai and Li (2019) <doi:10.48550/arXiv.1905.05389>.
| Version: | 1.0.0 |
|---|---|
| Depends: | dplyr (≥ 1.0), MASS (≥ 7.0), Matrix (≥ 1.0), quadprog (≥ 1.0), R (≥ 3.5.0), stats |
| Imports: | caret, cli, e1071, forcats, gbm, ggdist, ggplot2, ggthemes, glmnet, grf, haven, purrr, rlang, rpart, rqPen, scales, utils, bartCause, SuperLearner |
| Suggests: | doParallel, furrr, knitr, rmarkdown, testthat, bartMachine, elasticnet, randomForest, spelling |
| Published: | 2023-08-25 |
| DOI: | 10.32614/CRAN.package.evalITR |
| Author: | Michael Lingzhi Li [aut, cre], Kosuke Imai [aut], Jialu Li [ctb], Xiaolong Yang [ctb] |
| Maintainer: | Michael Lingzhi Li |
| BugReports: | https://github.com/MichaelLLi/evalITR/issues |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: | https://github.com/MichaelLLi/evalITR,https://michaellli.github.io/evalITR/,https://jialul.github.io/causal-ml/ |
| NeedsCompilation: | no |
| Language: | en-US |
| Materials: | README NEWS |
| In views: | CausalInference |
| CRAN checks: | evalITR results |
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