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 <mili at hbs.edu> |
| 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 |