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 Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=evalITR to link to this page.
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