Evaluating Individualized Treatment Rules R package evalITR version 1.0.0 CRAN Task View: CausalInference (https://CRAN.R-project.org/view=CausalInference) 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) <arXiv:1905.05389>. Software 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 Michael Lingzhi Li <mili@hbs.edu> Comprehensive R Archive Network (CRAN) Michael Lingzhi Li [aut, cre], Kosuke Imai [aut], Jialu Li [ctb], Xiaolong Yang [ctb] GPL (>= 2) 2023-08-25 application/tgz https://CRAN.R-project.org/package=evalITR doi:10.32614/CRAN.package.evalITR en-US