evalITR: Evaluating Individualized Treatment Rules

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


Reference manual: evalITR.pdf
Vignettes: Cross-validation with multiple ML algorithms
Cross-validation with single algorithm
Sample Splitting
Sample Splitting with Caret/SuperLearner
User Defined ITR
Compare Estimated and User Defined ITR


Package source: evalITR_1.0.0.tar.gz
Windows binaries: r-devel: evalITR_1.0.0.zip, r-release: evalITR_1.0.0.zip, r-oldrel: evalITR_1.0.0.zip
macOS binaries: r-release (arm64): evalITR_1.0.0.tgz, r-oldrel (arm64): evalITR_1.0.0.tgz, r-release (x86_64): evalITR_1.0.0.tgz, r-oldrel (x86_64): evalITR_1.0.0.tgz
Old sources: evalITR archive


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