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) <arXiv:1905.05389>.

Version: 0.3.0
Depends: stats, MASS (≥ 7.0), quadprog (≥ 1.0), Matrix (≥ 1.0), dplyr (≥ 1.0), R (≥ 3.5.0)
Suggests: testthat
Published: 2022-03-29
Author: Michael Lingzhi Li [aut, cre], Kosuke Imai [aut]
Maintainer: Michael Lingzhi Li <mlli at mit.edu>
BugReports: https://github.com/MichaelLLi/evalITR/issues
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://github.com/MichaelLLi/evalITR
NeedsCompilation: no
Materials: README NEWS
In views: CausalInference
CRAN checks: evalITR results


Reference manual: evalITR.pdf


Package source: evalITR_0.3.0.tar.gz
Windows binaries: r-devel: evalITR_0.3.0.zip, r-release: evalITR_0.3.0.zip, r-oldrel: evalITR_0.3.0.zip
macOS binaries: r-release (arm64): evalITR_0.3.0.tgz, r-oldrel (arm64): evalITR_0.3.0.tgz, r-release (x86_64): evalITR_0.3.0.tgz, r-oldrel (x86_64): evalITR_0.3.0.tgz
Old sources: evalITR archive


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