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 |
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