evalITR: Evaluating Individualized Treatment Rules

A collection of statistical methods for evaluating individualized treatment rules under randomized data. The provided metrics include PAV (Population Average Value), PAPE (Population Average Prescription Effect), and AUPEC (Area Under Prescription Effect Curve). It also provides the tools to analyze individualized treatment rules under budget constraints. Imai and Li (2019) <arXiv:1905.05389>.

Version: 0.1.0
Depends: stats, R (≥ 3.5.0)
Suggests: testthat
Published: 2020-02-20
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
CRAN checks: evalITR results


Reference manual: evalITR.pdf
Package source: evalITR_0.1.0.tar.gz
Windows binaries: r-devel: evalITR_0.1.0.zip, r-release: evalITR_0.1.0.zip, r-oldrel: evalITR_0.1.0.zip
macOS binaries: r-release: evalITR_0.1.0.tgz, r-oldrel: evalITR_0.1.0.tgz


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