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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