ITRLearn: Statistical Learning for Individualized Treatment Regime

Maximin-projection learning (MPL, Shi, et al., 2018) is implemented for recommending a meaningful and reliable individualized treatment regime for future groups of patients based on the observed data from different populations with heterogeneity in individualized decision making. Q-learning and A-learning are implemented for estimating the groupwise contrast function that shares the same marginal treatment effects. The packages contains classical Q-learning and A-learning algorithms for a single stage study as a byproduct. More functions will be added at later versions.

Version: 1.0-1
Imports: Formula, kernlab
Published: 2018-11-15
Author: Chengchun Shi, Rui Song, Wenbin Lu and Bo Fu
Maintainer: Chengchun Shi <cshi4 at ncsu.edu>
License: GPL-2
NeedsCompilation: yes
Citation: ITRLearn citation info
CRAN checks: ITRLearn results

Downloads:

Reference manual: ITRLearn.pdf
Package source: ITRLearn_1.0-1.tar.gz
Windows binaries: r-devel: ITRLearn_1.0-1.zip, r-devel-gcc8: ITRLearn_1.0-1.zip, r-release: ITRLearn_1.0-1.zip, r-oldrel: ITRLearn_1.0-1.zip
OS X binaries: r-release: ITRLearn_1.0-1.tgz, r-oldrel: ITRLearn_1.0-1.tgz
Old sources: ITRLearn archive

Linking:

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