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>
License: GPL-2
NeedsCompilation: yes
Citation: ITRLearn citation info
In views: CausalInference
CRAN checks: ITRLearn results


Reference manual: ITRLearn.pdf


Package source: ITRLearn_1.0-1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): ITRLearn_1.0-1.tgz, r-oldrel (arm64): ITRLearn_1.0-1.tgz, r-release (x86_64): ITRLearn_1.0-1.tgz, r-oldrel (x86_64): ITRLearn_1.0-1.tgz
Old sources: ITRLearn archive


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