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 |
| Reference manual: | ITRLearn.pdf |
| Package source: | ITRLearn_1.0-1.tar.gz |
| Windows binaries: | r-devel: ITRLearn_1.0-1.zip, r-release: ITRLearn_1.0-1.zip, r-oldrel: ITRLearn_1.0-1.zip |
| macOS binaries: | r-release: ITRLearn_1.0-1.tgz, r-oldrel: ITRLearn_1.0-1.tgz |
| Old sources: | ITRLearn archive |
Please use the canonical form https://CRAN.R-project.org/package=ITRLearn to link to this page.