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.
Package details |
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Author | Chengchun Shi, Rui Song, Wenbin Lu and Bo Fu |
Maintainer | Chengchun Shi <cshi4@ncsu.edu> |
License | GPL-2 |
Version | 1.0-1 |
Package repository | View on CRAN |
Installation |
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