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.
|Author||Chengchun Shi, Rui Song, Wenbin Lu and Bo Fu|
|Maintainer||Chengchun Shi <firstname.lastname@example.org>|
|Package repository||View on CRAN|
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