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.

Package details

AuthorChengchun Shi, Rui Song, Wenbin Lu and Bo Fu
MaintainerChengchun Shi <[email protected]>
Package repositoryView on CRAN
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ITRLearn documentation built on May 2, 2019, 11:03 a.m.