Dynamic treatment regimens (DTRs) are sequential decision rules tailored at each stage by time-varying subject-specific features and intermediate outcomes observed in previous stages. This package implements three methods: O-learning (Zhao et. al. 2012,2014), Q-learning (Murphy et. al. 2007; Zhao et.al. 2009) and P-learning (Liu et. al. 2014, 2015) to estimate the optimal DTRs.
|Depends:||kernlab, MASS, glmnet, ggplot2|
|Author:||Ying Liu, Yuanjia Wang, Donglin Zeng|
|Maintainer:||Ying Liu <yl2802 at cumc.columbia.edu>|
|CRAN checks:||DTRlearn results|
|Windows binaries:||r-devel: DTRlearn_1.2.zip, r-release: DTRlearn_1.2.zip, r-oldrel: DTRlearn_1.2.zip|
|OS X El Capitan binaries:||r-release: DTRlearn_1.2.tgz|
|OS X Mavericks binaries:||r-oldrel: DTRlearn_1.2.tgz|
|Old sources:||DTRlearn archive|
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