DTRlearn: Learning Algorithms for Dynamic Treatment Regimes
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.
| Version: |
1.2 |
| Depends: |
kernlab, MASS, glmnet, ggplot2 |
| Published: |
2015-12-28 |
| Author: |
Ying Liu, Yuanjia Wang, Donglin Zeng |
| Maintainer: |
Ying Liu <yl2802 at cumc.columbia.edu> |
| License: |
GPL-2 |
| NeedsCompilation: |
no |
| CRAN checks: |
DTRlearn results |
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