Learning Algorithms for Dynamic Treatment Regimes
Overview
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.
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- 14includes a package overview page
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Maintainer since 1.0 (2015)
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- RR 3.6.0 released · 2019-04-26
- archivedRemoved from CRAN2019-04-22check errors were not corrected despite reminders
- RR 3.5.0 released · 2018-04-23
- 1.32018-04-05 · diff ↗
- RR 3.4.0 released · 2017-04-21
- RR 3.3.0 released · 2016-05-03
- 1.22015-12-28 · diff ↗
- 1.12015-10-28 · diff ↗
- 1.02015-10-23
- RR 3.2.0 released · 2015-04-16
Package metadata
- First published
- 2015-10-23
- Total releases
- 4 / 11 yrs
- License
- GPL-2 OSI
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