DynTxRegime: Methods for Estimating Optimal Dynamic Treatment Regimes

Methods to estimate dynamic treatment regimes using Interactive Q-Learning, Q-Learning, weighted learning, and value-search methods based on Augmented Inverse Probability Weighted Estimators and Inverse Probability Weighted Estimators. Dynamic Treatment Regimes: Statistical Methods for Precision Medicine, Tsiatis, A. A., Davidian, M. D., Holloway, S. T., and Laber, E. B., Chapman & Hall/CRC Press, 2020, ISBN:978-1-4987-6977-8.

Version: 4.7
Depends: methods, modelObj, stats
Imports: kernlab, rgenoud, dfoptim
Suggests: MASS, rpart, nnet
Published: 2020-07-22
Author: S. T. Holloway, E. B. Laber, K. A. Linn, B. Zhang, M. Davidian, and A. A. Tsiatis
Maintainer: Shannon T. Holloway <sthollow at ncsu.edu>
License: GPL-2
NeedsCompilation: no
Materials: NEWS
CRAN checks: DynTxRegime results


Reference manual: DynTxRegime.pdf
Package source: DynTxRegime_4.7.tar.gz
Windows binaries: r-devel: DynTxRegime_4.7.zip, r-release: DynTxRegime_4.7.zip, r-oldrel: DynTxRegime_4.7.zip
macOS binaries: r-release: DynTxRegime_4.7.tgz, r-oldrel: DynTxRegime_4.7.tgz
Old sources: DynTxRegime archive

Reverse dependencies:

Reverse imports: DevTreatRules


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