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:	4.10
Depends:	methods, modelObj, stats
Imports:	kernlab, rgenoud, dfoptim
Suggests:	MASS, rpart, nnet
Published:	2022-06-07
Author:	S. T. Holloway, E. B. Laber, K. A. Linn, B. Zhang, M. Davidian, and A. A. Tsiatis
Maintainer:	Shannon T. Holloway <shannon.t.holloway at gmail.com>
License:	GPL-2
NeedsCompilation:	no
Materials:	NEWS
In views:	CausalInference
CRAN checks:	DynTxRegime results

Documentation:

Reference manual:

DynTxRegime.pdf

Downloads:

Package source:	DynTxRegime_4.10.tar.gz
Windows binaries:	r-devel: DynTxRegime_4.10.zip, r-release: DynTxRegime_4.10.zip, r-oldrel: DynTxRegime_4.10.zip
macOS binaries:	r-release (arm64): DynTxRegime_4.10.tgz, r-oldrel (arm64): DynTxRegime_4.10.tgz, r-release (x86_64): DynTxRegime_4.10.tgz, r-oldrel (x86_64): DynTxRegime_4.10.tgz
Old sources:	DynTxRegime archive

Reverse dependencies:

Reverse imports:

DevTreatRules

Linking:

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