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: 4.16 Depends: methods, modelObj, stats Imports: kernlab, rgenoud, dfoptim Suggests: MASS, rpart, nnet Published: 2025-05-03 DOI: 10.32614/CRAN.package.DynTxRegime Author: Shannon T. Holloway [aut, cre], E. B. Laber [aut], K. A. Linn [aut], B. Zhang [aut], M. Davidian [aut], A. A. Tsiatis [aut] 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: Downloads: Reverse dependencies: Linking:Please use the canonical form https://CRAN.R-project.org/package=DynTxRegime to link to this page.
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