Methods for Estimating Optimal Dynamic Treatment Regimes R package DynTxRegime version 4.16 CRAN Task View: CausalInference (https://CRAN.R-project.org/view=CausalInference) 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. Software Depends: methods, modelObj, stats Imports: kernlab, rgenoud, dfoptim Suggests: MASS, rpart, nnet Shannon T. Holloway <shannon.t.holloway@gmail.com> Comprehensive R Archive Network (CRAN) Shannon T. Holloway [aut, cre], E. B. Laber [aut], K. A. Linn [aut], B. Zhang [aut], M. Davidian [aut], A. A. Tsiatis [aut] GPL-2 2025-05-03 application/tgz https://CRAN.R-project.org/package=DynTxRegime doi:10.32614/CRAN.package.DynTxRegime