Bayesian Regression for Dynamic Treatment Regimes R package BayesRegDTR version 1.1.2 Methods to estimate optimal dynamic treatment regimes using Bayesian likelihood-based regression approach as described in Yu, W., & Bondell, H. D. (2023) <doi:10.1093/jrsssb/qkad016> Uses backward induction and dynamic programming theory for computing expected values. Offers options for future parallel computing. Software Depends: doRNG Imports: Rcpp (>= 1.0.13-1), mvtnorm, foreach, progressr, stats, future LinkingTo: Rcpp, RcppArmadillo Suggests: cli, testthat (>= 3.0.0), doFuture Weichang Yu <weichang.yu@unimelb.edu.au> Comprehensive R Archive Network (CRAN) Jeremy Lim [aut], Weichang Yu [aut, cre] (ORCID: <https://orcid.org/0000-0002-0399-3779>) GPL (>= 3) 2025-11-27 application/tgz https://CRAN.R-project.org/package=BayesRegDTR doi:10.32614/CRAN.package.BayesRegDTR