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.
Version: 1.0.1 Depends: doRNG Imports: Rcpp (≥ 1.0.13-1), mvtnorm, foreach, progressr, stats, future LinkingTo: Rcpp, RcppArmadillo Suggests: cli, testthat (≥ 3.0.0), doFuture Published: 2025-06-27 DOI: 10.32614/CRAN.package.BayesRegDTR Author: Jeremy Lim [aut, cre], Weichang Yu [aut] Maintainer: Jeremy Lim <jeremylim23 at gmail.com> BugReports: https://github.com/jlimrasc/BayesRegDTR/issues License: GPL (≥ 3) URL: https://github.com/jlimrasc/BayesRegDTR NeedsCompilation: yes Materials: README NEWS CRAN checks: BayesRegDTR results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=BayesRegDTR to link to this page.
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