Package: BayesRegDTR
Type: Package
Title: Bayesian Regression for Dynamic Treatment Regimes
Version: 1.1.2
Authors@R: c(
    person(
      "Jeremy", "Lim",
      email = "jeremylim23@gmail.com",
      role = c("aut")
    ),
    person(
      "Weichang", "Yu",
      email = "weichang.yu@unimelb.edu.au",
      role = c("aut", "cre"), 
      comment = c(ORCID = "0000-0002-0399-3779")
    )
  )
Description: 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.
License: GPL (>= 3)
Imports: Rcpp (>= 1.0.13-1), mvtnorm, foreach, progressr, stats, future
Depends: doRNG
Suggests: cli, testthat (>= 3.0.0), doFuture
LinkingTo: Rcpp, RcppArmadillo
Encoding: UTF-8
RoxygenNote: 7.3.3
URL: https://github.com/jlimrasc/BayesRegDTR
BugReports: https://github.com/jlimrasc/BayesRegDTR/issues
Config/testthat/edition: 3
NeedsCompilation: yes
Packaged: 2026-10-23 10:21:56 UTC; root
Author: Jeremy Lim [aut], Weichang Yu [aut, cre] (ORCID:
        <https://orcid.org/0000-0002-0399-3779>)
Maintainer: Weichang Yu <weichang.yu@unimelb.edu.au>
Repository: https://cran.r-universe.dev
Date/Publication: 2025-11-27 09:10:15 UTC
RemoteUrl: https://github.com/cran/BayesRegDTR
RemoteRef: HEAD
RemoteSha: fca5b434cab330af9fee1ca5bfe9a33645677bf2