BayesRegDTR: Bayesian Regression for Dynamic Treatment Regimes

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 ORCID iD [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:

Reference manual: BayesRegDTR.html , BayesRegDTR.pdf

Downloads:

Package source: BayesRegDTR_1.0.1.tar.gz
Windows binaries: r-devel: BayesRegDTR_1.0.1.zip, r-release: BayesRegDTR_1.0.1.zip, r-oldrel: BayesRegDTR_1.0.1.zip
macOS binaries: r-release (arm64): BayesRegDTR_1.0.1.tgz, r-oldrel (arm64): BayesRegDTR_1.0.1.tgz, r-release (x86_64): BayesRegDTR_1.0.1.tgz, r-oldrel (x86_64): BayesRegDTR_1.0.1.tgz

Linking:

Please use the canonical form https://CRAN.R-project.org/package=BayesRegDTR to link to this page.