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: |
1.1.2 |
| Depends: |
doRNG |
| Imports: |
Rcpp (≥ 1.0.13-1), mvtnorm, foreach, progressr, stats, future |
| LinkingTo: |
Rcpp, RcppArmadillo |
| Suggests: |
cli, testthat (≥ 3.0.0), doFuture |
| Published: |
2025-11-27 |
| DOI: |
10.32614/CRAN.package.BayesRegDTR |
| Author: |
Jeremy Lim [aut],
Weichang Yu  [aut,
cre] |
| Maintainer: |
Weichang Yu <weichang.yu at unimelb.edu.au> |
| 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:
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