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 |
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