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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 |
| 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.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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