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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.
Getting started
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Package details |
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| Author | Jeremy Lim [aut, cre], Weichang Yu [aut] (ORCID: <https://orcid.org/0000-0002-0399-3779>) |
| Maintainer | Jeremy Lim <jeremylim23@gmail.com> |
| License | GPL (>= 3) |
| Version | 1.0.1 |
| URL | https://github.com/jlimrasc/BayesRegDTR |
| Package repository | View on CRAN |
| Installation |
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