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BayesRegDTR
View on CRAN: Click
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Download and install BayesRegDTR package within the R console
Install from CRAN:
install.packages("BayesRegDTR")
Install from Github:
library("remotes")
install_github("cran/BayesRegDTR") Install by package version:
library("remotes")
install_version("BayesRegDTR", "1.1.2") Attach the package and use:
library("BayesRegDTR")
Maintained by
Jeremy Lim
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[Scholar Profile | Author Map]
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First Published: 2025-06-27
Latest Update: 2025-06-27
Description:
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.
How to cite:
Jeremy Lim (2025). BayesRegDTR: Bayesian Regression for Dynamic Treatment Regimes. R package version 1.1.2, https://cran.r-project.org/web/packages/BayesRegDTR. Accessed 30 Oct. 2026.
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