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BayesRegDTR  

Bayesian Regression for Dynamic Treatment Regimes
View on CRAN: Click here


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
[Scholar Profile | Author Map]
All associated links for this package
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 26 Aug. 2026.
Previous versions and publish date:
1.0.1 (2025-06-27 15:20), 1.1.1 (2025-10-26 19:30), (2026-07-09 07:58)
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Complete documentation for BayesRegDTR
Functions, R codes and Examples using the BayesRegDTR R package
Full BayesRegDTR package functions and examples
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