BayesRegDTR-package {BayesRegDTR} | R Documentation |
BayesRegDTR: Bayesian Regression for Dynamic Treatment Regimes
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.
Author(s)
Maintainer: Jeremy Lim jeremylim23@gmail.com
Authors:
Weichang Yu weichang.yu@unimelb.edu.au (ORCID)
References
Yu, W., & Bondell, H. D. (2023), “Bayesian Likelihood-Based Regression for Estimation of Optimal Dynamic Treatment Regimes”, Journal of the Royal Statistical Society Series B: Statistical Methodology, 85(3), 551-574. doi:10.1093/jrsssb/qkad016
See Also
generate_dataset()
for generating a toy dataset to test the model fitting on
BayesLinRegDTR.model.fit()
for obtaining an estimated posterior
distribution of the optimal treatment option at a user-specified prediction stage
Useful links:
Report bugs at https://github.com/jlimrasc/BayesRegDTR/issues