BayesRegDTR1.1.2 package

Bayesian Regression for Dynamic Treatment Regimes

BayesLinRegDTR.model.fit

Main function for fitting a Bayesian likelihood-based linear regressio...

BayesRegDTR-package

BayesRegDTR: Bayesian Regression for Dynamic Treatment Regimes

compute_MC_draws_mvt

Compute Monte Carlo Draws from Multivariate Dataset

compute_MC_draws_uvt

Compute Monte Carlo Draws from Univariate Dataset

generate_dataset_mvt

Generate Multivariate dataset

generate_dataset_uvt

Generate Univariate Dataset

generate_dataset

Generate a toy dataset in the right format for testing BayesLinRegDTR....

Download source packageRead PDF manual

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.

  • Maintainer: Weichang Yu
  • License: GPL (>= 3)
  • Last published: 2025-11-27

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