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BayesRegDTR

Bayesian Regression for Dynamic Treatment Regimes

v1.1.2 · Nov 27, 2025 · GPL (>= 3)

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.

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r-devel-linux-x86_64-debian-clang OK
r-devel-linux-x86_64-debian-gcc OK
r-devel-linux-x86_64-fedora-clang OK
r-devel-linux-x86_64-fedora-gcc OK
r-devel-macos-arm64 OK
r-devel-windows-x86_64 OK
r-oldrel-macos-arm64 OK
r-oldrel-macos-x86_64 OK
r-oldrel-windows-x86_64 OK
r-patched-linux-x86_64 OK
r-release-linux-x86_64 OK
r-release-macos-arm64 OK
r-release-macos-x86_64 OK
r-release-windows-x86_64 OK

Additional Issues

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Check History

OK 14 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE Mar 10, 2026

Dependency Network

Dependencies Reverse dependencies doRNG Rcpp mvtnorm foreach progressr future BayesRegDTR

Version History

new 1.1.2 Mar 10, 2026
updated 1.1.2 ← 1.1.1 diff Nov 26, 2025
updated 1.1.1 ← 1.0.1 diff Oct 25, 2025
new 1.0.1 Jun 26, 2025