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BayesRegDTR

Bayesian Regression for Dynamic Treatment Regimes

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.

Versions across snapshots

VersionRepositoryFileSize
1.1.2 rolling linux/jammy R-4.5 BayesRegDTR_1.1.2.tar.gz 275.3 KiB
1.1.2 rolling linux/noble R-4.5 BayesRegDTR_1.1.2.tar.gz 276.5 KiB
1.1.2 rolling source/ R- BayesRegDTR_1.1.2.tar.gz 189.8 KiB
1.1.2 latest linux/jammy R-4.5 BayesRegDTR_1.1.2.tar.gz 275.3 KiB
1.1.2 latest linux/noble R-4.5 BayesRegDTR_1.1.2.tar.gz 276.5 KiB
1.1.2 latest source/ R- BayesRegDTR_1.1.2.tar.gz 189.8 KiB
1.1.2 2026-04-26 source/ R- BayesRegDTR_1.1.2.tar.gz 189.8 KiB
1.1.2 2026-04-23 source/ R- BayesRegDTR_1.1.2.tar.gz 189.8 KiB
1.1.2 2026-04-09 windows/windows R-4.5 BayesRegDTR_1.1.2.zip 685.5 KiB

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