Package: BayesRegDTR 1.1.2
Weichang Yu
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.
Authors:
BayesRegDTR_1.1.2.tar.gz
BayesRegDTR_1.1.2.tar.gz(r-4.7-arm64)BayesRegDTR_1.1.2.tar.gz(r-4.7-x86_64)BayesRegDTR_1.1.2.tar.gz(r-4.6-arm64)BayesRegDTR_1.1.2.tar.gz(r-4.6-x86_64)
BayesRegDTR_1.1.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
BayesRegDTR/json (API)
NEWS
| # Install 'BayesRegDTR' in R: |
| install.packages('BayesRegDTR', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/jlimrasc/bayesregdtr/issues
Last updated from:fca5b434ca. Checks:6 OK. Indexed: no.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 131 | ||
| linux-devel-x86_64 | OK | 133 | ||
| source / vignettes | OK | 185 | ||
| linux-release-arm64 | OK | 125 | ||
| linux-release-x86_64 | OK | 127 | ||
| wasm-release | OK | 110 |
Exports:BayesLinRegDTR.model.fitgenerate_dataset
Dependencies:codetoolsdigestdoRNGforeachfutureglobalsiteratorslistenvmvtnormparallellyprogressrRcppRcppArmadillorngtools
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| BayesRegDTR: Bayesian Regression for Dynamic Treatment Regimes | BayesRegDTR-package BayesRegDTR |
| Main function for fitting a Bayesian likelihood-based linear regression model | BayesLinRegDTR.model.fit |
| Generate a toy dataset in the right format for testing BayesLinRegDTR.model.fit | generate_dataset |
