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:Jeremy Lim [aut], Weichang Yu [aut, cre]

BayesRegDTR_1.1.2.tar.gz
BayesRegDTR_1.1.2.zip(r-4.7)BayesRegDTR_1.1.2.zip(r-4.6)BayesRegDTR_1.1.2.zip(r-4.5)
BayesRegDTR_1.1.2.tgz(r-4.6-x86_64)BayesRegDTR_1.1.2.tgz(r-4.6-arm64)BayesRegDTR_1.1.2.tgz(r-4.5-x86_64)BayesRegDTR_1.1.2.tgz(r-4.5-arm64)
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://jlimrasc.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/jlimrasc/bayesregdtr/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library

On CRAN:

Conda:

openblascppopenmp

3.00 score 2 scripts 175 downloads 2 exports 14 dependencies

Last updated from:7668c9f4e7. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK139
linux-devel-x86_64OK155
source / vignettesOK173
linux-release-arm64OK122
linux-release-x86_64OK145
macos-release-arm64OK187
macos-release-x86_64OK293
macos-oldrel-arm64OK177
macos-oldrel-x86_64OK275
windows-develOK117
windows-releaseOK107
windows-oldrelOK138
wasm-releaseOK111

Exports:BayesLinRegDTR.model.fitgenerate_dataset

Dependencies:codetoolsdigestdoRNGforeachfutureglobalsiteratorslistenvmvtnormparallellyprogressrRcppRcppArmadillorngtools