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-x86_64)BayesRegDTR_1.1.2.zip(r-4.7-arm64)BayesRegDTR_1.1.2.zip(r-4.6-x86_64)BayesRegDTR_1.1.2.zip(r-4.6-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✨
DESCRIPTION |NEWS
card.svg |card.png
BayesRegDTR/json (API)

# 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

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

On CRAN:

Conda:

openblascppopenmp

2.00 score 2 scripts 312 downloads 2 exports 14 dependencies

Last updated from:fca5b434ca. Checks:10 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-arm64OK92
linux-devel-x86_64OK145
source / vignettesOK334
linux-release-arm64OK133
linux-release-x86_64OK132
windows-devel-arm64OK219
windows-devel-x86_64OK138
windows-release-arm64OK238
windows-release-x86_64OK142
wasm-releaseOK96

Exports:BayesLinRegDTR.model.fitgenerate_dataset

Dependencies:codetoolsdigestdoRNGforeachfutureglobalsiteratorslistenvmvtnormparallellyprogressrRcppRcppArmadillorngtools