Package: SMARTbayesR 2.0.0

SMARTbayesR: Bayesian Set of Best Dynamic Treatment Regimes and Sample Size in SMARTs for Binary Outcomes

Permits determination of a set of optimal dynamic treatment regimes and sample size for a SMART design in the Bayesian setting with binary outcomes. Please see Artman (2020) <arxiv:2008.02341>.

Authors:William Artman [aut, cre]

SMARTbayesR_2.0.0.tar.gz
SMARTbayesR_2.0.0.zip(r-4.7)SMARTbayesR_2.0.0.zip(r-4.6)SMARTbayesR_2.0.0.zip(r-4.5)
SMARTbayesR_2.0.0.tgz(r-4.6-any)SMARTbayesR_2.0.0.tgz(r-4.5-any)
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SMARTbayesR_2.0.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
SMARTbayesR/json (API)

# Install 'SMARTbayesR' in R:
install.packages('SMARTbayesR', repos = c('https://wilart.r-universe.dev', 'https://cloud.r-project.org'))

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

2.00 score 3 scripts 214 downloads 8 exports 1 dependencies

Last updated from:b247e7a065. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK101
source / vignettesOK379
linux-release-x86_64OK114
macos-release-arm64OK173
macos-oldrel-arm64OK138
windows-develOK66
windows-releaseOK76
windows-oldrelOK67
wasm-releaseOK96

Exports:LogORLogRRMCBUpperLimitsPosteriorEDTRProbsPosteriorTrtSeqProbPowerBayesianRDSimDesign1

Dependencies:LaplacesDemon

SMARTbayesR

William J Artman

Rendered fromSMARTbayesR.Rmdusingknitr::rmarkdownon May 18 2026.

Last update: 2021-09-30
Started: 2021-09-30