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]

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SMARTbayesR/json (API)

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

Peer review:

On CRAN:

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

2.00 score 2 scripts 196 downloads 8 exports 1 dependencies

Last updated 3 years agofrom:b247e7a065. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 21 2024
R-4.5-winOKNov 21 2024
R-4.5-linuxOKNov 21 2024
R-4.4-winOKNov 21 2024
R-4.4-macOKNov 21 2024
R-4.3-winOKNov 21 2024
R-4.3-macOKNov 21 2024

Exports:LogORLogRRMCBUpperLimitsPosteriorEDTRProbsPosteriorTrtSeqProbPowerBayesianRDSimDesign1

Dependencies:LaplacesDemon

SMARTbayesR

William J Artman

Rendered fromSMARTbayesR.Rmdusingknitr::rmarkdownon Nov 21 2024.

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