Summary | Workflow: analysis | Simulated data analysis | Power analysis | Relative risk | Risk difference | Design-3 SMART | References
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:
SMARTbayesR_2.0.0.tar.gz
SMARTbayesR_2.0.0.zip(r-4.7-any)SMARTbayesR_2.0.0.zip(r-4.6-any)SMARTbayesR_2.0.0.zip(r-4.5-any)
SMARTbayesR_2.0.0.tgz(r-4.6-any)SMARTbayesR_2.0.0.tgz(r-4.5-any)
SMARTbayesR_2.0.0.tar.gz(r-4.7-any)SMARTbayesR_2.0.0.tar.gz(r-4.6-any)
SMARTbayesR_2.0.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
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')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated from:b247e7a065. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel | OK | 146 | ||
| source / vignettes | OK | 258 | ||
| linux-release | OK | 94 | ||
| macos-release | OK | 194 | ||
| macos-oldrel | OK | 208 | ||
| windows-devel | OK | 77 | ||
| windows-release | OK | 66 | ||
| windows-oldrel | OK | 73 | ||
| wasm-release | OK | 80 |
Exports:LogORLogRRMCBUpperLimitsPosteriorEDTRProbsPosteriorTrtSeqProbPowerBayesianRDSimDesign1
Dependencies:LaplacesDemon
