Bayesian Set of Best Dynamic Treatment Regimes and Sample Size in SMARTs for Binary Outcomes
Overview
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.
Install
Health
NOTECRAN incoming feasibilityr-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc
Maintainer: ‘William Artman <William_Artman@URMC.Rochester.edu>’ The Description field contains outcomes. Please see Artman (2020) <arXiv:2008.02341>. Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.
- NOTE11 OK, 2 NOTE2026-09-08
- ERROR10 OK, 2 NOTE, 1 ERROR2026-09-07
- NOTE12 OK, 2 NOTE2026-03-10
Documentation
- Help pages
- 9includes a package overview page
- Examples that run
- 80%4 run in a plain check · 1 only when donttest examples are enabled (R CMD check --as-cran)
- Exported functions with examples
- 63%
- Documented parameters
- 100%
- Return-value docs
- 100%
- Help pages citing references
- 0%
- SMARTBayesR HTML
Downloads
Dependencies
Installing SMARTbayesR adds 1 package (0 with compiled code, 2.0 MB of source tarballs). Heaviest: LaplacesDemon 2.0 MB.
Code & Tests
People & History
Package metadata
- First published
- 2020-11-04
- Total releases
- 4 / 6 yrs
- License
- GPL-3 OSI
- Download size
- 23 KB
Cite
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