brisk: Bayesian Benefit Risk Analysis
Quantitative methods for benefit-risk analysis help to condense complex decisions into a univariate metric describing the overall benefit relative to risk. One approach is to use the multi-criteria decision analysis framework (MCDA), as in Mussen, Salek, and Walker (2007) <doi:10.1002/pds.1435>. Bayesian benefit-risk analysis incorporates uncertainty through posterior distributions which are inputs to the benefit-risk framework. The brisk package provides functions to assist with Bayesian benefit-risk analyses, such as MCDA. Users input posterior samples, utility functions, weights, and the package outputs quantitative benefit-risk scores. The posterior of the benefit-risk scores for each group can be compared. Some plotting capabilities are also included.
| Version: | 0.1.0 |
| Imports: | dplyr (≥ 1.0),ellipsis (≥ 0.3),ggplot2 (≥ 3.3),hitandrun (≥ 0.5),purrr (≥ 0.3),rlang (≥ 1.0),tidyr (≥ 1.1) |
| Suggests: | knitr,fs (≥ 1.5),testthat (≥ 3.0.0),tibble (≥ 3.1),rmarkdown |
| Published: | 2022-08-31 |
| DOI: | 10.32614/CRAN.package.brisk |
| Author: | Richard Payne [aut, cre], Sai Dharmarajan [rev], Eli Lilly and Company [cph] |
| Maintainer: | Richard Payne <paynestatistics at gmail.com> |
| BugReports: | https://github.com/rich-payne/brisk/issues |
| License: | MIT + fileLICENSE |
| URL: | https://rich-payne.github.io/brisk/ |
| NeedsCompilation: | no |
| Materials: | README |
| CRAN checks: | brisk results |
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