Bayesian Benefit Risk Analysis R package brisk version 0.1.1 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. Software Imports: dplyr (>= 1.2), 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 Richard Payne <paynestatistics@gmail.com> Comprehensive R Archive Network (CRAN) Richard Payne [aut, cre], Sai Dharmarajan [rev], Eli Lilly and Company [cph] MIT + file LICENSE (https://CRAN.R-project.org/package=brisk/LICENSE) 2026-03-04 application/tgz https://CRAN.R-project.org/package=brisk doi:10.32614/CRAN.package.brisk