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
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 + file LICENSE
URL:	https://rich-payne.github.io/brisk/
NeedsCompilation:	no
Materials:	README
CRAN checks:	brisk results

Documentation:

Reference manual:	brisk.pdf
Vignettes:	Random Weights

Downloads:

Package source:	brisk_0.1.0.tar.gz
Windows binaries:	r-devel: brisk_0.1.0.zip, r-release: brisk_0.1.0.zip, r-oldrel: brisk_0.1.0.zip
macOS binaries:	r-release (arm64): brisk_0.1.0.tgz, r-oldrel (arm64): brisk_0.1.0.tgz, r-release (x86_64): brisk_0.1.0.tgz, r-oldrel (x86_64): brisk_0.1.0.tgz

Linking:

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