richardbayes.r-universe.dev

[rich-payne] brisk 0.1.1

paynestatistics@gmail.com (Richard Payne) — Tue, 03 Mar 2026 18:39:20 GMT

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) . 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.

[rich-payne] dreamer 3.2.0.9000

paynestatistics@gmail.com (Richard Daniel Payne) — Thu, 19 Dec 2024 14:40:20 GMT

Fits dose-response models utilizing a Bayesian model averaging approach as outlined in Gould (2019) for both continuous and binary responses. Longitudinal dose-response modeling is also supported in a Bayesian model averaging framework as outlined in Payne, Ray, and Thomann (2024) . Functions for plotting and calculating various posterior quantities (e.g. posterior mean, quantiles, probability of minimum efficacious dose, etc.) are also implemented. Copyright Eli Lilly and Company (2019).

[rich-payne] yodel 1.0.0

paynestatistics@gmail.com (Richard Payne) — Fri, 12 Apr 2024 18:59:57 GMT

Provides helper functions to perform Bayesian model averaging using Markov chain Monte Carlo samples from separate models. Calculates weights and obtains draws from the model-averaged posterior for quantities of interest specified by the user. Weight calculations can be done using marginal likelihoods or log-predictive likelihoods as in Ando, T., & Tsay, R. (2010) .