The Bernadette package - Bayesian inference and model selection for stochastic epidemics

CRAN: https://CRAN.R-project.org/package=Bernadette

The Bernadette package for R provides a framework for Bayesian analysis of infectious disease transmission dynamics via diffusion driven multi-type epidemic models with time-varying epidemiological parameters, with a particular focus on Coronavirus Disease 2019 (COVID-19). It uses the Stan probabilistic programming language and implements the Bayesian epidemic model described in Bouranis et al. For models fit using Markov chain Monte Carlo, it allows for computation of approximate leave-one-out cross-validation (LOO, LOOIC) or the Widely Applicable Information Criterion (WAIC) for model checking and comparison.

The Github page for this package contains a detailed README file with a description of the modeling framework and the steps involved in the workflow.

The Bergm package - Bayesian exponential random graph models

CRAN: https://CRAN.R-project.org/package=Bergm

The Bergm package for R provides a comprehensive framework for Bayesian parameter estimation and model selection for exponential random graph models using various algorithms. It can also supply graphical Bayesian goodness-of-fit procedures that address the issue of model adequacy. See also Alberto Caimo’s webpage.