The background Results from Bayesian analyses are often the result of many hours of study design, data processing, model construction, evaluation, checking, and validation (see here for an excellent overview of a typical Bayesian workflow). Because model building/fitting/checking is an … Continue reading →

MCMCvis version 0.13.1 is now on CRAN, complete with new features for summarizing and visualizing MCMC output. See HERE and HERE for posts that cover general package features. Highlights since the last CRAN version include: ​MCMCsummary – Specify quantiles for … Continue reading →

When fitting a Bayesian model using MCMC (often via JAGS/BUGS/Stan), a number of checks are typically performed to make sure your model is worth interpreting without further manipulation (remember: all models are wrong, some are useful!): R-hat (AKA Gelman-Rubin statistic) … Continue reading →

Model results can be thought of as a reward for the many hours of model design, troubleshooting, re-design, etc. that analyses often require. Following the potentially exhausting mental exercise to acquire these results, I think we’d all like the interpretation … Continue reading →

MCMCvis, an R package for visualizing, manipulating, and summarizing MCMC output is now available on CRAN! It can be installed with: install.packages('MCMCvis') The vignette (tutorial) can be run with: vignette('MCMCvis') MCMCvis was designed to perform key functions for MCMC analysis … Continue reading →