Bayesian Modeling in R

What problem does it solve?

This Skill enables practitioners to build and evaluate Bayesian models in R using brms and rstanarm, providing robust uncertainty quantification beyond traditional methods.

Core Features & Use Cases

• Prior specification & posterior analysis: define priors, fit models, and interpret posterior distributions.
• Model comparison & checks: perform LOO/WAIC, Bayes factors, and posterior predictive checks to compare models.
• Flexible modeling capabilities: handle linear, generalized, mixed-effects, non-linear, and survival-type models with Stan backends.
• Use Case: ecological studies with hierarchical data and small sample sizes can benefit from full Bayesian inference to quantify uncertainty.

Quick Start

Install and load brms and rstanarm, fit a simple Gaussian model with two predictors using brm, then summarize the posterior draws.