Bayesian inference in R

What problem does it solve?

This Skill enables data scientists and researchers to perform Bayesian inference in R using brms, providing a structured workflow from model specification to posterior analysis, priors, and diagnostics.

Core Features & Use Cases

• Brms modeling: Fit multilevel and complex hierarchical models using brms with a CmdStanR backend.
• DAG validation: Validate causal structures with dagitty and ggdag to assess identifiability and adjustment sets.
• Marginal effects & visualization: Compute marginal effects, derive posterior summaries, and visualize results for interpretation.
• Use Case: Compare competing models in hierarchical experiments or observational studies to infer credible effects and perform model diagnostics.

Quick Start

brm(formula = outcome ~ predictor + (1 | group), data = data, family = gaussian()) summary(model)