r-bayes

What problem does it solve?

This Skill streamlines Bayesian statistical modeling and causal inference in R, making complex analyses more accessible and reproducible.

Core Features & Use Cases

• Causal DAGs: Define, validate, and visualize Directed Acyclic Graphs for causal inference.
• Bayesian Modeling: Fit multilevel models using brms with explicit prior specification and robust diagnostics.
• Marginal Effects: Extract and interpret model predictions and effects using marginaleffects.
• Use Case: Analyze longitudinal survey data to understand the causal impact of an intervention, accounting for individual differences and temporal dynamics.

Quick Start

Use the r-bayes skill to fit a multilevel Bayesian model predicting outcome from predictor1 and predictor2 using the brms package.