Performs advanced Bayesian statistical modeling and causal inference using the brms and Stan ecosystem in R.
This skill equips Claude with specialized knowledge for conducting Bayesian analysis in R, focusing on the powerful brms package and its Stan backend. It streamlines the entire research workflow, from initial causal discovery with Directed Acyclic Graphs (DAGs) and model specification with custom priors to sophisticated multilevel modeling and longitudinal data centering. By incorporating best practices for marginal effects, tidy posterior manipulation, and rigorous model diagnostics, it enables data scientists to build robust, interpretable probabilistic models while avoiding common statistical pitfalls like improper clustering or temporal precedence errors.
Key Features
01Comprehensive MCMC diagnostics and posterior predictive checks
026 GitHub stars
03Automated within-person centering and lagged predictor patterns
04Marginal effects calculation and visualization with marginaleffects and tidybayes
05Causal DAG creation and validation using dagitty and ggdag
06Advanced brms model configuration for multilevel and longitudinal data
Use Cases
01Analyzing longitudinal or nested datasets with complex random effects structures
02Validating causal assumptions against empirical data before running regressions
03Computing interpretable marginal probabilities and odds ratios from complex Bayesian models
