r-bayes

Bayesian multilevel models in R use brms to configure explicit priors, family distributions, and CmdStanR backends. You define model formulas, set prior distributions, and run Stan sampling to generate posterior draws for regression analysis.

Compute marginal effects from brms models using marginaleffects to extract posterior draws and calculate average marginal effects (AMEs). This generates response-scale predictions with uncertainty intervals for binary, count, continuous, and ordinal outcomes.

Causal DAGs guide Bayesian regression planning by constructing directed acyclic graphs with dagitty and ggdag. You validate conditional independence relationships before fitting brms models to ensure proper causal adjustment and confounder control.

Nested and repeated-measures data in Bayesian regression use brms to specify random intercepts, random slopes, and crossed effects. You apply person-mean centering and lagged predictors to establish temporal precedence in longitudinal modeling patterns.

Posterior diagnostics for Bayesian regression involve extracting posterior draws to compute odds ratios, credible intervals, and probability of direction. You run posterior predictive checks to validate model fit and interpret effect estimates with uncertainty quantification.

brms supports ordinal and count outcomes in multilevel Bayesian models by selecting appropriate family distributions. You fit models with suitable sampling settings and extract posteriors to compute interpretable marginal effects across different outcome types.