Run Bayesian inference workflows with brms for multilevel models and posterior diagnostics.
npx skills add https://github.com/gnoblet/ANA_app_svelte --skill r-bayes-gnoblet
Please help me install this Agent Skill. Skill: r-bayes Source: https://github.com/gnoblet/ANA_app_svelte/tree/main/.claude/.claude/skills/r-bayes Command: npx skills add https://github.com/gnoblet/ANA_app_svelte --skill r-bayes-gnoblet
Provides a structured workflow for Bayesian analysis in R using brms, including model specification, priors, and posterior diagnostics.
Run a minimal brms example to illustrate Bayesian inference on a small dataset.
High-intent search queries and answers about installing and using this skill.
You can perform Bayesian regression with brms by following a structured workflow that includes model specification, prior selection, fitting, and posterior diagnostic checks for robust inference.
The standard workflow for Bayesian inference with brms includes formula specification, data preparation, setting priors, model fitting, diagnostic checks, extracting results, and ensuring reproducibility.
You validate causal models by using dagitty and ggdag to construct and test Directed Acyclic Graphs (DAGs) before fitting your Bayesian regression to ensure the causal structure supports robust inferences.
Marginal effects from a Bayesian multilevel model are interpreted through clear reporting and visualization of posterior predictions, allowing you to understand the specific impact of predictors on the outcome.
Yes, brms supports reproducible Bayesian analysis workflows by utilizing model caching, which saves fitted model objects to avoid refitting and ensures consistent reporting of posterior results.
jhelvyFit Bayesian multilevel models with brms and visualize causal DAGs in R.
ab604Perform Bayesian inference in R with brms and CmdStanR.
impact-initiativesFit brms multilevel Bayesian models with DAG-based causal assumption checks.
choxosSpecify priors and analyze posterior distributions in R using brms and rstanarm.
