Fit Bayesian multilevel models with brms and visualize causal DAGs in R.
npx skills add https://github.com/jhelvy/dotfiles --skill r-bayes
Please help me install this Agent Skill. Skill: r-bayes Source: https://github.com/jhelvy/dotfiles/tree/main/claude/skills/r-bayes Command: npx skills add https://github.com/jhelvy/dotfiles --skill r-bayes
This Skill streamlines Bayesian statistical modeling and causal inference in R, making complex analyses more accessible and reproducible.
brms with explicit prior specification and robust diagnostics.marginaleffects.Use the r-bayes skill to fit a multilevel Bayesian model predicting outcome from predictor1 and predictor2 using the brms package.
High-intent search queries and answers about installing and using this skill.
You can fit Bayesian multilevel models in R using the brms package, which supports specifying random intercepts, random slopes, and within-person centering for complex longitudinal data structures.
To define and validate Directed Acyclic Graphs (DAGs) in R, use dagitty and ggdag packages to visually map, test, and validate causal assumptions before estimating model effects.
Extract and interpret marginal effects from brms Bayesian models using the marginaleffects package to calculate predictions, visualize effects, and understand model outcomes accurately.
Yes, brms supports analyzing longitudinal survey data by estimating causal intervention impacts while accounting for individual differences and temporal dynamics via multilevel modeling.
Yes, fitting Bayesian models with brms involves explicit prior specification and robust model diagnostics to validate results, ensure reproducibility, and accurately interpret posterior distributions.
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