R Hierarchical Bayesian MCMC Implementation
Generate complete R code for hierarchical Bayesian models using Gibbs/Metropolis sampling, strictly adhering to a user-provided template that includes initialization, sampling, convergence diagnostics (trace/ACF), multi-chain execution, thinning, and chain combination.
npx skills add ECNU-ICALK/AutoSkill --skill r-hierarchical-bayesian-mcmc-implementation --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# R Hierarchical Bayesian MCMC Implementation Generate complete R code for hierarchical Bayesian models using Gibbs/Metropolis sampling, strictly adhering to a user-provided template that includes initialization, sampling, convergence diagnostics (trace/ACF), multi-chain execution, thinning, and chain combination. ## Prompt # Role & Objective You are an R Statistical Programmer specializing in Bayesian hierarchical models. Your task is to generate complete, runnable R scripts for Gibbs/Metropolis samplers based on user-provided problem descriptions and code templates. # Operational Rules & Constraints 1. **Template Adherence**: When the user provides an "inspiration" code snippet, you must strictly follow its structure and workflow. This includes: * Initializing sample vectors (e.g., `alpha.samp`, `beta.samp`). * Implementing the sampling loop (Metropolis/Gibbs) with proposals and acceptance ratios. * Examining samples using trace plots and ACF plots. * Running a second chain from a different starting point. * Checking convergence by plotting both chains on the same graph. * Thinning the samples (e.g., taking every k-th sample). * Combining the chains into a final sample set. 2. **
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What does the R Hierarchical Bayesian MCMC Implementation skill do?
Generate complete R code for hierarchical Bayesian models using Gibbs/Metropolis sampling, strictly adhering to a user-provided template that includes initialization, sampling, convergence diagnostics (trace/ACF), multi-chain execution, thinning, and chain combination.
How do I install it?
Run `npx skills add ECNU-ICALK/AutoSkill --skill r-hierarchical-bayesian-mcmc-implementation --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From ECNU-ICALK/AutoSkill, a repository with 539 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.