Does it handle complex models like survival or zero-inflated analysis?

Absolutely, it includes templates for ordinal regression, zero-inflated/hurdle models, and parametric or semi-parametric survival models like Cox and Weibull.

Can this skill help with custom prior specification?

Yes, it provides implementation patterns for setting custom priors across different parameter classes and performing prior predictive checks to ensure your priors are reasonable.

What R packages does this skill support?

It primarily supports brms and rstanarm for modeling, bayesplot for visualization, loo for model comparison, and tidybayes for data manipulation.

Can I compare different Bayesian models using this skill?

Yes, it covers advanced model comparison techniques including LOO-CV, WAIC, and Bayes Factors, as well as model stacking for weighted predictions.

How does it assist with model validation?

The skill includes standard workflows for MCMC diagnostics (Rhat, ESS, trace plots) and posterior predictive checks (density overlays, interval plots) to ensure model convergence and fit.

Bayesian Modeling in R

Name: Bayesian Modeling in R
Author: choxos

bychoxos

0•

数据科学与机器学习

Performs comprehensive Bayesian statistical modeling and posterior analysis using Stan-based R packages like brms and rstanarm.

This skill empowers Claude to design, fit, and evaluate Bayesian models within the R ecosystem. It provides structured guidance for the entire Bayesian workflow, from setting custom priors and conducting prior predictive checks to performing MCMC diagnostics and robust model comparisons using LOO-CV and Bayes Factors. Whether you are building simple linear regressions or complex multi-level, non-linear, or distributional models, this skill ensures best practices in Bayesian inference and uncertainty quantification are followed.

主要功能

01Rigorous posterior analysis including MCMC diagnostics and hypothesis testing

02Model comparison using LOO cross-validation, WAIC, and Bayes Factors

03Automated setup for brms and rstanarm regression models

040 GitHub stars

05Implementation of mixed-effects, non-linear, and distributional models

06Advanced prior specification and prior predictive check visualizations

使用场景

01Estimating complex hierarchical or multi-level models in biostatistics or social sciences

02Validating and comparing multiple statistical models for predictive accuracy

03Conducting rigorous uncertainty quantification for non-linear regression tasks

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add choxos/biostatagent bayesian-modeling

For use in Claude.ai and ChatGPT

主要功能

01Rigorous posterior analysis including MCMC diagnostics and hypothesis testing

02Model comparison using LOO cross-validation, WAIC, and Bayes Factors

03Automated setup for brms and rstanarm regression models

040 GitHub stars

05Implementation of mixed-effects, non-linear, and distributional models

06Advanced prior specification and prior predictive check visualizations

使用场景

01Estimating complex hierarchical or multi-level models in biostatistics or social sciences

02Validating and comparing multiple statistical models for predictive accuracy

03Conducting rigorous uncertainty quantification for non-linear regression tasks

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add choxos/biostatagent bayesian-modeling

For use in Claude.ai and ChatGPT