Which R packages are supported by this skill?

The skill primarily focuses on the brms and rstanarm packages for modeling, bayesplot for visualization, and loo for model comparison.

Can I fit non-linear models with this skill?

Yes, it includes specific patterns and syntax for fitting non-linear and distributional models using the brms formula syntax.

How does it handle Bayesian model comparison?

It provides standardized workflows for Leave-One-Out Cross-Validation (LOO-CV), WAIC, Bayes Factors, and model stacking.

Does it assist with MCMC convergence issues?

Yes, it includes diagnostics for R-hat, effective sample size, and trace plots, along with control parameters to resolve divergences.

Is this compatible with the Tidyverse?

Absolutely. The skill is designed around Tidy modeling principles and integrates with packages like tidybayes for posterior manipulation.

Bayesian Modeling in R

Name: Bayesian Modeling in R
Author: choxos

bychoxos

데이터 과학 및 ML

Facilitates advanced Bayesian statistical modeling in R using Stan-based packages for comprehensive data analysis and inference.

소개

This skill provides a specialized workflow for Bayesian statistical modeling within the R environment, leveraging powerful packages like brms and rstanarm. It guides users through the entire Bayesian process, from defining custom priors and fitting complex linear or non-linear mixed-effects models to conducting rigorous posterior predictive checks and MCMC diagnostics. Ideal for researchers and data scientists, it streamlines model comparison using LOO-CV and Bayes factors, ensuring robust and reproducible statistical inference through best-practice Bayesian workflows.

주요 기능

Support for mixed-effects, non-linear, and distributional models
Comprehensive posterior analysis and MCMC diagnostics with bayesplot
Rigorous model comparison using LOO cross-validation and Bayes factors
0 GitHub stars
Advanced prior specification and prior predictive check capabilities
Seamless integration with brms and rstanarm for Stan-based modeling

사용 사례

Fitting complex hierarchical or multilevel models for social or biological research
Performing robust regression analysis with non-Gaussian error distributions
Comparing competing scientific hypotheses using Bayesian model selection techniques

소개

주요 기능

Support for mixed-effects, non-linear, and distributional models
Comprehensive posterior analysis and MCMC diagnostics with bayesplot
Rigorous model comparison using LOO cross-validation and Bayes factors
0 GitHub stars
Advanced prior specification and prior predictive check capabilities
Seamless integration with brms and rstanarm for Stan-based modeling

사용 사례

Fitting complex hierarchical or multilevel models for social or biological research
Performing robust regression analysis with non-Gaussian error distributions
Comparing competing scientific hypotheses using Bayesian model selection techniques