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Bayesian Modeling in R

Specify priors and analyze posterior distributions in R using brms and rstanarm.

6|1|Updated Dec 10, 2025
Search Tags:#workflow#bayesian#model-comparison#MCMC#posterior-analysis#brms#rstanarm
One-click install
npx skills add https://github.com/choxos/TidyRModelling --skill bayesian-modeling-in-r
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: Bayesian Modeling in R
Source: https://github.com/choxos/TidyRModelling/tree/main/plugins/r-tidy-modelling/skills/bayesian-modeling
Command: npx skills add https://github.com/choxos/TidyRModelling --skill bayesian-modeling-in-r

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill enables practitioners to build and evaluate Bayesian models in R using brms and rstanarm, providing robust uncertainty quantification beyond traditional methods.

Core Features & Use Cases

  • Prior specification & posterior analysis: define priors, fit models, and interpret posterior distributions.
  • Model comparison & checks: perform LOO/WAIC, Bayes factors, and posterior predictive checks to compare models.
  • Flexible modeling capabilities: handle linear, generalized, mixed-effects, non-linear, and survival-type models with Stan backends.
  • Use Case: ecological studies with hierarchical data and small sample sizes can benefit from full Bayesian inference to quantify uncertainty.

Quick Start

Install and load brms and rstanarm, fit a simple Gaussian model with two predictors using brm, then summarize the posterior draws.

Frequently Asked Questions about Bayesian Modeling in R

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I specify priors and analyze posterior distributions in R?▼

To specify priors and analyze posterior distributions in R, use brms or rstanarm to define model priors, fit the model, and extract posterior draws for interpretation. This Skill automates that workflow.

What's the best way to compare Bayesian models using LOO or WAIC?▼

The best way to compare Bayesian models is using LOO, WAIC, and Bayes factors. This Skill performs model comparison and posterior predictive checks to evaluate and differentiate competing models.

Can I fit hierarchical mixed-effects models with small sample sizes in R?▼

Yes, you can fit hierarchical mixed-effects models with small sample sizes in R using brms and rstanarm. Full Bayesian inference quantifies uncertainty robustly, which is ideal for ecological studies.

Do I need a Stan backend installed to run Bayesian regression in R?▼

Yes, you need a Stan backend installed to run Bayesian regression. This Skill requires R with brms, rstanarm, bayesplot, and related packages configured with Stan to execute posterior analysis.

Does brms support non-linear and survival-type Bayesian models?▼

Yes, brms supports non-linear and survival-type Bayesian models. This Skill handles linear, generalized, mixed-effects, non-linear, and survival-type models using Stan backends for flexible inference.

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