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regression-models

Solve Bayesian regression modeling tasks using Stan and JAGS templates.

9|1|Updated Jan 10, 2026
Search Tags:#regression#bayesian#stan#jags#poisson#logistic
One-click install
npx skills add https://github.com/choxos/BiostatAgent --skill regression-models
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: regression-models
Source: https://github.com/choxos/BiostatAgent/tree/main/plugins/bayesian-modeling/skills/regression-models
Command: npx skills add https://github.com/choxos/BiostatAgent --skill regression-models

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Bayesian regression modeling tasks across multiple distributions are streamlined by providing ready-to-use Stan and JAGS templates for linear, logistic, Poisson, negative binomial, and robust regression, reducing setup time and ensuring best practices.

Core Features & Use Cases

  • Ready-to-run Stan and JAGS specifications for common regression families.
  • Clear guidance on data, parameters, and priors to accelerate model building.
  • Use Case: quickly compare model families on a dataset with count, binary, or continuous outcomes.

Quick Start

Run a minimal Stan or JAGS example with your data to fit a regression model.

Frequently Asked Questions about regression-models

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

FAQPage Schema
How do I write a Bayesian regression model in Stan for a continuous outcome?▼

To build a Bayesian logistic regression model in Stan, you define data blocks for binary outcomes, specify priors, and set parameter definitions. This Skill provides ready-to-run Stan templates for logistic regression with clear guidance on priors to accelerate setup.

Can I use JAGS for Poisson regression with count data?▼

Yes, JAGS supports Poisson regression for count data outcomes. This Skill provides ready-to-use JAGS specifications for Poisson and negative binomial regression families, ensuring best practices for probabilistic inference on count datasets.

What's the best way to compare linear and logistic regression families on one dataset?▼

Comparing regression families involves fitting multiple distributions like linear, logistic, and Poisson variants. This Skill supplies ready-to-run Stan and JAGS templates across common regression families, enabling quick model comparison on datasets with mixed outcome types.

Does this support robust regression variants for probabilistic inference?▼

Robust regression variants are supported for probabilistic inference in both Stan and JAGS. The Skill provides ready-to-run specifications for robust regression alongside linear, logistic, and Poisson families for applied analytics projects.

When do I need negative binomial regression instead of Poisson in Bayesian modeling?▼

Negative binomial regression is needed when count data exhibits overdispersion relative to Poisson assumptions. This Skill provides ready-to-use Stan and JAGS templates for negative binomial regression to handle such count outcomes.

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