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pymc-extras

Add B-spline basis functions and distributional regression to PyMC models.

76|10|Updated Feb 5, 2026
Search Tags:#pymc#bayesian analysis#advanced modeling#nonlinear effects#distributional regression
One-click install
npx skills add https://github.com/pymc-labs/python-analytics-skills --skill pymc-extras
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: pymc-extras
Source: https://github.com/pymc-labs/python-analytics-skills/tree/main/skills/pymc-extras
Command: npx skills add https://github.com/pymc-labs/python-analytics-skills --skill pymc-extras

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pymc, pymc-extras, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill extends PyMC models with sophisticated features, enabling more complex statistical modeling and analysis.

Core Features & Use Cases

  • Advanced Splines: Integrate B-spline basis functions for nonlinear effects in models.
  • Distributional Regression: Model full distribution parameters, not just means.
  • R2D2M2CP Priors: Use a prior that reasons about the proportion of variance explained.
  • Use Case: If you're building a model for insurance risk assessment and want to include non-Gaussian distributions or complex nonlinear relationships, this Skill can help you do that with PyMC.

Quick Start

Use the 'pymc-extras' skill to add splines to your PyMC model for a non-linear effect.

Frequently Asked Questions about pymc-extras

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

FAQPage Schema
How do I add nonlinear effects to my Bayesian model using splines in PyMC?▼

To add nonlinear effects to your Bayesian model, you can use this Skill to integrate B-spline basis functions directly into your PyMC statistical models.

What is distributional regression and does PyMC support it for modeling variance?▼

Distributional regression models full distribution parameters rather than just the mean. This Skill extends PyMC to support distributional modeling for complex statistical analysis.

Can I use PyMC for insurance risk assessment with non-Gaussian distributions and complex relationships?▼

Yes, you can model insurance risk assessment with non-Gaussian distributions and complex nonlinear relationships by extending your PyMC models with this Skill's advanced features.

What is the best way to detect sparse signals in high-dimensional Bayesian data?▼

The best way to detect sparse signals in high-dimensional data is using R2D2M2CP shrinkage priors, which reason about the proportion of variance explained within your PyMC models.

Do I need advanced statistical knowledge to use distributional regression and shrinkage priors?▼

Yes, an advanced understanding of Bayesian statistical modeling is required to effectively use features like distributional regression, splines, and R2D2M2CP priors in PyMC.

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