User Guide

Welcome to the Aurora-GLM user guide. This section provides step-by-step tutorials covering every major feature of the framework, from initial installation through advanced GAMM modeling.

Aurora-GLM is a Python framework for Generalized Linear Models (GLM), Generalized Additive Models (GAM), and Generalized Additive Mixed Models (GAMM). Import the package as import aurora.

Contents

• Installation
  • Prerequisites
  • Stable release
  • Optional dependencies
  • From source
  • Verifying the installation
• Quickstart
  • Gaussian GLM
  • Poisson GLM
  • Basic GAM
  • Result objects
  • Where to go next
• Generalized Linear Models (GLM)
  • Overview
  • Distribution families
  • Link functions
  • Inference
  • Diagnostics
  • Prediction
  • Comparison with statsmodels
• Generalized Additive Models (GAM)
  • Univariate GAM
• Generalized Additive Mixed Models (GAMM)
  • Quick start
  • Random effects
  • Prediction
• Formula Syntax Reference
  • Basic syntax
  • Smooth terms
  • Parametric terms
  • Random effects
  • Tensor products
  • Data input modes
  • Combined example
• Inference & Diagnostics
  • Wald tests
  • Confidence intervals
  • Robust standard errors
  • GLM diagnostics
  • Diagnostic plots
• Multi-Backend Support
  • Backend overview
• Migration Guide
  • From statsmodels
  • From R mgcv
  • From R lme4
  • Numerical validation