Aurora-GLM Documentation ======================== .. raw:: html

A modular, extensible, and high-performance Python framework for Generalized Linear Models (GLM), Generalized Additive Models (GAM), and Generalized Additive Mixed Models (GAMM).

.. grid:: 1 2 3 3 :gutter: 2 .. grid-item-card:: User Guide :link: guide/index :link-type: doc :class-card: guide-card Step-by-step tutorials covering GLM, GAM, GAMM, formula syntax, multi-backend support, and inference. .. grid-item-card:: API Reference :link: api/index :link-type: doc :class-card: api-card Complete reference for all public modules, classes, and functions. .. grid-item-card:: Examples :link: examples/index :link-type: doc :class-card: examples-card 17 case studies including insurance pricing, species distribution, clinical trials, and more. Features -------- - **Multi-backend**: NumPy, PyTorch, and JAX with automatic detection - **GPU acceleration**: Up to 141x speedup via PyTorch CUDA - **10+ distribution families**: Gaussian, Poisson, Binomial, Gamma, Beta, and more - **9 link functions**: Logit, Log, Probit, CLogLog, and more - **R-compatible formulas**: ``y ~ s(x1) + x2 + (1 | group)`` - **Validated against R**: Results match mgcv, lme4 within 1e-6 tolerance - **Sparse matrix support**: Efficient for large n or many smooth terms Quick Start ----------- .. code-block:: python import aurora from aurora import fit_glm, GaussianFamily, LogitLink # Fit a GLM result = fit_glm(X, y, family=GaussianFamily()) # Inspect results result.summary() result.plot_diagnostics() # Use PyTorch backend for GPU acceleration import torch result = fit_glm(torch.tensor(X), torch.tensor(y), family=GaussianFamily()) .. toctree:: :hidden: :maxdepth: 2 guide/index api/index examples/index Indices ------- * :ref:`genindex` * :ref:`modindex` * :ref:`search`