Welcome to glum's documentation! ========================================= ``glum`` is a fast, modern, Python-first GLM estimation library. Generalized linear modeling (GLM) is a core statistical tool that includes many common methods like least-squares regression, Poisson regression and logistic regression as special cases. In addition to fitting basic GLMs, ``glum`` supports a wide range of features. These include: * Built-in cross validation for optimal regularization, efficiently exploiting a “regularization path” * L1 and elastic net regularization, which produce sparse and easily interpretable solutions * L2 regularization, including variable matrix-valued (Tikhonov) penalties, which are useful in modeling correlated effects * Normal, Poisson, logistic, gamma, and Tweedie distributions, plus varied and customizable link functions * Built-in formula-based model specification using ``formulaic`` * Classical statistical inference for unregularized models using dispersion and standard errors * Box and linear inequality constraints, sample weights, offsets * A scikit-learn-like API to fit smoothly into existing workflows ``glum`` was also built with performance in mind. The following figure shows the runtime of a realistic example using an insurance dataset. For more details and other benchmarks, see the :doc:`Benchmarks` section. .. BENCHMARK_FIGURES_START .. image:: _static/wide-insurance-gamma-normalized.png :width: 600 .. BENCHMARK_FIGURES_END We suggest visiting the :doc:`Installation` and :doc:`Getting Started` sections first. .. toctree:: :maxdepth: 1 Installation Getting Started Motivation Benchmarks .. toctree:: :maxdepth: 2 Tutorials .. toctree:: :maxdepth: 1 Contributing/Development Algorithmic details API Reference GitHub Changelog :ref:`genindex`