{"categories":[{"label":"Mathematics","url":"https://skillfed.io/packages/category/scientific-engineering-mathematics/2"}],"enrichment":{"capability":"glum is a high-performance Python library for fitting generalized linear models (GLMs) with support for multiple distributions, regularization methods, formula-based specification, and statistical inference.","skillfed_tags":["statistical-modeling","regularization","formula-based"],"use_cases":["Build logistic regression models with L1 regularization for sparse, interpretable solutions in classification tasks","Fit Poisson regression for count data in demand forecasting or event prediction","Specify complex models with formulas including spline basis functions and categorical encoding without manual feature engineering","Apply Tikhonov (matrix-valued) penalties to model correlated random effects in hierarchical data","Perform statistical inference on unregularized GLM coefficients with confidence intervals and hypothesis tests","Benchmark GLM fitting performance across libraries using the included glum_benchmarks module"],"what_it_does":"glum is a scikit-learn-compatible GLM library designed for speed and statistical completeness. It supports Normal, Poisson, binomial, gamma, inverse Gaussian, negative binomial, and Tweedie distributions with customizable link functions. The library includes L1, L2, and elastic net regularization, built-in cross-validation for regularization tuning, and classical statistical inference for unregularized models. It also supports box constraints, linear inequality constraints, sample weights, and offsets.\n\nThe package emphasizes formula-based model specification via formulaic, allowing users to define models with intuitive syntax including monotonic constraints. It works with multiple dataframe backends (pandas, polars, and others) through narwhals. Performance is a core design goal\u2014the library is optimized for scenarios where observations greatly outnumber predictors, and includes benchmarking tools for comparison against other modern libraries.","worth_installing":"Yes, if you need fast GLM fitting with broad distribution and regularization support and a scikit-learn-like API. The active maintenance, permissive license, and zero known vulnerabilities make it production-ready. Medium install friction is acceptable for the performance and feature gains. Verify performance on your specific problem size and distribution before committing to production use."},"id":"glum","links":{"html":"https://skillfed.io/packages/glum","md":"https://skillfed.io/packages/glum.md","pypi":"https://pypi.org/project/glum/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-05-06","license_spdx":null,"license_treatment":"permissive","name":"glum","python_support":"supports_current","summary":"High performance Python GLMs with all the features!"},"popularity":{"monthly_downloads":330902,"position":7528,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"3.4.1"}