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The yaglm package aims to make the broader ecosystem of modern generalizedlinear models accessible to data analysts and researchers. This ecosystemencompasses a range of loss functions (e.g. linear, logistic, quantileregression), constraints (e.g. positive, isotonic) and penalties. Beyond thebasic lasso/ridge, the package supports structured penalties such as thenuclear norm as well as the group, exclusive, fused, and generalized lasso. Italso supports more accurate adaptive and non-convex (e.g. SCAD) versions ofthese penalties that often come with strong statistical guarantees at limitedadditional computational expense. yaglm comes with a variety of tuningparameter selection methods including: cross-validation, information criteriathat have favorable model selection properties, and degrees of freedomestimators. While several solvers are built in (e.g. FISTA), a key designchoice allows users to employ their favorite state of the art optimizationalgorithms. Designed to be user friendly, the package automatically createstuning parameter grids, supports tuning with fast path algorithms along withparallelization, and follows a unified scikit-learn compatible API.

yaglm: a Python package for fitting and tuning generalized linear models that supports structured, adaptive and non-convex penalties