asgl: A Python Package for Penalized Quantile, Linear, and Logistic Regression

Abstract

asgl is an open-source Python package that offers a robust and versatile framework for fitting a variety of regression models including linear, logistic, and, notably, quantile regression. It implements a comprehensive suite of penalization techniques such as LASSO, ridge, group LASSO, sparse group LASSO, elastic net, and their adaptive variants. A key contribution of asgl is its extensive support for adaptive penalizations, critically offering a range of built-in methodologies for estimating the necessary adaptive weights as proposed by Mendez-Civieta, Aguilera-Morillo, and Lillo (2021). This feature addresses a significant practical challenge - the weight estimation process - in applying advanced adaptive methods, especially in high-dimensional settings, and is largely absent from other packages. Furthermore, asgl offers penalized quantile regression, a less commonly available feature in statistical software. The primary class, Regressor, ensures seamless integration with the scikit-learn ecosystem, facilitating straightforward model evaluation and hyperparameter optimization. asgl has demonstrated utility in variable selection and prediction tasks across both low- and high-dimensional data, positioning it as a comprehensive tool for modern statistical modeling.