{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/asgl-a-python-package-for-penalized-linear","title":"Asgl: A Python Package for Penalized Linear and Quantile Regression","arxiv_id":"2111.00472","date":"2021-10-31","proceeding":null,"authors":["Álvaro Méndez Civieta","M. Carmen Aguilera-Morillo","Rosa E. Lillo"],"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 \\pkg{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 et al. (2020). 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.","url_abs":"https://arxiv.org/abs/2111.00472v1","url_pdf":"https://arxiv.org/pdf/2111.00472v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"asgl-a-python-package-for-penalized-linear","repo_url":"https://github.com/alvaromc317/asgl","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}