sparsegl: An R Package for Estimating Sparse Group Lasso
Methodology
2025-01-10 v2
Abstract
The sparse group lasso is a high-dimensional regression technique that is useful for problems whose predictors have a naturally grouped structure and where sparsity is encouraged at both the group and individual predictor level. In this paper we discuss a new R package for computing such regularized models. The intention is to provide highly optimized solution routines enabling analysis of very large datasets, especially in the context of sparse design matrices.
Cite
@article{arxiv.2208.02942,
title = {sparsegl: An R Package for Estimating Sparse Group Lasso},
author = {Xiaoxuan Liang and Aaron Cohen and Anibal Solón Heinsfeld and Franco Pestilli and Daniel J. McDonald},
journal= {arXiv preprint arXiv:2208.02942},
year = {2025}
}
Comments
18 pages, 9 figures, 1 table