sparsegl: An R Package for Estimating Sparse Group Lasso

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.

18 pages, 9 figures, 1 table

References in corpus (1)

• The degrees of freedom of the Group Lasso for a General Design

Cited by in corpus (1)

• A review of regularised estimation methods and cross-validation in spatiotemporal statistics