sparsegl: An R Package for Estimating Sparse Group Lasso

TL;DR

The paper introduces 'sparsegl', an R package designed for efficient estimation of sparse group lasso models, facilitating high-dimensional regression with grouped predictors and sparsity constraints.

Contribution

The paper presents a new R package that offers optimized routines for sparse group lasso, enabling analysis of large datasets with grouped predictors.

Findings

Efficient algorithms for sparse group lasso implemented in R.

Capability to handle large datasets with sparse design matrices.

Facilitates high-dimensional regression with grouped structures.

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.