# sparsegl: An R Package for Estimating Sparse Group Lasso

arXiv: https://arxiv.org/abs/2208.02942 · HTML: https://mlsanity.com/paper/2208.02942/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Aug 2022.

## Built on by (most cited first, 9 of 9)

- [A Fast and Scalable Pathwise-Solver for Group Lasso and Elastic Net Penalized Regression via Block-Coordinate Descent](https://mlsanity.com/paper/2405.08631/) (2024)
- [Strong Screening Rules for Group-based SLOPE Models](https://mlsanity.com/paper/2405.15357/) (2024)
- [Classification by sparse generalized additive models](https://mlsanity.com/paper/2212.01792/) (2022)
- [Dual Feature Reduction for the Sparse-group Lasso and its Adaptive Variant](https://mlsanity.com/paper/2405.17094/) (2024)
- [Convex Estimation of Gaussian Graphical Regression Models with Covariates](https://mlsanity.com/paper/2410.06326/) (2024)
- [GRASP: Grouped Regression with Adaptive Shrinkage Priors](https://mlsanity.com/paper/2506.18092/) (2025)
- [Preference-Based Dynamic Ranking Structure Recognition](https://mlsanity.com/paper/2509.24493/) (2025)
- [Transfer learning for functional linear regression via control variates](https://mlsanity.com/paper/2601.17217/) (2026)
- [Improving the Efficiency of Subgroup Analysis in Randomized Controlled Trials with TMLE](https://mlsanity.com/paper/2605.15483/) (2026)

## Abstract (arXiv)

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.