# The flare Package for High Dimensional Linear Regression and Precision Matrix Estimation in R

arXiv: https://arxiv.org/abs/2006.15419 · HTML: https://mlsanity.com/paper/2006.15419/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jun 2020.

## Built on by (most cited first, 5 of 5)

- [A unified consensus-based parallel ADMM algorithm for high-dimensional regression with combined regularizations](https://mlsanity.com/paper/2311.12319/) (2023)
- [Square Root Principal Component Pursuit: Tuning-Free Noisy Robust Matrix Recovery](https://mlsanity.com/paper/2106.09211/) (2021)
- [A dual semismooth Newton based augmented Lagrangian method for large-scale linearly constrained sparse group square-root Lasso problems](https://mlsanity.com/paper/2111.13878/) (2021)
- [A proximal-proximal majorization-minimization algorithm for nonconvex tuning-free robust regression problems](https://mlsanity.com/paper/2106.13683/) (2021)
- [The Regularization Parameter: Sparse Precision Matrix Estimation](https://mlsanity.com/paper/2607.07735/) (2026)

## Abstract (arXiv)

This paper describes an R package named flare, which implements a family of new high dimensional regression methods (LAD Lasso, SQRT Lasso, ℓq Lasso, and Dantzig selector) and their extensions to sparse precision matrix estimation (TIGER and CLIME). These methods exploit different nonsmooth loss functions to gain modeling flexibility, estimation robustness, and tuning insensitiveness. The developed solver is based on the alternating direction method of multipliers (ADMM). The package flare is coded in double precision C, and called from R by a user-friendly interface. The memory usage is optimized by using the sparse matrix output. The experiments show that flare is efficient and can scale up to large problems.