This paper describes an R package named flare, which implements a family of new high dimensional regression methods (LAD Lasso, SQRT Lasso, $\ell_q$ 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.

本文介绍了一个名为flare的R包，它实现了一系列新的高维回归方法和它们对稀疏精度矩阵估计的扩展。这些方法利用不同的非平滑损失函数来获得建模灵活性，估计稳健性和调优不敏感性。开发的求解器基于交替方向乘子法（ADMM）。包flare采用双精度C编写，并通过用户友好的界面从R中调用。使用稀疏矩阵输出来优化内存使用。实验表明，flare高效且能够扩展到大规模问题。

R中高维线性回归和精度矩阵估计的flare包