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.

中文翻译：

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R中高维线性回归和精确矩阵估计的flare包

本文描述了一个名为flare的R包，它实现了一系列新的高维回归方法（LAD Lasso、SQRT Lasso、$\ell_q$ Lasso和Dantzig选择器）及其对稀疏精度矩阵估计（TIGER和CLIME）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、估计鲁棒性和调整不灵敏性。开发的求解器基于乘法器的交替方向法 (ADMM)。包耀斑以双精度 C 编码，并通过用户友好的界面从 R 调用。通过使用稀疏矩阵输出来优化内存使用。实验表明，flare 是有效的，并且可以扩展到大型问题。