论文标题
R的高维线性回归和精确矩阵估计的耀斑封装
The flare Package for High Dimensional Linear Regression and Precision Matrix Estimation in R
论文作者
论文摘要
本文介绍了一个名为Flare的R软件包，该软件包实现了新的高维回归方法的家族（LAD Lasso，SQRT Lasso，$ \ ell_q $ lasso和Dantzig Selector）及其扩展程序及其扩展，以进行稀疏精密矩阵估计（Tiger和Clime）。这些方法利用不同的非平滑损失函数来获得建模灵活性，估计鲁棒性和不敏感性。开发的求解器基于乘数的交替方向方法（ADMM）。软件包耀斑以双精度C进行编码，并通过用户友好的界面从R中调用。通过使用稀疏矩阵输出来优化内存使用量。实验表明，火炬是有效的，可以扩展到大问题。
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.
