Description: The package "flare" provides the implementation of a family of high-dimensional Lasso regression based machine learning toolkits, including a family of various Lasso regression and sparse Gaussian graphical model estimation. Lasso variants including Dantzig Selector, LAD Lasso, SQRT Lasso for estimating high dimensional sparse linear model. The sparse Gaussian graphical model estimation includes TIGER and CLIME using L1 penalty. We adopt the combination of the dual smoothing and monotone fast iterative soft-thresholding algorithm (MFISTA). The computation is memory-optimized using the sparse matrix output.

Depends: R (>= 2.15.0), lattice, MASS, Matrix
Description: The package "flare" provides the implementation of a family of Lasso variants including Dantzig Selector, LAD Lasso, SQRT Lasso, Lq Lasso for estimating high dimensional sparse linear model. We adopt the alternating direction method of multipliers and convert the original optimization problem into a sequential L1 penalized least square minimization problem, which can be efficiently solved by linearization algorithm. Besides the sparse linear model estimation, we also provide the extension of these Lasso variants to sparse Gaussian graphical model estimation including TIGER and CLIME using either L1 or adaptive penalty. The computation is memory-optimized using the sparse matrix output.

Description: The package "flare" provides the implementation of a family of Lasso variants including Dantzig Selector, LAD Lasso, SQRT Lasso, Lq Lasso for estimating high dimensional sparse linear model. We adopt the alternating direction method of multipliers and convert the original optimization problem into a sequential L1 penalized least square minimization problem, which can be efficiently solved by linearization algorithm. A multi-stage screening approach is adopted for further acceleration. Besides the sparse linear model estimation, we also provide the extension of these Lasso variants to sparse Gaussian graphical model estimation including TIGER and CLIME using either L1 or adaptive penalty. Missing values can be tolerated for Dantzig selector and CLIME. The computation is memory-optimized using the sparse matrix output.