Package: flare
Type: Package
Title: Family of Lasso Regression
Version: 0.9.8
Date: 2013-01-26
Author: Xingguo Li, Tuo Zhao, Lie Wang, Xiaoming Yuan and Han Liu
Maintainer: Xingguo Li <xingguo.leo@gmail.com>
Depends: R (>= 2.15.0), lattice, igraph, 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. For Dantzig selector and Lq Lasso, we adopt the
alternating direction method of multipliers (ADMM) and convert
the original optimization problem into a sequential L1
penalized least square minimization problem, which can be
efficiently solved by combining the linearization and the
efficient coordinate descent algorithm. For LAD and SQRT Lasso,
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.
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 (ADMM) using either
L1 or adaptive L1 penalty.
License: GPL-2
Repository: CRAN
Packaged: 2013-01-27 05:33:23 UTC; leo
Date/Publication: 2013-01-27 08:13:22

Package: flare
Type: Package
Title: Family of Lasso Regression
Version: 0.9.9
Date: 2013-03-31
Author: Xingguo Li, Tuo Zhao, Lie Wang, Xiaoming Yuan and Han Liu
Maintainer: Xingguo Li <xingguo.leo@gmail.com>
Depends: R (>= 2.15.0), lattice, igraph, 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. For Dantzig selector and Lq Lasso, we adopt the
alternating direction method of multipliers (ADMM) and convert
the original optimization problem into a sequential L1
penalized least square minimization problem, which can be
efficiently solved by combining the linearization and the
efficient coordinate descent algorithm. For LAD and SQRT Lasso,
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.
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 (ADMM) using either
L1 or adaptive L1 penalty.
License: GPL-2
Repository: CRAN
Packaged: 2013-04-01 13:12:11 UTC; admin-118
NeedsCompilation: yes
Date/Publication: 2013-04-01 15:52:29