Family of Lasso Regression
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 combining the linearization
and the efficient coordinate descent algorithm. 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 using either L1 or
adaptive penalty.