prepare_data

default value, returned if x is null

default

type

if TRUE <code>y</code> will be treated as sparse, if FALSE <code>y</code> will be treated as dens.

sparse

Creates a sgldata data object from a matrix or vector

Fast generic solver for sparse group lasso optimization
problems. The loss (objective) function must be defined in a
C++ module. The optimization problem is solved using a
coordinate gradient descent algorithm. Convergence of the
algorithm is established (see reference) and the algorithm is
applicable to a broad class of loss functions. Use of parallel
computing for cross validation and subsampling is supported
through the 'foreach' and 'doParallel' packages. Development
version is on GitHub, please report package issues on GitHub.

prepare_data: Prepare a sgldata data object

Description

Usage

Arguments

See Also