Sparse group lasso generic optimizer

Documentation for package ‘sglOptim’ version 1.1.137.0

coef.sgl	Extracting the nonzero coefficients
compute_error	Helper function for computing error rates
create.sgldata	Create a sgldata object
Err	Generic function for computing error rates
features	Generic function for extracting nonzero features (or groups)
features.sgl	Extracting nonzero features
models	Generic function for extracting the fitted models
models.sgl	Returns the estimated models (that is the beta matrices)
nmod	Generic function for counting the number of models
nmod.sgl	Returns the number of models in a sgl object
parameters	Generic function for extracting nonzero parameters
parameters.sgl	Extracting nonzero parameters
prepare.args	Generic function for preparing the sgl call arguments
prepare.args.sgldata	Prepare sgl function arguments
print_with_metric_prefix	Print a numeric with metric prefix
rearrange	Generic rearrange function
rearrange.sgldata	Rearrange sgldata
sgl.algorithm.config	Create a new algorithm configuration
sgl.standard.config	Standard algorithm configuration
sgl_cv	Generic sparse group lasso cross validation using multiple possessors
sgl_fit	Fit a sparse group lasso regularization path.
sgl_lambda_sequence	Generic routine for computing a lambda sequence for the regularization path
sgl_predict	Sgl predict
sgl_print	Print information about sgl object
sgl_subsampling	Generic sparse group lasso subsampling procedure
test.data	Simulated data set