sgl_cv: Sparse group lasso cross validation using multiple possessors
In SglOptimizer: Sparse group lasso optimizer

Sparse group lasso cross validation using multiple possessors

  sgl_cv(module_name, PACKAGE, data, covariateGrouping,
    groupWeights, parameterWeights, alpha, lambda,
    fold = 2L, cv.indices = list(), max.threads = 2L,
    seed = 331L, algorithm.config = sgl.standard.config)

`call_sym`	reference to objective specific C++ routines
`data`
`covariateGrouping`	grouping of covariates, a vector of length p. Each element of the vector specifying the group of the covariate.
`groupWeights`	the group weights, a vector of length m+1 (the number of groups).
`parameterWeights`	a matrix of size K \times (p+1).
`alpha`	the α value 0 for group lasso, 1 for lasso, between 0 and 1 gives a sparse group lasso penalty.
`lambda`	the lambda sequence for the regularization path.
`fold`	the fold of the cross validation, an integer larger than 1 and less than N+1. Ignored if `cv.indices != NULL`. If `fold`≤`max(table(classes))` then the data will be split into `fold` disjoint subsets keeping the ration of classes approximately equal. Otherwise the data will be split into `fold` disjoint subsets without keeping the ration fixed.
`cv.indices`	a list of indices of a cross validation splitting. If `cv.indices = NULL` then a random splitting will be generated using the `fold` argument.
`max.threads`	the maximal number of threads to be used
`seed`	the seed used for generating the random cross validation splitting, only used if `fold`≤`max(table(classes))`.
`algorithm.config`	the algorithm configuration to be used.