sim.data

The use of this data set is only intended for testing and
  examples. The data set contains 100 simulated samples
  grouped into 10 classes. For each sample 400 covariates
  have been simulated.

datasets

data

Sparse group lasso multiclass classification, suitable for
    high dimensional problems with many classes. Fast algorithm for
    solving the multinomial sparse group lasso convex optimization
    problem. This package apply template metaprogramming
    techniques, therefore -- when compiling the package from source
    -- a high level of optimization is needed to gain full speed
    (e.g. for the GCC compiler use -O3). Use of multiple processors
    for cross validation and subsampling is supported through
    OpenMP. The Armadillo C++ library is used as the primary linear
    algebra engine.

sim.data: Simulated data set

Description

Arguments