Implements the SparseStep model for solving regression
problems with a sparsity constraint on the parameters. The SparseStep
regression model was proposed in Van den Burg, Groenen, and Alfons (2017)
. In the model, a regularization term is added to the
regression problem which approximates the counting norm of the parameters.
By iteratively improving the approximation a sparse solution to the
regression problem can be obtained. In this package both the standard
SparseStep algorithm is implemented as well as a path algorithm which uses
golden section search to determine solutions with different values for the
regularization parameter.