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) <https://arxiv.org/abs/1701.06967>. 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.
Package details 


Author  Gertjan van den Burg [aut, cre], Patrick Groenen [ctb], Andreas Alfons [ctb] 
Maintainer  Gertjan van den Burg <[email protected]> 
License  GPL (>= 2) 
Version  1.0.0 
URL  https://github.com/GjjvdBurg/SparseStep https://arxiv.org/abs/1701.06967 
Package repository  View on CRAN 
Installation 
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