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.
|Author||Gertjan van den Burg [aut, cre], Patrick Groenen [ctb], Andreas Alfons [ctb]|
|Maintainer||Gertjan van den Burg <[email protected]>|
|License||GPL (>= 2)|
|Package repository||View on CRAN|
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