sparsestep (1.0.0)

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SparseStep Regression.

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.

Maintainer: Gertjan van den Burg
Author(s): Gertjan van den Burg [aut, cre], Patrick Groenen [ctb], Andreas Alfons [ctb]

License: GPL (>= 2)

Uses: Matrix

Released almost 3 years ago.



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