sparsestep: 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.

Package details

AuthorGertjan van den Burg [aut, cre], Patrick Groenen [ctb], Andreas Alfons [ctb]
MaintainerGertjan van den Burg <[email protected]>
LicenseGPL (>= 2)
Package repositoryView on CRAN
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sparsestep documentation built on May 1, 2019, 10:28 p.m.