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) <doi:10.48550/arXiv.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.

Version: 1.0.1
Depends: R (≥ 3.0.0), Matrix (≥ 1.0-6)
Imports: graphics
Published: 2021-01-10
DOI: 10.32614/CRAN.package.sparsestep
Author: Gertjan van den Burg [aut, cre], Patrick Groenen [ctb], Andreas Alfons [ctb]
Maintainer: Gertjan van den Burg <gertjanvandenburg at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
Classification/MSC: 62J05, 62J07
Citation: sparsestep citation info
Materials: NEWS
CRAN checks: sparsestep results


Reference manual: sparsestep.pdf


Package source: sparsestep_1.0.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): sparsestep_1.0.1.tgz, r-oldrel (arm64): sparsestep_1.0.1.tgz, r-release (x86_64): sparsestep_1.0.1.tgz, r-oldrel (x86_64): sparsestep_1.0.1.tgz
Old sources: sparsestep archive


Please use the canonical form to link to this page.