Package: sparsestep 1.0.1

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) <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.

Authors:Gertjan van den Burg [aut, cre], Patrick Groenen [ctb], Andreas Alfons [ctb]

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sparsestep.pdf |sparsestep.html
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NEWS

# Install 'sparsestep' in R:
install.packages('sparsestep', repos = c('https://gjjvdburg.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/gjjvdburg/sparsestep/issues

On CRAN:

feature-selectionlasso-variantsregularized-linear-regressionsparse-regressionsparse-regularization

2.70 score 1 stars 7 scripts 150 downloads 2 exports 2 dependencies

Last updated 4 years agofrom:4c49fabae8. Checks:OK: 1 NOTE: 6. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 12 2024
R-4.5-winNOTEOct 12 2024
R-4.5-linuxNOTEOct 12 2024
R-4.4-winNOTEOct 12 2024
R-4.4-macNOTEOct 12 2024
R-4.3-winNOTEOct 12 2024
R-4.3-macNOTEOct 12 2024

Exports:path.sparsestepsparsestep

Dependencies:latticeMatrix