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.
| Version: | 1.0.1 |
| Depends: | R (≥ 3.0.0), Matrix (≥ 1.0-6) |
| Imports: | graphics |
| Published: | 2021-01-10 |
| Author: | Gertjan van den Burg [aut, cre], Patrick Groenen [ctb], Andreas Alfons [ctb] |
| Maintainer: | Gertjan van den Burg <gertjanvandenburg at gmail.com> |
| BugReports: | https://github.com/GjjvdBurg/SparseStep |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: | https://github.com/GjjvdBurg/SparseStep, https://arxiv.org/abs/1701.06967 |
| 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: sparsestep_1.0.1.zip, r-release: sparsestep_1.0.1.zip, r-oldrel: sparsestep_1.0.1.zip |
| 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 |
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