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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.
Getting started
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Package details |
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| Author | Gertjan van den Burg [aut, cre], Patrick Groenen [ctb], Andreas Alfons [ctb] |
| Maintainer | Gertjan van den Burg <gertjanvandenburg@gmail.com> |
| License | GPL (>= 2) |
| Version | 1.0.1 |
| URL | https://github.com/GjjvdBurg/SparseStep https://arxiv.org/abs/1701.06967 |
| Package repository | View on CRAN |
| Installation |
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