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.
- Version1.0.1
- R versionunknown
- LicenseGPL-2
- LicenseGPL-3
- Needs compilation?No
- sparsestep citation info
- Last release01/10/2021
Documentation
Team
Gertjan van den Burg
Andreas Alfons
Patrick Groenen
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- Depends1 package