GjjvdBurg/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

Package details

LicenseGPL (>= 2)
URL https://github.com/GjjvdBurg/SparseStep https://arxiv.org/abs/1701.06967
Package repositoryView on GitHub
Installation Install the latest version of this package by entering the following in R:
GjjvdBurg/SparseStep documentation built on Jan. 23, 2021, 8:32 a.m.