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) <https://arxiv.org/abs/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

Maintainer
LicenseGPL (>= 2)
Version1.0.0
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:
install.packages("devtools")
library(devtools)
install_github("GjjvdBurg/SparseStep")
GjjvdBurg/SparseStep documentation built on May 8, 2017, 12:54 p.m.