SparseStep Regression R package sparsestep version 1.0.1 Classification/MSC: 62J05, 62J07 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. Software Depends: R (>= 3.0.0), Matrix (>= 1.0-6) Imports: graphics Gertjan van den Burg <gertjanvandenburg@gmail.com> Comprehensive R Archive Network (CRAN) Gertjan van den Burg [aut, cre], Patrick Groenen [ctb], Andreas Alfons [ctb] GPL (>= 2) 2021-01-10 application/tgz https://CRAN.R-project.org/package=sparsestep doi:10.32614/CRAN.package.sparsestep