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sparsestep  

SparseStep Regression
View on CRAN: Click here


Download and install sparsestep package within the R console
Install from CRAN:
install.packages("sparsestep")

Install from Github:
library("remotes")
install_github("cran/sparsestep")

Install by package version:
library("remotes")
install_version("sparsestep", "1.0.1")



Attach the package and use:
library("sparsestep")
Maintained by
Gertjan van den Burg
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2017-01-27
Latest Update: 2021-01-10
Description:
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.
How to cite:
Gertjan van den Burg (2017). sparsestep: SparseStep Regression. R package version 1.0.1, https://cran.r-project.org/web/packages/sparsestep. Accessed 03 Feb. 2025.
Previous versions and publish date:
1.0.0 (2017-01-27 10:18)
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