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sparsestep
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]
[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 22 Dec. 2024.
Previous versions and publish date:
1.0.0 (2017-01-27 10:18)
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Complete documentation for sparsestep
Functions, R codes and Examples using
the sparsestep R package
Some associated functions: coef.sparsestep . path.sparsestep . plot.sparsestep . predict.sparsestep . print.sparsestep . sparsestep-package . sparsestep .
Some associated R codes: coef.sparsestep.R . path.sparsestep.R . plot.sparsestep.R . postprocess.R . predict.sparsestep.R . preprocess.R . print.sparsestep.R . run.sparsestep.R . sparsestep-package.R . sparsestep.R . Full sparsestep package functions and examples
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