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sparsenet
View on CRAN: Click
here
Download and install sparsenet package within the R console
Install from CRAN:
install.packages("sparsenet")
Install from Github:
library("remotes")
install_github("cran/sparsenet")
Install by package version:
library("remotes")
install_version("sparsenet", "1.7")
Attach the package and use:
library("sparsenet")
Maintained by
Trevor Hastie
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2012-03-03
Latest Update: 2024-02-05
Description:
Efficient procedure for fitting regularization paths between L1 and L0, using the MC+ penalty of Zhang, C.H. (2010)<doi:10.1214/09-AOS729>. Implements the methodology described in Mazumder, Friedman and Hastie (2011) <doi:10.1198/jasa.2011.tm09738>. Sparsenet computes the regularization surface over both the family parameter and the tuning parameter by coordinate descent.
How to cite:
Trevor Hastie (2012). sparsenet: Fit Sparse Linear Regression Models via Nonconvex Optimization. R package version 1.7, https://cran.r-project.org/web/packages/sparsenet. Accessed 22 Dec. 2024.
Previous versions and publish date:
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Complete documentation for sparsenet
Functions, R codes and Examples using
the sparsenet R package
Some associated functions: cv.sparsenet . gendata . plot.cv.sparsenet . plot.sparsenet . predict.cv.sparsenet . predict.sparsenet . sparsenet-internal . sparsenet-package . sparsenet .
Some associated R codes: argmin.R . coef.cv.sparsenet.R . coef.sparsenet.R . cv.sparsenet.R . gendata.R . getcoef_list.R . lambda.interp.R . lambda0.R . nonzeroCoef.R . onAttach.R . plot.cv.sparsenet.R . plot.sparsenet.R . plotCoef.R . predict.cv.sparsenet.R . predict.sparsenet.R . print.cv.sparsenet.R . print.sparsenet.R . sparsenet.R . sparsepredict.R . summary.sparsenet.R . zeromat.R . Full sparsenet package functions and examples
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