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gglasso  

Group Lasso Penalized Learning Using a Unified BMD Algorithm
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("gglasso", "1.5.1")



Attach the package and use:
library("gglasso")
Maintained by
Yi Yang
[Scholar Profile | Author Map]
First Published: 2012-04-30
Latest Update: 2020-03-18
Description:
A unified algorithm, blockwise-majorization-descent (BMD), for efficiently computing the solution paths of the group-lasso penalized least squares, logistic regression, Huberized SVM and squared SVM. The package is an implementation of Yang, Y. and Zou, H. (2015) DOI: .
How to cite:
Yi Yang (2012). gglasso: Group Lasso Penalized Learning Using a Unified BMD Algorithm. R package version 1.5.1, https://cran.r-project.org/web/packages/gglasso. Accessed 10 Apr. 2025.
Previous versions and publish date:
1.0 (2012-04-30 07:42), 1.1 (2013-02-24 08:45), 1.2 (2014-06-01 22:35), 1.3 (2014-08-17 08:22), 1.4 (2017-09-15 09:43), 1.5 (2020-03-18 08:00)
Other packages that cited gglasso R package
View gglasso citation profile
Other R packages that gglasso depends, imports, suggests or enhances
Complete documentation for gglasso
Functions, R codes and Examples using the gglasso R package
Some associated functions: bardet . coef.cv.gglasso . coef.gglasso . colon . cv.gglasso . gglasso . plot.cv.gglasso . plot.gglasso . predict.cv.gglasso . predict.gglasso . print.gglasso . 
Some associated R codes: auxiliary.R . bardet-data.R . colon-data.R . cv.R . gglasso.R . imports.R . loss.R . model.R . plot.cv.gglasso.R . plot.gglasso.R . tools.R . utilities.R .  Full gglasso package functions and examples
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