glmnet

Lasso and Elastic-Net Regularized Generalized Linear Models

Extremely efficient procedures for fitting the entire lasso or elastic-net regularization path for linear regression, logistic and multinomial regression models, Poisson regression and the Cox model. Two recent additions are the multiple-response Gaussian, and the grouped multinomial regression. The algorithm uses cyclical coordinate descent in a path-wise fashion, as described in the paper linked to via the URL below.

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Description file content

Package
glmnet
Type
Package
Title
Lasso and Elastic-Net Regularized Generalized Linear Models
Version
2.0-16
Date
2018-03-12
Author
Jerome Friedman [aut, cre], Trevor Hastie [aut, cre], Rob Tibshirani [aut, cre], Noah Simon [aut, ctb], Balasubramanian Narasimhan [ctb], Junyang Qian [ctb]
Maintainer
Trevor Hastie
Depends
Matrix (>= 1.0-6), utils, foreach
Imports
methods
Suggests
survival, knitr, lars
Description
Extremely efficient procedures for fitting the entire lasso or elastic-net regularization path for linear regression, logistic and multinomial regression models, Poisson regression and the Cox model. Two recent additions are the multiple-response Gaussian, and the grouped multinomial regression. The algorithm uses cyclical coordinate descent in a path-wise fashion, as described in the paper linked to via the URL below.
License
GPL-2
VignetteBuilder
knitr
URL
NeedsCompilation
yes
Packaged
2018-03-12 04:20:32 UTC; hastie
Repository
CRAN
Date/Publication
2018-04-02 12:06:40 UTC

install.packages('glmnet')

2.0-16

7 months ago

http://www.jstatsoft.org/v33/i01/.

Trevor Hastie

GPL-2

Depends on

Matrix (>= 1.0-6), utils, foreach

Imports

methods

Suggests

survival, knitr, lars

Discussions