Package: glmnet
Type: Package
Title: Lasso and Elastic-Net Regularized Generalized Linear Models
Version: 2.0-18
Date: 2019-05-18
Author: Jerome Friedman [aut, cre],
	Trevor Hastie [aut, cre],
	Rob Tibshirani [aut, cre],
	Noah Simon [aut, ctb],
	Balasubramanian Narasimhan [aut, ctb],
	Junyang Qian [ctb]
Maintainer: Trevor Hastie <hastie@stanford.edu>
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: http://www.jstatsoft.org/v33/i01/.
NeedsCompilation: yes
Packaged: 2019-05-19 00:31:37 UTC; hastie
Repository: CRAN
Date/Publication: 2019-05-20 05:10:12 UTC
Built: R 3.5.3; x86_64-w64-mingw32; 2019-11-05 16:41:10 UTC; windows
Archs: i386, x64
