Package: glmnet
Type: Package
Title: Lasso and Elastic-Net Regularized Generalized Linear Models
Version: 3.0-2
Date: 2019-12-09
Authors@R: c(person("Jerome", "Friedman", role=c("aut")),
	     person("Trevor", "Hastie", role=c("aut", "cre"), email = "hastie@stanford.edu"),
	     person("Rob", "Tibshirani", role=c("aut")),
	     person("Balasubramanian", "Narasimhan", role=c("aut")),
	     person("Noah", "Simon", role=c("aut")),
	     person("Junyang", "Qian", role=c("ctb")))
Depends: R (>= 3.6.0), Matrix (>= 1.0-6)
Imports: methods, utils, foreach, shape
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 papers listed in the URL below.
License: GPL-2
VignetteBuilder: knitr
Encoding: UTF-8
URL: https://glmnet.stanford.edu,
        https://dx.doi.org/10.18637/jss.v033.i01,
        https://dx.doi.org/10.18637/jss.v039.i05
RoxygenNote: 6.1.1
NeedsCompilation: yes
Packaged: 2019-12-11 01:55:13 UTC; hastie
Author: Jerome Friedman [aut],
  Trevor Hastie [aut, cre],
  Rob Tibshirani [aut],
  Balasubramanian Narasimhan [aut],
  Noah Simon [aut],
  Junyang Qian [ctb]
Maintainer: Trevor Hastie <hastie@stanford.edu>
Repository: CRAN
Date/Publication: 2019-12-11 17:00:02 UTC
Built: R 3.6.3; x86_64-w64-mingw32; 2020-05-05 06:03:27 UTC; windows
Archs: x64
