Description: Extremely efficient procedures for fitting the entire
lasso or elastic-net regularization path for linear regression,
logistic and multinomial regression models. The algorithm uses
cyclical coordinate descent in a pathwise fashion, as described
in the paper on the maintainer's website.

Packaged: Sun Jun  1 18:48:44 2008; hastie

logistic and multinomial regression models, poisson regression
and the Cox model. The algorithm uses cyclical coordinate
descent in a pathwise fashion, as described in the paper listed

and the Cox model. Two recent additions are the multiresponse
gaussian, and the grouped multinomial. The algorithm uses

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 multiresponse gaussian, and the grouped multinomial. The algorithm uses cyclical coordinate descent in a pathwise fashion, as described in the paper listed below.

Author: Jerome Friedman, Trevor Hastie, Noah Simon, Rob Tibshirani

Title: Lasso and Elastic-Net Regularized Generalized Linear Models
Depends: Matrix (>= 1.0-6), utils, foreach
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. The algorithm uses cyclical coordinate descent in a path-wise fashion, as described in the paper linked to via the URL below.

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.