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.

Imports: methods, utils, foreach, shape, survival
Description: Extremely efficient procedures for fitting the entire lasso or elastic-net regularization path for linear regression, logistic and multinomial regression models, Poisson regression, Cox model,  multiple-response Gaussian, and the grouped multinomial regression. There are two new and important additions. The family argument can be a GLM family object, which opens the door to any programmed family. This comes with a modest computational cost, so when the built-in families suffice, they should be used instead.  The other novelty is the relax option, which refits each of the active sets in the path unpenalized. The algorithm uses cyclical coordinate descent in a path-wise fashion, as described in the papers listed in the URL below.

Suggests: survival, knitr, lars, testthat

Suggests: knitr, lars, testthat, xfun, rmarkdown

Imports: methods, utils, foreach, shape, survival, Rcpp

Title: Add Nonparametric Bootstrap SE to 'glmnet' for Selected
Description: Builds a LASSO, Ridge, or Elastic Net model with 'glmnet' or
'cv.glmnet' with bootstrap inference statistics (SE, CI, and p-value)
for selected coefficients with no shrinkage applied for them. Model
performance can be evaluated on test data and an automated alpha
selection is implemented for Elastic Net. Parallelized computation is
used to speed up the process. The methods are described in Friedman et
Imports: boot, glmnet, graphics, parallel, stats

Description: Provides a formula interface for the 'glmnet' package for
elasticnet regression, a method for cross-validating the alpha parameter,
Imports: stats, graphics, grDevices, glmnet, parallel, Matrix
Suggests: knitr, rmarkdown, MASS, doParallel, testthat
Author: Microsoft [cph], Hong Ooi [aut, cre]