[](https://cran.r-project.org/package=glmnet)[](https://CRAN.R-project.org/package=glmnet)
We provide extremely efficient procedures for fitting the entire lasso
or elastic-net regularization path for linear regression (gaussian),
multi-task gaussian, logistic and multinomial regression models (grouped
or not), Poisson regression and the Cox model. The algorithm uses
cyclical coordinate descent in a path-wise fashion. Details may be found
in Friedman, Hastie, and Tibshirani ([2010](#ref-glmnet)), Simon et al.
([2011](#ref-coxnet)), Tibshirani et al. ([2012](#ref-strongrules)),
Simon, Friedman, and Hastie ([2013](#ref-block)).
Version 3.0 is a major release with several new features, including:
- Relaxed fitting to allow models in the path to be refit without
regularization. CV will select from these, or from specified
mixtures of the relaxed fit and the regular fit;
- Progress bar to monitor computation;
- Assessment functions for displaying performance of models on test
data. These include all the measures available via `cv.glmnet`, as
well as confusion matrices and ROC plots for classification models;
- print methods for CV output;
- Functions for building the `x` input matrix for `glmnet` that allow
for *one-hot-encoding* of factor variables, appropriate treatment of
missing values, and an option to create a sparse matrix if
appropriate.
- A function for fitting unpenalized a single version of any of the
GLMs of `glmnet`.
Version 4.0 is a major release that allows for any GLM family, besides
the built-in families.
## References