# glmnet v4.0-2

Monthly downloads

## Lasso and Elastic-Net Regularized Generalized Linear Models

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.

## Readme

# Lasso and Elastic-Net Regularized Generalized Linear Models

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), Simon et al. (2011), Tibshirani et al. (2012), Simon, Friedman, and Hastie (2013).

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

*Journal of Statistical Software, Articles*33 (1): 1–22. https://doi.org/10.18637/jss.v033.i01.

*Journal of Statistical Software, Articles*39 (5): 1–13. https://doi.org/10.18637/jss.v039.i05.

*Journal of the Royal Statistical Society: Series B (Statistical Methodology)*74 (2): 245–66. https://doi.org/10.1111/j.1467-9868.2011.01004.x.

## Functions in glmnet

Name | Description | |

elnet.fit | Solve weighted least squares (WLS) problem for a single lambda value | |

Cindex | compute C index for a Cox model | |

dev_function | Elastic net deviance value | |

assess.glmnet | assess performance of a 'glmnet' object using test data. | |

deviance.glmnet | Extract the deviance from a glmnet object | |

coxgrad | compute gradient for cox model | |

glmnet.fit | Fit a GLM with elastic net regularization for a single value of lambda | |

get_start | Get null deviance, starting mu and lambda max | |

get_eta | Helper function to get etas (linear predictions) | |

cv.glmnet | Cross-validation for glmnet | |

glmnet.path | Fit a GLM with elastic net regularization for a path of lambda values | |

glmnet-internal | Internal glmnet functions | |

glmnet | fit a GLM with lasso or elasticnet regularization | |

glmnet-package | Elastic net model paths for some generalized linear models | |

predict.glmnetfit | Get predictions from a glmnetfit fit object | |

print.cv.glmnet | print a cross-validated glmnet object | |

glmnet.control | internal glmnet parameters | |

glmnet.measures | Display the names of the measures used in CV for different "glmnet" families | |

predict.cv.glmnet | make predictions from a "cv.glmnet" object. | |

print.glmnet | print a glmnet object | |

pen_function | Elastic net penalty value | |

coef.glmnet | Extract coefficients from a glmnet object | |

plot.glmnet | plot coefficients from a "glmnet" object | |

makeX | convert a data frame to a data matrix with one-hot encoding | |

rmult | Generate multinomial samples from a probability matrix | |

na.replace | Replace the missing entries in a matrix columnwise with the entries in a supplied vector | |

plot.cv.glmnet | plot the cross-validation curve produced by cv.glmnet | |

obj_function | Elastic net objective function value | |

coxnet.deviance | compute deviance for cox model output | |

bigGlm | fit a glm with all the options in glmnet | |

beta_CVX | Simulated data for the glmnet vignette | |

No Results! |

## Vignettes of glmnet

Name | ||

assets/coxnet.RDS | ||

assets/glmnet_refs.bib | ||

assets/vignette_binomial.png | ||

assets/vignette_gaussian.png | ||

Coxnet.Rmd | ||

glmnet.Rmd | ||

glmnetFamily.Rmd | ||

relax.Rmd | ||

No Results! |

## Last month downloads

## Details

Type | Package |

Date | 2020-6-13 |

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 | 7.1.0 |

NeedsCompilation | yes |

Packaged | 2020-06-14 23:21:58 UTC; hastie |

Repository | CRAN |

Date/Publication | 2020-06-16 00:00:02 UTC |

imports | foreach , methods , shape , survival , utils |

suggests | knitr , lars , testthat |

depends | Matrix (>= 1.0-6) , R (>= 3.6.0) |

Contributors | Rob Tibshirani, Jerome Friedman, Balasubramanian Narasimhan, Noah Simon, Junyang Qian, Kenneth Tay |

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