README.md

In glmnet: Lasso and Elastic-Net Regularized Generalized Linear Models

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.

Version 4.1 is a major release that expands the scope for survival modeling, allowing for (start, stop) data, strata, and sparse X inputs. It also provides a much-requested method for survival:survfit.

References

Friedman, Jerome, Trevor Hastie, and Robert Tibshirani. 2010. “Regularization Paths for Generalized Linear Models via Coordinate Descent.” *Journal of Statistical Software, Articles* 33 (1): 1–22. .

Simon, Noah, Jerome Friedman, and Trevor Hastie. 2013. “A Blockwise Descent Algorithm for Group-Penalized Multiresponse and Multinomial Regression.”

Simon, Noah, Jerome Friedman, Trevor Hastie, and Robert Tibshirani. 2011. “Regularization Paths for Cox’s Proportional Hazards Model via Coordinate Descent.” *Journal of Statistical Software, Articles* 39 (5): 1–13. .

Tibshirani, Robert, Jacob Bien, Jerome Friedman, Trevor Hastie, Noah Simon, Jonathan Taylor, and Ryan Tibshirani. 2012. “Strong Rules for Discarding Predictors in Lasso-Type Problems.” *Journal of the Royal Statistical Society: Series B (Statistical Methodology)* 74 (2): 245–66. .

Kenneth Tay, J, Narasimhan, Balasubramanian, Hastie, Trevor. 2023. “Elastic Net Regularization Paths for All Generalized Linear Models.” *Journal of Statistical Software, Articles* 106 (1): 1–31. .

glmnet documentation built on Aug. 22, 2023, 9:12 a.m.