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:

Version 4.0 is a major release that allows for any GLM family, besides the built-in families.


Friedman, Jerome, Trevor Hastie, and Rob 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 Rob 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 J. 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. .

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