  The lasso and elastic net are popular regularized regression models for
supervised learning. Friedman, Hastie, and Tibshirani (2010) introduced a
computationally efficient algorithm for computing the elastic net
regularization path for ordinary least squares regression, logistic regression
and multinomial logistic regression, while Simon, Friedman, Hastie, and
Tibshirani (2011) extended this work to Cox models for right-censored data. We
further extend the reach of the elastic net-regularized regression to all
generalized linear model families, Cox models with (start, stop] data and
strata, and a simplified version of the relaxed lasso. We also discuss
convenient utility functions for measuring the performance of these fitted
models.
