Introduction

First released in 2010 by Jerome Friedman, Trevor Hastie, and Robert Tibshirani, glmnet has become the standard R package for fitting sparse regularized regression models. Over more than a decade of active development, its authors have steadily broadened its capabilities and improved its performance. The package is now widely used, yet many of its features and applications are less well known than they deserve to be.

We wrote this book to bring glmnet’s many capabilities together in a single, accessible reference that will keep evolving with the package. Whether you are a practitioner or a researcher, we hope you find it useful in your work.