glmnet is (almost) all you need

Preface

This is the website for the first edition of “glmnet is (almost) all you need”, published online.

glmnet (Friedman et al. 2010) is a powerful R package for learning sparse models in supervised learning, but only its most basic capabilities are widely used. This book describes the full range of glmnet’s capabilities and provides code which readers can adapt for their own purposes.

By reading this book, we hope data science practitioners will gain exposure to a wide variety of statistical tools, develop a basic understanding of when to use them, and have access to code that they can adapt for their own purposes.

Acknowledgements

glmnet is the work of many hands. We wish to thank our colleagues and students at Stanford for their contributions to glmnet, especially Jerome Friedman, Noah Simon, Junyang Qian, James Yang, and Jonathan Taylor. Many bug reports and fixes contributed by users are recorded in package news section; the work of Tomas Kalibera, David Keplinger, and others is gratefully acknowledged.

Friedman, Jerome, Trevor Hastie, and Robert Tibshirani. 2010. Regularization Paths for Generalized Linear Models via Coordinate Descent. 33. https://doi.org/10.18637/jss.v033.i01.