  Least Angle Regression is a promising technique for variable selection
applications, offering a nice alternative to stepwise regression. It provides
an explanation for the similar behavior of LASSO ($\ell_1$-penalized
regression) and forward stagewise regression, and provides a fast
implementation of both. The idea has caught on rapidly, and sparked a great
deal of research interest. In this paper, we give an overview of Least Angle
Regression and the current state of related research.
