Least Angle Regression software: LARS

"Least Angle Regression" ("LAR") is a new model selection
algorithm; a useful and less greedy version of traditional
forward selection methods. LAR is described in detail in a paper 
by Brad Efron, Trevor Hastie, Iain Johnstone and Rob Tibshirani,
soon to appear in the Annals of Statistics. 

The paper, as well as R and Splus packages, are available at
http://www-stat.stanford.edu/~hastie/Papers#LARS

A simple modification of the LAR algorithm implements Tibshirani's
Lasso, an attractive version of OLS that constrains the sum of the
absolute regression coefficients; the Lasso modification of the LARS
software calculates the entire Lasso path of coefficients for a given
problem at the cost of a single least squares fit.

A different LARS modification efficiently implements epsilon Forward
Stagewise linear regression, another promising new model selection
method closely related to Boosting.