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.

The packages for R have also been submitted to CRAN

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  Trevor Hastie             hastie@stanford.edu  
  Professor, Department of Statistics, Stanford University
  Phone: (650) 725-2231 (Statistics)          Fax: (650) 725-8977  
  (650) 498-5233 (Biostatistics)   Fax: (650) 725-6951
  URL: http://www-stat.stanford.edu/~hastie  
  address: room 104, Department of Statistics, Sequoia Hall
           390 Serra Mall, Stanford University, CA 94305-4065  
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