DOC: added explanation for LARS
@@ -398,7 +398,11 @@ Least Angle Regression
Least-angle regression (LARS) is a regression algorithm for
high-dimensional data, developed by Bradley Efron, Trevor Hastie, Iain
-Johnstone and Robert Tibshirani.
+Johnstone and Robert Tibshirani. LARS is similar to forward stepwise
+regression. At each step, it finds the predictor most correlated with the
+response. When there are multiple predictors having equal correlation, instead
+of continuing along the same predictor, it proceeds in a direction equiangular
+between the predictors.
The advantages of LARS are:
