 Least angle regression

A piecewise linear regression-path algorithm which starts at zero, activates a predictor with maximal absolute residual score, and moves in a direction that decreases all active absolute scores equally until another predictor ties them. With $G=X^{\mathsf{T}}X/n$ and active correlation signs $s_A$, the coefficient direction is $G_{AA}^{-1}{s}_A$. Ordinary LAR does not drop a predictor when its coefficient crosses zero; sign compatibility of LAR and Lasso characterizes when its path also obeys the Lasso KKT conditions.