The new HPPLS procedure fits models by using any of several linear predictive methods, including partial least squares (PLS), to optimally address one or both of these two goals: explaining response variation and explaining predictor variation.
The new HPQUANTSELECT procedure performs high-performance quantile regression analysis. PROC HPQUANTSELECT not only fits quantile regression models but also offers extensive capabilities for quantile regression model selection, and it supports statistical inferences with or without the assumption of independently and identically distributed (iid) errors.