Subsections:

The HPPLS procedure is a high-performance version of the PLS procedure in SAS/STAT software, which fits models by using any
one of a number of linear predictive methods, including *partial least squares* (PLS).
Ordinary least squares regression, as implemented in SAS/STAT procedures such as the GLM and REG procedures, has the single
goal of minimizing sample response prediction error, and it seeks linear functions of the predictors that explain as much
variation in each response as possible. The HPPLS procedure implements techniques that have the additional goal of accounting
for variation in the predictors,
under the assumption that directions in the predictor space that are well sampled should provide better prediction for *new* observations when the predictors are highly correlated. All the techniques that the HPPLS procedure implements work by extracting
successive linear combinations of the predictors, called *factors* (also called *components*, *latent vectors*, or *latent variables*), which optimally address one or both of these two
goals: explaining response variation and explaining predictor variation. In particular, the method of partial least squares
balances the two objectives by seeking factors that explain both response and predictor variation.

The name "partial least squares" also applies to a more general statistical method that is *not* implemented in this procedure. The partial least squares method was originally developed in the 1960s by the econometrician
Herman Wold (1966) for modeling "paths" of causal relation between any number of "blocks" of variables. However, the HPPLS procedure fits only
*predictive* partial least squares models that have one "block" of predictors and one "block" of responses. If you are interested in fitting
more general path models, you should consider using the CALIS procedure.

PROC HPPLS runs in either single-machine mode or distributed mode.

**Note**: Distributed mode requires SAS High-Performance Statistics .