  Given R groups of numerical variables X1, ... XR, we assume that each group
is the result of one underlying latent variable, and that all latent variables
are bound together through a linear equation system. Moreover, we assume that
some explanatory latent variables may interact pairwise in one or more
equations. We basically consider PLS Path Modelling's algorithm to estimate
both latent variables and the model's coefficients. New "external" estimation
schemes are proposed that draw latent variables towards strong group structures
in a more flexible way. New "internal" estimation schemes are proposed to
enable PLSPM to make good use of variable group complementarity and to deal
with interactions. Application examples are given.

Machine Learning