SCGLR implements a new Partial Least Squares regression approach in the multivariate generalized linear framework. The method
allows the joint modeling of random variables from different exponential family distributions, searching for
common PLS-type components. scglr and scglrCrossVal are the two main functions.
The former constructs the components and performs the parameter estimation, while the
latter selects the approriate number of components by cross-validation.
Dedicated plots, print, and summary functions are available.
The package contains also an ecological
dataset dealing with the abundance of multiple tree genera given a large number of geo-referenced environmental
variables.
Bry X., Trottier C., Verron T. and Mortier F. (2013) Supervised Component Generalized Linear Regression using a PLS-extension of the Fisher scoring algorithm. Journal of Multivariate Analysis, 119, 47-60.#' @docType package