R/package.r

In SCGLR: Supervised Component Generalized Linear Regression

# #' Supervised Component Generalized Linear Regression (SCGLR)
# #'
# #' SCGLR implements a new Partial Least Squares (PLS) 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. \code{\link{scglr}} and \code{\link{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.
# #' @references Bry X., Trottier C., Verron T. and Mortier F. (2013) Supervised Component Generalized Linear Regression using a PLS-extension of the Fisher scoring algorithm. \emph{Journal of Multivariate Analysis}, 119, 47-60.#' 
# #' @docType package
# #' @name scglr-package
# #' @author Mortier F., Trottier C., Cornu G., Bry X.
#' @importFrom Matrix bdiag
#' @import Formula
#' @import expm
#' @importFrom graphics barplot
#' @importFrom stats screeplot
#' @import ggplot2
NULL

.onLoad <- function(libname,pkgname) {
}

.onAttach <- function(libname,pkgname) {
  #packageStartupMessage("Experimental version of SCGLR with grouped (theme) covariate extending SCGLR 2.03 version (aka version 2.99)")
}