Nothing
#' CLERE methodology for simultaneous variables clustering and regression
#'
#' The methodology consists in creating clusters of variables involved in a
#' high dimensional linear regression model so as to reduce the dimensionality.
#' A model-based approach is proposed and fitted using a Stochastic EM-Gibbs
#' algorithm (SEM-Gibbs).
#'
#' @name clere-package
#' @aliases clere clere-package
#' @docType package
#'
#' @seealso Overview : \code{\link{clere-package}} \cr
#' Classes : \code{\linkS4class{Clere}}, \code{\linkS4class{Pacs}} \cr
#' Methods : \code{\link{plot}}, \code{\link{clusters}}, \code{\link{predict}}, \code{\link{summary}} \cr
#' Functions : \code{\link{fitClere}}, \code{\link{fitPacs}}
#' Datasets : \code{\link{numExpRealData}}, \code{\link{numExpSimData}}, \code{\link{algoComp}}
#'
#' @examples
#'
#' # Simple example using simulated data
#' # to see how to you the main function clere
#' library(clere)
#' x <- matrix(rnorm(50 * 100), nrow = 50, ncol = 100)
#' y <- rnorm(50)
#' model <- fitClere(y = y, x = x, g = 2, plotit = FALSE)
#' plot(model)
#' clus <- clusters(model, threshold = NULL)
#' predict(model, newx = x+1)
#' summary(model)
#'
NULL
## usethis namespace: start
#' @useDynLib clere, .registration = TRUE
## usethis namespace: end
NULL
## usethis namespace: start
#' @importFrom Rcpp sourceCpp
## usethis namespace: end
NULL
## usethis namespace: start
#' @import RcppEigen
## usethis namespace: end
NULL
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.