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-#' Mean-Center the data
-#' 
-#' Mean Centers the input and output matrices. 
-#' 
-#' @param data an object of class \code{idframe}
-#' 
-#' @return 
-#' A list containing the following elements
-#' 
-#' \tabular{ll}{
-#'    \code{fitted.values} \tab \code{idframe} object with mean-centered variables \cr
-#'    \code{output.mean} \tab \code{vector} containing means for each output variable \cr
-#'    \code{input.mean} \tab \code{vector} containing means for each input variable
-#'  }
-#' 
-#' @examples
-#' data(cstr)
-#' fit.mean <- demean(cstr)
-#' cstr_demean <- predict(fit.mean) 
-#' 
-#' @seealso \code{\link{predict.demean}}, \code{\link[stats]{colMeans}}
-#' @export
-demean <- function(data){
-  
-  data_demean <- data 
-  output.mean <- colMeans(data$output)
-  input.mean <- colMeans(data$input)
-  
-  
-  data_demean$output <- data$output - output.mean
-  data_demean$input <- data$input - input.mean
-  
-  est <- list(fitted.values=data_demean,output.mean = output.mean,
-              input.mean = input.mean)
-  
-  class(est) <- "demean"
-  return(est)
-}
-
-#' Predict the centered values
-#' 
-#' Center an \code{idframe} object based on the training center means
-#' 
-#' @param object an object of class \code{idframe}
-#' @param newdata An optional idframe object in which to look for variables with which
-#' to predict. If ommited, the original idframe object is used
-#' 
-#' @return an \code{idframe} object
-#' 
-#' @examples
-#' ## Examples for train and test sets
-#' data(cstr)
-#' splitList <- dataPartition(cstr,p=0.6)
-#' train <- splitList$estimation # training set 
-#' test <- splitList$validation # testing set
-#' fit.mean <- demean(train)
-#' train_demean <- predict(fit.mean)
-#' test_demean <- predict(fit.mean,newdata=test)  
-#' @export
-predict.demean <- function(object,newdata=NULL,...){
-  
-  if(is.null(newdata)){
-    data <- fitted(object)
-  } else{
-    data <- newdata
-    data$output <- data$output - object$output.mean
-    data$input <- data$input - object$input.mean
-  }
-  return(data)
-}