#' all in one function of iterated HP-filter
#'
#' @param x the data you want to conduct HP-filter
#' @param lambda the turning parameter
#' @param iter logical parameter, TRUE is to conduct iterated HP-filter, FALSE is not
#' @param test_type the type for creterion
#' @param sig_p significant p-value
#' @param Max_Iter maximum iterated time
#'
#' @return cycle component, iterated number, p-value .
#' @export
#'
#' @examples lam <- 100 # tuning parameter for the annaul data
#'
#' # raw HP filter
#' bx_HP <- BoostedHP(x, lambda = lam, iter= FALSE)$trend
#'
#' # by BIC
#' bx_BIC <- BoostedHP(IRE, lambda = lam, iter= TRUE, test_type = "BIC")
#'
#' # by ADF
#' bx_ADF <- BoostedHP(IRE, lambda = lam, iter= TRUE, test_type = "adf", sig_p = 0.050)
#'
#' # summarize the outcome
#' outcome <- cbind(IRE, bx_HP$trend, bx_BIC$trend, bx_ADF$trend)
#'
#' matplot(outcome, type = "l", ylab = "", lwd = rep(2,4))
hist.bHP <- function(x){
message("This is hist method special for 'bHP' class (",x$test_type,").")
}
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