#' Bootstrap Learning Performed on a Gaussian Bayesian Network.
#'
#' The 15 node network structure was simulated with Melancon's and Philippe's
#' Uniform Random Acyclic Digraphs algorithm. This produces a highly connected DAG.
#' See \code{?random.graph} for more information.
#'
#' \itemize{
#' \item truth. An object of \code{bn.fit}, a simulated structure and parameter set.
#' \item boot. An object of \code{bn.strength} and \code{data.frame}, results of
#' model averaging applied to data simulated from truth.
#' }
#'
#' @docType data
#' @keywords datasets
#' @name melancon_boot
#' @usage data(melancon_boot)
#' @format A list with 2 items
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#' Bootstrap Learning Performed on a Gaussian Bayesian Network.
#'
#' The 40 node network structure was simulated with full ordering based generation.
#' This produces a sparsely connected DAG. See \code{?random.graph} for more information.
#'
#' \itemize{
#' \item truth. An object of \code{bn.fit}, a simulated structure and parameter set.
#' \item boot. An object of \code{bn.strength} and \code{data.frame}, results of
#' model averaging applied to data simulated from truth.
#' }
#'
#' @docType data
#' @keywords datasets
#' @name ordered_boot
#' @usage data(ordered_boot)
#' @format A list with 2 items
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