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#' An S4 class of the cluster tree.
#'
#' @description The \code{ClusterTree} object is the computational object for belief propagation.
#' @slot cluster A \code{vector} storing the name of clusters in the cluster tree.
#' @slot node A \code{vector} storing the name of nodes in the Bayesian network.
#' @slot graph A \code{list} of two graphNEL objects: \code{$dag} stores the graph of Bayesian network,
#' \code{$tree} stores the graph of the cluster tree.
#' @slot member A named \code{list} of the node cluster membership.
#' @slot parent A named \code{vector} indicating the parent node of a given cluster in the cluster tree.
#' @slot cluster.class A named \code{vector} of logical values indicating whether a cluster is continuous or discrete.
#' @slot node.class A named \code{vector} of logical values indicating whether a node is continuous or discrete.
#' @slot assignment A named \code{list} indicating the assignment of discrete nodes discrete clusters.
#' @slot propagated A \code{logical} value indicating whether the discrete compartment has been propagated.
#'
#' @slot cpt A named \code{list} of the conditional probability tables.
#' @slot jpt A named \code{list} of the joint distribution tables.
#' @slot lppotential A named \code{list} of the linear predictor potentials assigned to each cluster in the lppotential slots.
#' @slot postbag A named \code{list} of the linear predictor potentials assigned to each cluster in the postbag slots.
#' @slot activeflag A named \code{vector} of logical values indicating whether a continuous cluster is active.
#' @slot absorbed.variables A \code{vector} of characters indicating variables observed with hard evidence.
#' @slot absorbed.values A \code{list} indicating the values of the variables observed with hard evidence.
#' @slot absorbed.soft.variables A \code{vector} of characters indicating variables observed with soft or likelihood evidence.
#' @slot absorbed.soft.values A \code{list} of the likelihoods of the soft or likelihood evidence.
setClass("ClusterTree",
slots = list(cluster = "character",
node = "character",
graph = "list",
member = "list",
parent = "character",
cluster.class = "logical",
node.class = "logical",
assignment = "list",
propagated = "logical",
cpt = "list",
jpt = "list",
lppotential = "list",
postbag = "list",
activeflag = "logical",
absorbed.variables = "character",
absorbed.values = "list",
absorbed.soft.variables = "character",
absorbed.soft.values = "list"
)
)
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