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#' Obtain marginal distributions
#'
#' Get the marginal distributions of multiple variables
#'
#' @details Get the marginal distributions of multiple variables. The function \code{Marginals}
#' returns a \code{list} of marginal distributions. The marginal distribution of a discrete variable
#' is a named vector of probabilities. Meanwhile, the marginal distributions of
#' continous variables in a CG-BN model are mixtures of Gaussian distributions.
#' To fully represent this information, the marginal of a continuous variable is represented by
#' a \code{data.frame} with three columns to specify
#' parameters for each Gaussian distribution in the mixture, which are
#'
#' \describe{
#' \item{\code{mean}}{the mean value of a Gaussian distribution.}
#' \item{\code{sd}}{the standard deviation of a Gaussian distribution.}
#' \item{\code{n}}{the number of Gaussian mixtures}
#' }
#'
#' @param tree a \code{\linkS4class{ClusterTree}} object
#' @param vars a \code{vector} of variables for query of marginal distributions
#'
#' @return
#'
#' \describe{
#' \item{\code{marginals}}{a \code{list} of marginal distributions}
#' \item{\code{types}}{a named \code{vector} indicating the types of the variables whose
#' marginals are queried: \code{TRUE} for discrete, \code{FALSE} for continuous.}
#' }
#'
#' @author Han Yu
#'
#' @references Cowell, R. G. (2005). Local propagation in conditional Gaussian Bayesian networks.
#' Journal of Machine Learning Research, 6(Sep), 1517-1550. \cr
#' \cr
#' Yu H, Moharil J, Blair RH (2020). BayesNetBP: An R Package for Probabilistic Reasoning in Bayesian
#' Networks. Journal of Statistical Software, 94(3), 1-31. <doi:10.18637/jss.v094.i03>.
#'
#' @examples
#'
#' data(liver)
#' tree.init.p <- Initializer(dag=liver$dag, data=liver$data,
#' node.class=liver$node.class,
#' propagate = TRUE)
#' tree.post <- AbsorbEvidence(tree.init.p, c("Nr1i3", "chr1_42.65"), list(1,"1"))
#' marg <- Marginals(tree.post, c("HDL", "Ppap2a"))
#' marg$marginals$HDL
#' head(marg$marginals$Ppap2a)
#'
#' @seealso \code{\link{PlotMarginals}} for visualization of the marginal distributions,
#' \code{\link{SummaryMarginals}} for summarization of the marginal distributions of
#' continuous variables.
#'
#' @export
Marginals <- function(tree, vars) {
if(!tree@propagated) {
stop("The ClusterTree object must be propagated before making queries.")
}
if(sum(vars %in% tree@absorbed.variables)!=0) {
var.in <- vars[vars %in% tree@absorbed.variables]
msg1 <- paste0(var.in, collapse=", ")
stop(paste0(msg1, " is/are already observed."))
}
node.class <- tree@node.class
marginal.types <- node.class[vars]
margs <- list()
for (i in 1:length(vars)) {
var <- vars[i]
if (node.class[[var]]) {
margs[[i]] <- DiscreteMarginal(tree, var)
} else {
margs[[i]] <- PushMarginal(tree, var)
}
}
names(margs) <- vars
output <- list()
output$marginals <- margs
output$types <- marginal.types
class(output) <- "marginals"
return(output)
}
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