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```
#' Calculate the changepoint probabilities.
#'
#' This function calculates the marginal changepoint probabilities from the
#' changepoint samples taken during the MCMC simulation.
#'
#'
#' @param network.samples List of network and changepoint samples collected
#' during the MCMC simulation by \code{\link{EDISON.run}} and
#' \code{\link{runDBN}}.
#' @return Returns a matrix of dimension NumNodes by NumTimePoints, where each
#' entry contains the marginal posterior probability of a changepoint for that
#' node at that timepoint.
#' @author Frank Dondelinger
#' @examples
#'
#' # Generate random gene network and simulate data from it
#' dataset = simulateNetwork()
#'
#' # Run MCMC simulation to infer networks and changepoint locations
#' result = EDISON.run(dataset$sim_data, num.iter=500)
#'
#' # Calculate posterior probabilities of changepoints
#' cps = calculateCPProbabilities(result)
#'
#' @export calculateCPProbabilities
calculateCPProbabilities <-
function(network.samples) {
timePoints = 1:(network.samples$n+1)
sampled = network.samples[[1]]$sampled
numNodes = length(network.samples) - 1
prob.cps = matrix(0, numNodes, length(timePoints))
colnames(prob.cps) <- timePoints
for(sample.i in 1:length(sampled)) {
for(target in 1:numNodes) {
cps.temp = network.samples[[target]]$cp_samples[sample.i,]
cps.indices = which(timePoints %in% cps.temp)
prob.cps[target,cps.indices] = prob.cps[target,cps.indices] + 1
numSegs = length(cps.temp) - 1
}
}
prob.cps = prob.cps / length(sampled)
global.cps = calculateCPPGlobal(prob.cps)
return(list(node.cps=prob.cps, global.cps=global.cps))
}
```

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