"CrocAuc.MBparams.MBnetwork.MBdataDE.MBgibbsPars" <-
function(params,graph,data,gibbsPars,quiet=FALSE,truth,perm=c("none","gene","know"),contrasts=NULL,t0=1,...)
{
# Compatibility checks
    isCompat1 <- compatObjects(params,graph)
    isCompat2 <- compatObjects(graph,data)
    isValid <- validObject(gibbsPars)

# Run Gibbs sampler on simulated data
    if(!quiet) cat("\nRunning gibbs sampler on simulated data...\n\n")
    graph <- calcCPDs(x=graph,data=data,contrasts)
    if(perm=="none") {
      graph <- CgibbsSampler(x=graph,params=params,gibbsPars=gibbsPars,quiet=quiet,...)
      scores <- calcLods(graph)
    }
    else {
      if(perm=="gene") graph <- CpermSigGene(graph=graph,params=params,gibbsPars=gibbsPars,quiet=quiet,...)
      else graph <- CpermSigKnow(graph=graph,params=params,gibbsPars=gibbsPars,quiet=quiet,...)
      scores <- -1*graph@Pvals
    }

# ROC analysis
    if(!quiet) cat("\nPerforming ROC AUC analysis...\n\n")
    #scores[is.na(scores)] <- min(scores,na.rm=TRUE)-1
    auc.sim <- performance(prediction(predictions=scores,labels=truth),measure="auc",fpr.stop=t0)@y.values[[1]]

    return(auc.sim)
}