--- a
+++ b/R/Jaccard.R
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+#' @title Jaccard’s similarity
+#' @description Robustness of the clusters can be assessed by Jaccard’s
+#'   similarity, which reflects the reproducibility of individual clusters
+#'   across bootstrapping runs. Jaccard’s similarity is the intersect of two
+#'   clusters divided by the union.
+#' @export
+#' @param object \code{DISCBIO} class object.
+#' @param Clustering Clustering has to be one of the following:
+#'   ["K-means","MB"]. Default is "K-means"
+#' @param K A numeric value of the number of clusters
+#' @param plot if `TRUE`, plots the mean Jaccard similarities
+#' @param R number of bootstrap replicates
+#' @importFrom graphics barplot box
+#' @return A plot of the mean Jaccard similarity coefficient per cluster.
+Jaccard <- function(object, Clustering = "K-means", K, plot = TRUE, R = 100) {
+  JACCARD <- vector()
+
+  # Validation
+  if (!(Clustering %in% c("K-means", "MB"))) {
+    stop("Clustering has to be either K-means or MB")
+  }
+  for (i in 1:K) {
+    # Optimize by avoiding if every loop. Only thing variable is data
+    if (Clustering == "K-means") {
+      target_col <- object@kmeans$kpart
+    } else if (Clustering == "MB") {
+      target_col <- object@MBclusters$clusterid
+    }
+    results <- bootstrap(object@fdata[, which(target_col == i)], R)
+    # to get the mean of all bootstrappings (mean of mean Jaccard values)
+    JACCARD[i] <- round(mean(results), digits = 3)
+  }
+  if (plot) {
+    barplot(
+      height    = JACCARD,
+      names.arg = seq_len(length(JACCARD)),
+      ylab      = "Mean Jaccard's similarity values",
+      xlab      = "Clusters",
+      las       = 1,
+      ylim      = c(0, 1),
+      col       = c("black", "blue", "green", "red", "yellow", "gray")
+    )
+    box()
+  }
+  return(JACCARD)
+}