diff --git a/inst/appdir/visualizationAcrossTasks.Rmd b/inst/appdir/visualizationAcrossTasks.Rmd
index c5dba90..f0d9fce 100644
--- a/inst/appdir/visualizationAcrossTasks.Rmd
+++ b/inst/appdir/visualizationAcrossTasks.Rmd
@@ -1,124 +1,115 @@
 # Visualization of cross-task insights
 
 The algorithms are ordered according to consensus ranking.
 
 ## Characterization of algorithms
 
 ### Ranking stability: Variability of achieved rankings across tasks
 <!-- Variability of achieved rankings across tasks: If a -->
 <!-- reasonably large number of tasks is available, a blob plot -->
 <!-- can be drawn, visualizing the distribution -->
 <!-- of ranks each algorithm attained across tasks. -->
 <!-- Displayed are all ranks and their frequencies an algorithm -->
 <!-- achieved in any task. If all tasks would provide the same -->
 <!-- stable ranking, narrow intervals around the diagonal would -->
 <!-- be expected. -->
 Algorithms are color-coded, and the area of each blob at position $\left( A_i, \text{rank } j \right)$ is proportional to the relative frequency $A_i$ achieved rank $j$ across multiple tasks. The median rank for each algorithm is indicated by a black cross. This way, the distribution of ranks across tasks can be intuitively visualized.
 
 
 \bigskip
 
 ```{r blobplot_raw,fig.width=9, fig.height=9}
 #stability.ranked.list
 stability(object,ordering=ordering_consensus,max_size=9,size=8,shape=4)+
   scale_color_manual(values=cols)
 ```
 
 
 ```{r, child=if (isMultiTask && bootstrappingEnabled) system.file("appdir", "characterizationOfAlgorithmsBootstrapping.Rmd", package="challengeR")}
 
 ```
 
 
 ## Characterization of tasks
 
 
 ```{r, child=if (isMultiTask && bootstrappingEnabled) system.file("appdir", "characterizationOfTasksBootstrapping.Rmd", package="challengeR")}
 
 ```
 
 ### Cluster Analysis
 <!-- Quite a different question of interest -->
 <!-- is to investigate the similarity of tasks with respect to their -->
 <!-- rankings, i.e., which tasks lead to similar ranking lists and the -->
 <!-- ranking of which tasks are very different. For this question -->
 <!-- a hierarchical cluster analysis is performed based on the -->
 <!-- distance between ranking lists. Different distance measures -->
 <!-- can be used (here: Spearman's footrule distance) -->
 <!-- as well as different agglomeration methods (here: complete and average).  -->
 
 
 Dendrogram from hierarchical cluster analysis} and \textit{network-type graphs} for assessing the similarity of tasks based on challenge rankings. 
 
 A dendrogram is a visualization approach based on hierarchical clustering. It depicts clusters according to a chosen distance measure (here: Spearman's footrule) as well as a chosen agglomeration method (here: complete and average agglomeration). 
 \bigskip
 
 
 ```{r dendrogram_complete, fig.width=6, fig.height=5,out.width='60%'}
 if (length(object$matlist)>2) {
   dendrogram(object,
              dist = "symdiff",
              method="complete")
 } else cat("\nCluster analysis only sensible if there are >2 tasks.\n\n")
 ```
 
 \bigskip
 
 
 ```{r dendrogram_average, fig.width=6, fig.height=5,out.width='60%'}
 if (length(object$matlist)>2)   
   dendrogram(object,
              dist = "symdiff",
              method="average")
 
 ```
 
-<!-- An alternative representation of -->
-<!-- distances between tasks (see Eugster et al, 2008) is provided by networktype -->
-<!-- graphs. -->
-<!-- Every task is represented by a node and nodes are connected -->
-<!-- by edges. Distance between nodes increase exponentially with -->
-<!-- the chosen distance measure d (here: distance between nodes -->
-<!-- equal to 1:05d). Thick edges represent smaller distance, i.e., -->
-<!-- the ranking lists of corresponding tasks are similar. Tasks with -->
-<!-- a unique winner are filled to indicate the algorithm. In case -->
-<!-- there are more than one first-ranked algorithm, nodes remain -->
-<!-- uncoloured. -->
-
-
-In network-type graphs (see Eugster et al, 2008), every task is represented by a node and nodes are connected by edges whose length is determined by a chosen distance measure. Here, distances between nodes are chosen to increase exponentially in Spearman's footrule distance with growth rate 0.05 to accentuate large distances.
-Hence, tasks that are similar with respect to their algorithm ranking appear closer together than those that are dissimilar. Nodes representing tasks with a unique winner are colored-coded by the winning algorithm. In case there are more than one first-ranked algorithms in a task, the corresponding node remains uncolored.
-\bigskip
 
-```{r ,eval=T,fig.width=12, fig.height=6,include=FALSE, fig.keep="none"}
-if (length(object$matlist)>2) {
-  netw=network(object,
-               method = "symdiff", 
-               edge.col=grDevices::grey.colors,
-               edge.lwd=1,
-               rate=1.05,
-               cols=cols
-               )
-  
-  plot.new()
-  leg=legend("topright",  names(netw$leg.col), lwd = 1, col = netw$leg.col, bg =NA,plot=F,cex=.8)
-  w <- grconvertX(leg$rect$w, to='inches')
-  addy=6+w
-} else addy=1
 
-```
+<!-- In network-type graphs (see Eugster et al, 2008), every task is represented by a node and nodes are connected by edges whose length is determined by a chosen distance measure. Here, distances between nodes are chosen to increase exponentially in Spearman's footrule distance with growth rate 0.05 to accentuate large distances. -->
+<!-- Hence, tasks that are similar with respect to their algorithm ranking appear closer together than those that are dissimilar. Nodes representing tasks with a unique winner are colored-coded by the winning algorithm. In case there are more than one first-ranked algorithms in a task, the corresponding node remains uncolored. -->
+<!-- \bigskip -->
+
+<!-- ```{r ,eval=T,fig.width=12, fig.height=6,include=FALSE, fig.keep="none"} -->
+<!-- if (length(object$matlist)>2) { -->
+<!--   netw=network(object, -->
+<!--                method = "symdiff",  -->
+<!--                edge.col=grDevices::grey.colors, -->
+<!--                edge.lwd=1, -->
+<!--                rate=1.05, -->
+<!--                cols=cols -->
+<!--                ) -->
+
+<!--   plot.new() -->
+<!--   leg=legend("topright",  names(netw$leg.col), lwd = 1, col = netw$leg.col, bg =NA,plot=F,cex=.8) -->
+<!--   w <- grconvertX(leg$rect$w, to='inches') -->
+<!--   addy=6+w -->
+<!-- } else addy=1 -->
+
+<!-- ``` -->
+
+<!-- ```{r network, fig.width=addy, fig.height=6,out.width='100%',dev=NULL} -->
+<!-- if (length(object$matlist)>2) { -->
+<!--   plot(netw, -->
+<!--        layoutType = "neato", -->
+<!--        fixedsize=TRUE, -->
+<!--        # fontsize, -->
+<!--        # width, -->
+<!--        # height, -->
+<!--        shape="ellipse", -->
+<!--        cex=.8 -->
+<!--        ) -->
+<!-- } -->
+
+<!-- ``` -->
+
 
-```{r network, fig.width=addy, fig.height=6,out.width='100%',dev=NULL}
-if (length(object$matlist)>2) {
-  plot(netw,
-       layoutType = "neato",
-       fixedsize=TRUE,
-       # fontsize,
-       # width,
-       # height,
-       shape="ellipse",
-       cex=.8
-       )
-}
-
-```
\ No newline at end of file