diff --git a/R/firstlib.R b/R/firstlib.R
index 89ae2e2..e0b3685 100644
--- a/R/firstlib.R
+++ b/R/firstlib.R
@@ -1,17 +1,17 @@
 .onAttach <- function (lib, pkg) {
   ver <- read.dcf(file.path(lib,pkg,"DESCRIPTION"),"Version")
   ver <- as.character(ver)
   packageStartupMessage("\nchallengeR ",
                         ver,
-                        " loaded. \n\n",
+                        " loaded. \n",
                         #                       "Note: Layouting in case of many algorithms or tasks is not yet optimized. Please be patient, we are steadily working on improving the package",
-                        "\n\nPlease cite as:\n   Wiesenfarth, M., Reinke, A., Landman, B.A., Cardoso, M.J., Maier-Hein, L. and Kopp-Schneider, A. (2019). Methods and open-source toolkit for analyzing and visualizing challenge results. arXiv preprint arXiv:1910.05121\n\n",
+                        "\nPlease cite as:\n   Wiesenfarth, M., Reinke, A., Landman, B.A., Cardoso, M.J., Maier-Hein, L. and Kopp-Schneider, A. (2019). Methods and open-source toolkit for analyzing and visualizing challenge results. arXiv preprint arXiv:1910.05121\n",
                         domain = NULL,
                         appendLF = TRUE)
 }
 
 .onLoad <- function(...) {
 }
 
 .onUnload <- function (libpath) {
 }
diff --git a/R/violin.R b/R/violin.R
index 72e91cf..d66feda 100644
--- a/R/violin.R
+++ b/R/violin.R
@@ -1,91 +1,91 @@
 violin <- function(x,...) UseMethod("violin")
 violin.default <- function(x, ...) stop("not implemented for this class")
 
 violin.bootstrap.list=function(x,...){
   ken=melt(kendall.bootstrap.list(x))
   colnames(ken)[2]="Task"
-  cat("\n\nSummary Kendall's tau\n")
+  cat("\n\nSummary Kendall's tau:\n")
   ss=ken%>%group_by(Task)%>%
     summarise(mean=mean(value,na.rm=T),
               median=median(value,na.rm=T),
               q25=quantile(value,probs = .25,na.rm=T),
               q75=quantile(value,probs = .75,na.rm=T))%>%
     arrange(desc(median))
 
-  print(as.data.frame(ss))
+  print(knitr::kable(as.data.frame(ss)))
 
   # drop task if no kendall could be computed
     noResults <- sapply(split(ss,ss$Task), 
                         function(x) all(is.na(x[,-1])))
     if (any(noResults)) {
-      message("No Kendall's tau could be calculated for any bootstrap sample in task ", 
+      cat("\nNo Kendall's tau could be calculated for any bootstrap sample in task ", 
               names(noResults)[noResults],
-              " because of missing variability. Task dropped from figure.")
+              " because of missing variability. Task dropped from figure.",fill=F)
       ken <- ken %>% filter(Task %in% names(noResults)[!noResults])
     
     }
   
   xAxisText <- element_blank()
 
   # Show task names as tick mark labels only for multi-task data set
   if (length(x$data) > 1) {
     xAxisText <- element_text(angle = 90, vjust = 0.5, hjust = 1)
   }
 
   ken%>%mutate(Task=factor(.data$Task,
                            levels=ss$Task))%>%
     ggplot(aes(Task,value))+
     geom_violin(alpha=.3,
                 color=NA,
                 na.rm=TRUE,
                 fill="blue")+
     geom_boxplot(width=0.1,
                  na.rm=TRUE,
                  fill="white")+
     theme(axis.text.x = xAxisText,
           legend.position = "none")+
     ylab("Kendall's tau")+
     scale_y_continuous(limits=c(min(min(ken$value),0),
                                 max(max(ken$value),1)))
 }
 
 
 
 kendall.bootstrap.list=function(x){
   ken=lapply(1:length(x$bootsrappedRanks),function(Task){
     id=match(rownames( x$bootsrappedRanks[[Task]]),
              rownames(x$matlist[[Task]]) )
     sapply(x$bootsrappedRanks[[Task]],
            function(bootSample) suppressWarnings(kendall(bootSample,
                                                          x$matlist[[Task]]$rank[id])))
   } )
   names(ken)=names((x$bootsrappedRanks))
 
   if (sum(is.na(x))>0){
    cat("Bootstrap samples without variability in rankings (all algorithms ranked 1) excluded.\n Frequency of such samples by task:\n",fill = T)
     sapply(ken,function(x) sum(is.na(x)))
   }
 
 
   return(ken)
 
 }
 
 
 density.bootstrap.list=function(x,...){
   ken=melt(kendall.bootstrap.list(x))
   colnames(ken)[2]="Task"
 
   cat("\n\nSummary Kendall's tau\n")
   ss=ken%>%group_by(Task)%>%
     summarise(mean=mean(value,na.rm=T),
               median=median(value,na.rm=T),
               q25=quantile(value,probs = .25,na.rm=T),
               q75=quantile(value,probs = .75,na.rm=T))%>%
     arrange(desc(median))
 
   print(as.data.frame(ss))
 
   ggplot(ken)+
     geom_density(aes(value,fill=Task),alpha=.3,color=NA)
 }
diff --git a/inst/appdir/visualizationViolinPlots.Rmd b/inst/appdir/visualizationViolinPlots.Rmd
index 36e8ba3..88e075e 100644
--- a/inst/appdir/visualizationViolinPlots.Rmd
+++ b/inst/appdir/visualizationViolinPlots.Rmd
@@ -1,9 +1,9 @@
 ## *Violin plot* for visualizing ranking stability based on bootstrapping \label{violin}
 
 The ranking list based on the full assessment data is pairwisely compared with the ranking lists based on the individual bootstrap samples (here $b=$ `r ncol(boot_object$bootsrappedRanks[[1]])` samples). For each pair of rankings, Kendall's $\tau$ correlation is computed. Kendall’s $\tau$ is a scaled index determining the correlation between the lists. It is computed by evaluating the number of pairwise concordances and discordances between ranking lists and produces values between $-1$ (for inverted order) and $1$ (for identical order). A violin plot, which simultaneously depicts a boxplot and a density plot, is generated from the results.
 
 \bigskip
 
-```{r violin}
+```{r violin, results='asis'}
 violin(boot_object)
 ```