diff --git a/R/R6Class-BipartiteSBM.R b/R/R6Class-BipartiteSBM.R
index 8260fc7..5d80b01 100644
--- a/R/R6Class-BipartiteSBM.R
+++ b/R/R6Class-BipartiteSBM.R
@@ -21,8 +21,8 @@ BipartiteSBM <-
         stopifnot(length(blockProp) ==  2, is.list(blockProp),
                   length(blockProp[[1]]) ==  nrow(connectParam$mean), # dimensions match between vector of
                   length(blockProp[[2]]) ==  ncol(connectParam$mean)) # block proportion and connectParam$mean
-        stopifnot(all(blockProp[[1]] > 0), all(blockProp[[1]] < 1))   # positive proportions
-        stopifnot(all(blockProp[[2]] > 0), all(blockProp[[2]] < 1))
+        stopifnot(all(blockProp[[1]] >= 0))   # positive proportions
+        stopifnot(all(blockProp[[2]] >= 0))
         names(blockProp) <- names(dimLabels)
 
         ## Check that connectivity parameters and model are consistent
diff --git a/R/R6Class-SimpleSBM.R b/R/R6Class-SimpleSBM.R
index 96a9ff4..c95502a 100644
--- a/R/R6Class-SimpleSBM.R
+++ b/R/R6Class-SimpleSBM.R
@@ -19,7 +19,7 @@ SimpleSBM <-
 
         ## SANITY CHECKS (on parameters)
         stopifnot(length(dimLabels) == 1)
-        stopifnot(is.atomic(blockProp), all(blockProp > 0), all(blockProp < 1)) # positive proportions
+        stopifnot(is.atomic(blockProp), all(blockProp >= 0)) # positive proportions
         stopifnot(all.equal(length(blockProp), ncol(connectParam$mean)),        # dimensions match between vector of
                   all.equal(length(blockProp), nrow(connectParam$mean)))        # block proportion and connectParam$mean
 
diff --git a/R/utils.R b/R/utils.R
index 9df7f2e..361c2a2 100644
--- a/R/utils.R
+++ b/R/utils.R
@@ -1,5 +1,6 @@
 as_indicator <- function(clustering) {
-  K <- length(unique(clustering))
+  #K <- length(unique(clustering))
+  K <- max(clustering)
   N  <- length(clustering)
   Z <- matrix(0, N, K)
   Z[cbind(seq.int(N), clustering)] <- 1
diff --git a/R/utils_plot.R b/R/utils_plot.R
index 74cda01..37152cf 100644
--- a/R/utils_plot.R
+++ b/R/utils_plot.R
@@ -115,8 +115,8 @@ plotMatrix = function(Mat, dimLabels, clustering = NULL, plotOptions = list()){
 
 
   if (!is.null(clustering)) {
-    g <- g + geom_vline(data = sepCol,mapping = aes_string(xintercept = 'sep'),size = currentOptions$line.width, col=currentOptions$line.color)
-    g <- g + geom_hline(data = sepRow + 1,mapping = aes_string(yintercept = 'sep'),size = currentOptions$line.width, col=currentOptions$line.color)
+    g <- g + geom_vline(data = sepCol,mapping = aes(xintercept = .data$sep),linewidth = currentOptions$line.width, col=currentOptions$line.color)
+    g <- g + geom_hline(data = sepRow + 1,mapping = aes(yintercept = .data$sep),linewidth = currentOptions$line.width, col=currentOptions$line.color)
   }
   if (!is.null(currentOptions$title)){g <- g + ggtitle(currentOptions$title) }
   g