diff --git a/R/createJSON.R b/R/createJSON.R
index 33f77e4..ed470aa 100644
--- a/R/createJSON.R
+++ b/R/createJSON.R
@@ -294,18 +294,22 @@ createJSON <- function(phi = matrix(), theta = matrix(), doc.length = integer(),
 #' many columns as there are terms in the vocabulary.
 #' 
 #' @export
+
 jsPCA <- function(phi) {
   # first, we compute a pairwise distance between topic distributions
   # using a symmetric version of KL-divergence
   # http://en.wikipedia.org/wiki/Jensen%E2%80%93Shannon_divergence
   jensenShannon <- function(x, y) {
-    m <- 0.5 * (x + y)
-    lhs <- ifelse(x == 0, 0, x * (log(x) - log(m)))
-    rhs <- ifelse(y == 0, 0, y * (log(y) - log(m)))
-    0.5 * sum(lhs) + 0.5 * sum(rhs)
-  }
+  eps <- 1e-10  # Small epsilon value
+  m <- 0.5 * (x + y)
+  lhs <- ifelse(x == 0, 0, x * (log(x + eps) - log(m + eps)))
+  rhs <- ifelse(y == 0, 0, y * (log(y + eps) - log(m + eps)))
+  0.5 * sum(lhs) + 0.5 * sum(rhs)
+}
   dist.mat <- proxy::dist(x = phi, method = jensenShannon)
   # then, we reduce the K by K proximity matrix down to K by 2 using PCA
   pca.fit <- stats::cmdscale(dist.mat, k = 2)
   data.frame(x = pca.fit[,1], y = pca.fit[,2])
 }
+
+