help js

R/jsTopics.R

@@ -7,10 +7,13 @@
 #' @details
 #' The Jensen-Shannon Similarity for two topics \eqn{\bm z_{i}} and
 #' \eqn{\bm z_{j}} is calculated by
-#' \deqn{\text{JS}(\bm z_{i}, \bm z_{j}) = 1 - \left( \text{KLD}(p_i, (p_i+p_j)/2) + \text{KLD}((p_j, (p_i+p_j)/2)) \right)/2 }
-#'
-#' with \eqn{V} is the vocabulary size and \eqn{n_k^{(v)}} is the count of
-#' assignments of the \eqn{v}-th word to the \eqn{k}-th topic.
+#' \deqn{JS(\bm z_{i}, \bm z_{j}) = 1 - \left( KLD(p_i, (p_i+p_j)/2) + KLD((p_j, (p_i+p_j)/2)) \right)/2}
+#' \deqn{= 1 - \left( KLD(p_i, p_i+p_j) + KLD((p_j, p_i+p_j)) \right)/2 - \log(2)}
+#' with \eqn{V} is the vocabulary size, \eqn{p_k = (p_k^{(1)}, ..., p_k^{(V)})},
+#' and \eqn{p_k^{(v)}} is the proportion of assignments of the
+#' \eqn{v}-th word to the \eqn{k}-th topic. KLD defines the Kullback-Leibler
+#' Divergence calculated by
+#' \deqn{KLD(\bm p_{k}, \bm p_{\Sigma}) = \sum_{v=1}^{V} p_k^{(v)} \log{\frac{p_k^{(v)}}{p_{\Sigma}^{(v)}}}.}
 #'
 #' @family TopicSimilarity functions
 #'