diff --git a/R/outForest.R b/R/outForest.R
index 76dc6cb..d771ffa 100644
--- a/R/outForest.R
+++ b/R/outForest.R
@@ -20,9 +20,9 @@
 #' `allow_predictions`, it can be applied to new data.
 #'
 #' The outlier score of the ith value \eqn{x_{ij}} of the jth variable is defined as
-#' \eqn{s_{ij} = (x_{ij} - p_{ij}) / \text{rmse}_j}, where \eqn{p_{ij}}
+#' \eqn{s_{ij} = (x_{ij} - p_{ij}) / \textrm{rmse}_j}, where \eqn{p_{ij}}
 #' is the corresponding out-of-bag prediction of the jth random forest and
-#' \eqn{\text{rmse}_j} its RMSE. If \eqn{|s_{ij}| > L} with
+#' \eqn{\textrm{rmse}_j} its RMSE. If \eqn{|s_{ij}| > L} with
 #' threshold \eqn{L}, then \eqn{x_{ij}} is considered an outlier.
 #'
 #' For large data sets, just by chance, many values can surpass the default threshold
diff --git a/man/outForest.Rd b/man/outForest.Rd
index 9a7135f..7cbed6b 100644
--- a/man/outForest.Rd
+++ b/man/outForest.Rd
@@ -109,9 +109,9 @@ mean by a random forest. If the method is trained on a reference data with optio
 \code{allow_predictions}, it can be applied to new data.
 
 The outlier score of the ith value \eqn{x_{ij}} of the jth variable is defined as
-\eqn{s_{ij} = (x_{ij} - p_{ij}) / \text{rmse}_j}, where \eqn{p_{ij}}
+\eqn{s_{ij} = (x_{ij} - p_{ij}) / \textrm{rmse}_j}, where \eqn{p_{ij}}
 is the corresponding out-of-bag prediction of the jth random forest and
-\eqn{\text{rmse}_j} its RMSE. If \eqn{|s_{ij}| > L} with
+\eqn{\textrm{rmse}_j} its RMSE. If \eqn{|s_{ij}| > L} with
 threshold \eqn{L}, then \eqn{x_{ij}} is considered an outlier.
 
 For large data sets, just by chance, many values can surpass the default threshold