diff --git a/R/VirtualTwins.R b/R/VirtualTwins.R
index 400aad9..68ea37f 100644
--- a/R/VirtualTwins.R
+++ b/R/VirtualTwins.R
@@ -1,15 +1,15 @@
 
-#' VirtualTwins : An adapation of VirtualTwins method created by Jared Foster.
+#' aVirtualTwins : An adapation of VirtualTwins method created by Jared Foster.
 #' 
-#' VirtualTwins is written mainly with reference classes. Briefly, there is three kinds of class :
+#' aVirtualTwins is written mainly with reference classes. Briefly, there is three kinds of class :
 #' \itemize{
-#'  \item \code{\link{VT.object}} class to represent RCT dataset used by VirtualTwins. To format correctly RCT dataset, use \code{\link{formatRCTDataset}}.
+#'  \item \code{\link{VT.object}} class to represent RCT dataset used by aVirtualTwins. To format correctly RCT dataset, use \code{\link{formatRCTDataset}}.
 #'  \item \code{\link{VT.difft}} class to compute difference between twins. Family \code{\link{VT.forest}} extends it to compute twins by random forest.
 #'  \item \code{\link{VT.tree}} class to find subgroups from \code{difft} by CART trees. \code{\link{VT.tree.class}} and \code{\link{VT.tree.reg}} extend it.
 #' }
 #'  
 #' @section TODO LIST:
-#' \emph{last update : 11.06.2015}
+#' \emph{last update : 24.07.2015}
 #' \itemize{
 #'  \item More detailed documentation and vignettes 
 #'  \item Write wrappers for classes
diff --git a/vignettes/full-example.Rmd b/vignettes/full-example.Rmd
index d1b6028..86e2773 100644
--- a/vignettes/full-example.Rmd
+++ b/vignettes/full-example.Rmd
@@ -278,7 +278,7 @@ This idea is taken from *method 3* of Jared Foster paper :
 
 > A modification of [previous methods] is to obtain $\hat{P_{1i}}$ and $\hat{P_{0i}}$ via cross-validation. In this méthod the specific data for subject $i$ is not used to obtain $\hat{P_{1i}}$ and $\hat{P_{0i}}$. Using k-fold cross-validation, we apply random forest regression approach to $\frac{k-1}{k}$ of the data and use the resulting predictor to obtain estimates of $P_{1i}$ and $P_{0i}$ for the remaining $\frac{1}{k}$ of the observations. This is repeated $k$ times.
 
-To use this approach, type `VirtualTwins:::VT.forest.fold()`. This class takes in argument :
+To use this approach, type `aVirtualTwins:::VT.forest.fold()`. This class takes in argument :
 
 * `vt.object` : return of `vt.data()` function
 * `fold` : number of fold (e.g. $5$)
@@ -289,7 +289,7 @@ __NOTE:__ This function use only `randomForest` package.
 
 ```{r, cache=TRUE}
 # initialize k-fold RF
-model.fold <- VirtualTwins:::VT.forest.fold(vt.o, fold = 5, ratio = 1, interactions = T)
+model.fold <- aVirtualTwins:::VT.forest.fold(vt.o, fold = 5, ratio = 1, interactions = T)
 # grow RF with randomForest package options
 # set do.trace option to see the 5 folds
 model.fold$run(ntree = 500, do.trace = 500)