diff --git a/R/formatRCTDataset.R b/R/formatRCTDataset.R
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+#' 
+#' RCT format for Virtual Twins
+#' 
+#' \code{formatRCTDataset} returns dataset that Virtual Twins is able to 
+#' analyze.
+#' 
+#' This function check these differents topic: Outcome must be binary and a 
+#' factor. If numeric with two distincts values, outcome becomes a factor where 
+#' the favorable reponse is the second level. Also, outcome is moved on the 
+#' first column of \code{dataset}.
+#' 
+#' Treatment must have two distinct numeric values, 0 : no treatment, 1 : 
+#' treatment. Treatment is moved to the second column.
+#' 
+#' Qualitatives variables must be factor. If it has more than two levels, if 
+#' running VirtualTwins with interaction, it creates dummy variables.
+#' 
+#' @param dataset data.frame representing RCT's
+#' @param outcome.field name of the outcome's field in \code{dataset}
+#' @param treatment.field name of the treatment's field in \code{dataset}
+#' @param interactions logical. If running VirtualTwins with treatment's
+#'   interactions, set to TRUE (default value)
+#' 
+#' @return return data.frame with good format (explained in details section) to run VirtualTwins
+#' 
+#' @examples
+#' \dontrun{
+#' data.format <- formatRCTDataset(data, "outcome", "treatment", TRUE)
+#' } 
+#'   
+#' @export
+formatRCTDataset <- function(dataset, outcome.field, treatment.field, interactions = TRUE){
+  
+  if(!is.data.frame(dataset)) stop("Dataset parameter must be data.frame")
+  if(!is.character(outcome.field)) stop(sprintf("%s, outcome.field parameter must be a string", outcome.field))
+  if(!is.character(treatment.field)) stop(sprintf("%s, treatment.field parameter must be a string", treatment.field))
+  
+  if(!outcome.field %in% colnames(dataset)) stop(sprintf("%s must be in data.frame colnames", outcome.field))
+  outcome.field.which <- which(outcome.field == colnames(dataset))
+  if(!treatment.field %in% colnames(dataset)) stop(sprintf("%s must be in data.frame colnames", treatment.field))
+  treatment.field.which <- which(treatment.field == colnames(dataset))
+  
+  d <- dataset
+  
+  outcome <- d[, outcome.field.which]
+  if(!is.factor(outcome)) outcome <- as.factor(outcome)
+  if(!length(levels(outcome)) == 2L) stop(sprintf("outcome %s must be binary", outcome.field))
+  cat(sprintf("\"%s\" will be the favorable outcome \n", levels(outcome)[2]))    
+  
+  treatment <- d[, treatment.field.which]
+  if(!is.numeric(treatment) & !is.integer(treatment)) treatment <- as.numeric(treatment)
+  if(!( length(unique(treatment)) == 2L & all(c(0,1) %in% unique(treatment)) ))
+    stop(sprintf("%s, response must be numeric:\n 0 = no treatment \n 1 = treatment \n", treatment.field))
+  
+  predictors <- colnames(dataset)[-c(outcome.field.which, treatment.field.which)]
+  
+  predictors.next <- vector()
+  iter = 1
+  for(i in predictors){
+    iter <- length(predictors.next)+1
+    
+    var <- d[, i]
+    
+    if(is.numeric(var) | is.integer(var)){
+      predictors.next[iter] <- i
+    }
+    
+    if(is.character(var)){
+      var <- as.factor(var)
+    }
+    
+    if(is.factor(var)){
+      if(length(levels(var))>2){
+        if(isTRUE(interactions)){
+          cat(sprintf("Creation of dummy variables for %s \n", i))
+          for(l in levels(var)){
+            n <- paste(i, l, sep = "_")
+            d[, n] <- ifelse(var == l, 1, 0)
+            predictors.next[iter] <- n
+            cat(sprintf("Dummy variable %s created \n", n))
+            iter <- iter + 1
+          }
+          #           d <- d[, predictors != i]
+        } else {
+          if(length(levels(var))>32){
+            stop(cat(sprintf("%s has too many levels (superior to 32)", i)))
+          }else{
+            warning(sprintf("%s has more than 2 levels. Virtual Twins won't be able to run with interactions.", i))
+            predictors.next[iter] <- i
+          }
+        }
+      }else if(length(levels(var)) == 2){
+        cat(sprintf("%s is two-level factor. It has to be transformed into numeric value : \n", i))
+        cat(srpintf("%s becomes 0 \n", levels(var)[1]))
+        cat(srpintf("%s becomes 1 \n", levels(var)[2]))
+        d[, i] <- ifelse(var == levels(var)[1], 0, 1)
+        predictors.next[iter] <- i
+      }else{
+        cat(sprintf("%s is deleted because only one level", i))
+      }
+    }
+  }
+  
+  colnames.order <- c(outcome.field, treatment.field, predictors.next)
+  cat(colnames.order)
+  d <- d[, colnames.order]
+  
+  return(invisible(d))
+}
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