diff --git a/R/rma.R b/R/rma.R
index 0213427..c22e6f9 100644
--- a/R/rma.R
+++ b/R/rma.R
@@ -1,42 +1,41 @@
-#' @title rma uni model
-#' @description
-#' Using metafor package to fit a rma uni model.
-#' @param yi
-#' A \code{string} of the variable which holds the vector of length k with the observed effect sizes or outcomes in the selected dataset (d)
-#' @param vi
-#' A \code{string} of the variable which holds the vector of length k with the corresponding sampling variances in the selected dataset (d)
-#' @param d
-#' A \code{string} representing the dataset name that should be used for fitting.
-#' @return
-#' rma uni model
-#' also creates a json file (imgHeight.json) that is used in a later api call to define the height of the plots
-#' @author Robert Studtrucker
-#' @export
-rma <- function(yi,vi,measure,d,effect="Effect") {
-  #library('metafor')
-  #library("ggplot2")
-  #library('jsonlite')
-  #library('psych')
-
-  requireNamespace("metafor")
-  requireNamespace("ggplot2")
-  requireNamespace("jsonlite")
-  requireNamespace("psych")
-
-  #load the in variable d defined dataset from the package
-  dat <- checkData(d)
-  checkParameter(dat,c(yi,vi))
-  dat <- dat[order(dat$r_year),]
-
-  if(measure == "COR") {
-    # z-standardisierte Daten erstellen
-    temp_dat <- metafor::escalc(measure="ZCOR", ri=dat[,yi], vi=dat[,vi], ni=dat[,"o_ni"], data=dat, var.names=c("o_zcor","o_zcor_var"))
-
-    # Modell berechnen
-    rma_model <- metafor::rma.uni(temp_dat[,"o_zcor"],temp_dat[,"o_zcor_var"], measure="ZCOR",slab=paste(dat$r_author, dat$r_year))
-
-  }else{
-    rma_model <- metafor::rma.uni(yi=dat[,yi],vi=dat[,vi],measure=measure,slab=paste(dat$r_author, dat$r_year))
-
-  }
-}
+#' @title rma uni model
+#' @description
+#' Using metafor package to fit a rma uni model.
+#' @param yi
+#' A \code{string} of the variable which holds the vector of length k with the observed effect sizes or outcomes in the selected dataset (d)
+#' @param vi
+#' A \code{string} of the variable which holds the vector of length k with the corresponding sampling variances in the selected dataset (d)
+#' @param d
+#' A \code{string} representing the dataset name that should be used for fitting.
+#' @return
+#' rma uni model
+#' also creates a json file (imgHeight.json) that is used in a later api call to define the height of the plots
+#' @author Robert Studtrucker
+#' @export
+rma <- function(yi,vi,measure,d,effect="Effect") {
+  #library('metafor')
+  #library("ggplot2")
+  #library('jsonlite')
+  #library('psych')
+
+  requireNamespace("metafor")
+  requireNamespace("ggplot2")
+  requireNamespace("jsonlite")
+  requireNamespace("psych")
+
+  #load the in variable d defined dataset from the package
+  dat <- d
+  checkParameter(dat,c(yi,vi))
+  dat <- dat[order(dat$r_year),]
+
+  if(measure == "COR") {
+    # z-standardisierte Daten erstellen
+    temp_dat <- metafor::escalc(measure="ZCOR", ri=dat[,yi], vi=dat[,vi], ni=dat[,"o_ni"], data=dat, var.names=c("o_zcor","o_zcor_var"))
+
+    # Modell berechnen
+    rma_model <- metafor::rma.uni(temp_dat[,"o_zcor"],temp_dat[,"o_zcor_var"], measure="ZCOR",slab=paste(dat$r_author, dat$r_year))
+
+  }else{
+    rma_model <- metafor::rma.uni(yi=dat[,yi],vi=dat[,vi],measure=measure,slab=paste(dat$r_author, dat$r_year))
+  }
+}