diff --git a/DESCRIPTION b/DESCRIPTION
index c73555093bb5d336c3e7539c41c718137cdbebfc..362b4f8c2ee238a93eae8e292f5ad086fced9dc3 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -22,6 +22,8 @@ Imports:
     s2dverification,
     abind,
     stats
+Suggests:
+    testthat
 License: LGPL-3
 URL: https://earth.bsc.es/gitlab/external/cstools
 BugReports: https://earth.bsc.es/gitlab/external/cstools/issues
diff --git a/R/AnoMultiMetric.R b/R/AnoMultiMetric.R
index 1e52cd580d3143593acafc58f282330208932000..764a8d702949e4401d84012aaaf0b4fdceece8bb 100644
--- a/R/AnoMultiMetric.R
+++ b/R/AnoMultiMetric.R
@@ -1,6 +1,7 @@
 #'Multiple Metrics applied in Multiple Model Anomalies
 #'
 #'@author Mishra Niti, \email{niti.mishra@bsc.es}
+#'@author Perez-Zanon Nuria, \email{nuria.perez@bsc.es}
 #'@description This function calculates correlation (Anomaly Correlation Coefficient; ACC), root mean square error (RMS) and the root mean square error skill score (RMSSS) of individual anomaly models and multi-models mean (if desired) with the observations.
 #'
 #'@param data an s2dverification object (list) giving as output of \code{Load} function from S2dverification package.
@@ -14,9 +15,9 @@
 #' \itemize{
 #'    \item\code{$ano_exp} {an array with the same dimensions as the \code{mod} label in the input \code{data}.}
 #'    \item\code{$ano_exp} {an array with the same dimensions as the \code{obs} label in the input \code{data}.}}}
-#'\item\code{$metric} {An array with the same dimensions as the \code{mod} label in the input \code{data} without the 'ftime' dimension containing the correlations coefficients. if \code{multimodel} is TRUE, the greatest first dimension correspons. The third dimension contains the statistics selected: for \code{correlation} and \code{rms} metrics, the third dimension of length four corresponds to the lower limit of the \code{siglev}\% confidence interval, the statistics itselfs, the upper limit of the \code{siglev}\% confidence interval and the \code{siglev}\% significance level, while for the \code{rmsss}, the third dimension of length two corresponds to the statistics itselfs and the p-value of the one-sided Fisher test with Ho: RMSSS = 0.}
+#'\item\code{$metric} {An array with two datset dimensions from the \code{mod} and \code{obs} label in the input \code{data}. If \code{multimodel} is TRUE, the greatest first dimension correspons to the Multi-Model Mean. The third dimension contains the statistics selected. For metric \code{correlation}, the third dimension is of length four and they corresponds to the lower limit of the 95\% confidence interval, the statistics itselfs, the upper limit of the 95\% confidence interval and the 95\% significance level. For metric \code{rms}, the third dimension is length three and they corresponds to the lower limit of the 95\% confidence interval, the RMSE and the upper limit of the 95\% confidence interval. For metric \code{rmsss}, the third dimension is length two and they corresponds to the statistics itselfs and the p-value of the one-sided Fisher test with Ho: RMSSS = 0.}
 #'\item\code{$names} {A vector containing the names of the models.}}
-#'@seealso \code{\link[s2dverification]{Corr}} and \code{link[s2dverification]{Load}}
+#'@seealso \code{\link[s2dverification]{Corr}}, \code{\link[s2dverification]{RMS}}, \code{\link[s2dverification]{RMSSS}} and \code{\link[s2dverification]{Load}}
 #'@references 
 #' \url{http://link.springer.com/10.1007/s00382-018-4404-z}
 #' 
@@ -61,7 +62,12 @@ from s2dverification package.")
       names <- paste0('mod', 1 : dim(data$mod)[1])
     }
   }
-  
+  if (!is.null(names)) {
+    if (length(names) != dim(data$mod)[1]) {
+      stop("Parameter 'names' must be the same length as the number of 
+           models in the element 'mod' of parameter 'data'.")
+    }
+  }
   if (!is.logical(multimodel)) {
     stop("Parameter 'multimodel' must be a logical value.")
   }
@@ -78,6 +84,7 @@ from s2dverification package.")
   # seasonal average of anomalies per model
   AvgExp <- MeanListDim(ano$ano_exp, narm = T, c(2, 4))
   AvgObs <- MeanListDim(ano$ano_obs, narm = T, c(2, 4))
+  
   # indv model correlation
   if (metric == 'correlation') {
     corr <- Corr(AvgExp, AvgObs, posloop = 1, poscor = 2)
@@ -91,8 +98,8 @@ from s2dverification package.")
   }
   if (multimodel == TRUE) {
     # seasonal avg of anomalies for multi-model
-    AvgExp_MMM <- MeanListDim(AvgExp, narm = T, 1)
-    AvgObs_MMM <- MeanListDim(AvgObs, narm = T, 1)
+    AvgExp_MMM <- MeanListDim(AvgExp, narm = TRUE, 1)
+    AvgObs_MMM <- MeanListDim(AvgObs, narm = TRUE, 1)
     # multi model correlation
     if (metric == 'correlation') {
       corr_MMM <- Corr(var_exp = InsertDim(AvgExp_MMM, 1, 1),
@@ -110,6 +117,8 @@ from s2dverification package.")
        corr <- abind::abind(corr, corr_MMM, along = 1)
     names <- c(names, 'MMM')
   }
-  names(dim(corr))[3] <- 'statistics'
+  
+  names(dim(corr)) <- c(dimnames[1], dimnames[1], 'statistics', dimnames[5 : 6])
+  
   return(list(ano = ano, metric = corr, names = names))
 }
diff --git a/man/AnoMultiMetric.Rd b/man/AnoMultiMetric.Rd
index 77a1f6ac3bc03f0cc577adb9d61ea99cfff16d6a..4e8e0715efd2bd4c58c5f70725f87daa02d9eabc 100644
--- a/man/AnoMultiMetric.Rd
+++ b/man/AnoMultiMetric.Rd
@@ -23,7 +23,7 @@ A list of 3:
 \itemize{
    \item\code{$ano_exp} {an array with the same dimensions as the \code{mod} label in the input \code{data}.}
    \item\code{$ano_exp} {an array with the same dimensions as the \code{obs} label in the input \code{data}.}}}
-\item\code{$metric} {An array with the same dimensions as the \code{mod} label in the input \code{data} without the 'ftime' dimension containing the correlations coefficients. if \code{multimodel} is TRUE, the greatest first dimension correspons. The third dimension contains the statistics selected: for \code{correlation} and \code{rms} metrics, the third dimension of length four corresponds to the lower limit of the \code{siglev}\% confidence interval, the statistics itselfs, the upper limit of the \code{siglev}\% confidence interval and the \code{siglev}\% significance level, while for the \code{rmsss}, the third dimension of length two corresponds to the statistics itselfs and the p-value of the one-sided Fisher test with Ho: RMSSS = 0.}
+\item\code{$metric} {An array with two datset dimensions from the \code{mod} and \code{obs} label in the input \code{data}. If \code{multimodel} is TRUE, the greatest first dimension correspons to the Multi-Model Mean. The third dimension contains the statistics selected. For metric \code{correlation}, the third dimension is of length four and they corresponds to the lower limit of the 95\% confidence interval, the statistics itselfs, the upper limit of the 95\% confidence interval and the 95\% significance level. For metric \code{rms}, the third dimension is length three and they corresponds to the lower limit of the 95\% confidence interval, the RMSE and the upper limit of the 95\% confidence interval. For metric \code{rmsss}, the third dimension is length two and they corresponds to the statistics itselfs and the p-value of the one-sided Fisher test with Ho: RMSSS = 0.}
 \item\code{$names} {A vector containing the names of the models.}}
 }
 \description{
@@ -44,8 +44,10 @@ str(a)
 \url{http://link.springer.com/10.1007/s00382-018-4404-z}
 }
 \seealso{
-\code{\link[s2dverification]{Corr}} and \code{link[s2dverification]{Load}}
+\code{\link[s2dverification]{Corr}}, \code{\link[s2dverification]{RMS}}, \code{\link[s2dverification]{RMSSS}} and \code{\link[s2dverification]{Load}}
 }
 \author{
 Mishra Niti, \email{niti.mishra@bsc.es}
+
+Perez-Zanon Nuria, \email{nuria.perez@bsc.es}
 }
diff --git a/tests/testthat.R b/tests/testthat.R
new file mode 100644
index 0000000000000000000000000000000000000000..009a2fc85ed678aba94520c6cffaf441ff659071
--- /dev/null
+++ b/tests/testthat.R
@@ -0,0 +1,4 @@
+library(testthat)
+library(CSTools)
+
+test_check("CSTools")
diff --git a/tests/testthat/test-AnoMultiMetric.R b/tests/testthat/test-AnoMultiMetric.R
new file mode 100644
index 0000000000000000000000000000000000000000..56de35dd7ee6e07ea5ae4e6e4dac7b705645d607
--- /dev/null
+++ b/tests/testthat/test-AnoMultiMetric.R
@@ -0,0 +1,77 @@
+context("Generic tests")
+test_that("basic use case", {
+  mod <- 1 : (2 * 3 * 4 * 5 * 6 * 8)
+  dim(mod) <- c(dataset = 2, member = 3, sdate = 4, ftime = 5, lat = 6, lon = 8)
+  obs <- 1 : (1 * 1 * 4 * 5 * 6 * 8)
+  dim(obs) <- c(dataset = 1, member = 1, sdate = 4, ftime = 5, lat = 6, lon = 8)
+  lon <- seq(0, 30, 5)
+  lat <- seq(0, 30, 5)
+  dat <- list(mod = mod, obs = obs, lat = lat, lon = lon)
+
+  expect_equal(
+    AnoMultiMetric(dat),
+    list(ano = list(ano_exp = array(rep(
+		    c(rep(-12, 6), rep(-4, 6), rep(4, 6), rep(12, 6)), 360),
+                    dim = c(dataset = 2, member = 3, sdate = 4, ftime = 5, 
+                            lat = 6, lon = 8)),
+                    ano_obs = array(rep(c(-2, -2/3,  2/3,  2), 360),
+                    dim = c(dataset = 1, member = 1, sdate = 4, ftime = 5,
+                            lat = 6, lon = 8))),
+         metric = array(rep(c(rep(1, 9), rep(0.9, 3)), 48), 
+                        dim = c(dataset = 3, dataset = 1, statistics = 4, lat = 6, lon = 8)),
+	 names = c("mod1", "mod2", "MMM")))
+  
+  res <- AnoMultiMetric(dat, metric = 'rms')
+  expect_equal(length(res), 3)
+  expect_equal(length(res$ano), 2)
+  expect_equal(dim(res$ano$ano_exp), 
+        c(dataset = 2, member = 3, sdate = 4, ftime = 5, lat = 6, lon = 8))
+  expect_equal(dim(res$ano$ano_obs), 
+        c(dataset = 1, member = 1, sdate = 4, ftime = 5, lat = 6, lon = 8))
+  expect_equal(dim(res$metric), 
+        c(dataset = 3, dataset = 1, statistics = 3, lat = 6, lon = 8))
+  res <- AnoMultiMetric(dat, metric = 'rms', multimodel = FALSE)
+  expect_equal(dim(res$metric), 
+        c(dataset = 2, dataset = 1, statistics = 3, lat = 6, lon = 8))
+  res <- AnoMultiMetric(dat, metric = 'rmsss')
+  expect_equal(dim(res$metric), 
+        c(dataset = 3, dataset = 1, statistics = 2, lat = 6, lon = 8))
+  res <- AnoMultiMetric(dat, metric = 'rmsss', multimodel = FALSE)
+  expect_equal(dim(res$metric), 
+        c(dataset = 2, dataset = 1, statistics = 2, lat = 6, lon =8))
+  res <- AnoMultiMetric(dat, names = c('A', 'B'))
+  expect_equal(res$names, c('A', 'B', 'MMM'))
+})
+
+
+test_that("Sanity checks", {
+  expect_error(
+    AnoMultiMetric(data = 1),
+      "Parameter 'data' must be a list as output of Load function
+from s2dverification package")
+  mod <- 1 : (2 * 3 * 4 * 5 * 6 * 8)
+  dim(mod) <- c(dataset = 2, member = 3, sdate = 4, ftime = 5, lat = 6, lon = 8)
+  obs <- 1 : (1 * 1 * 4 * 5 * 6 * 8)
+  dim(obs) <- c(dataset = 1, member = 1, sdate = 4, ftime = 5, lat = 6, lon = 8)
+  lon <- seq(0, 30, 5)
+  lat <- seq(0, 30, 5)
+  dat <- list(mod = mod, obs = obs, lat = lat, lon = lon)
+  
+  expect_error(
+    AnoMultiMetric(data = dat, metric = 1),
+    "Parameter 'metric' must be a character string indicating one of the 
+         options: 'correlation', 'rms' or 'rmse'")
+  expect_error(
+    AnoMultiMetric(data = dat, metric = NA),
+    "missing value where TRUE/FALSE needed")
+  expect_error(
+    AnoMultiMetric(data = dat, metric = NULL),
+    "argument is of length zero")
+  expect_error(
+    AnoMultiMetric(data = dat, metric = "correlation", multimodel = NULL),
+    "Parameter 'multimodel' must be a logical value.")
+  expect_error(
+    AnoMultiMetric(data = dat, metric = "correlation", multimodel = FALSE, names = 1),
+    "Parameter 'names' must be the same length as the number of 
+           models in the element 'mod' of parameter 'data'.")
+})