diff --git a/tests/testthat/test-rankThenAggregate.R b/tests/testthat/test-rankThenAggregate.R
index ae63900..8ce5459 100644
--- a/tests/testthat/test-rankThenAggregate.R
+++ b/tests/testthat/test-rankThenAggregate.R
@@ -1,286 +1,286 @@
 test_that("rank-then-aggregate by mean works with two algorithms for one case, small values are better", {
   data <- rbind(
     data.frame(algo="A1", value=0.6, case="C1"),
     data.frame(algo="A2", value=0.8, case="C1"))
 
-  challenge <- as.challenge(data, algorithm="algo", case="case", value="value", smallBetter = TRUE)
+  challenge <- as.challenge(data, taskName="T1", algorithm="algo", case="case", value="value", smallBetter = TRUE)
 
   ranking <- challenge%>%rankThenAggregate(FUN = mean)
 
   expectedRanking <- rbind(
-    "A1" = data.frame(rank_mean = 1, rank = 1),
-    "A2" = data.frame(rank_mean = 2, rank = 2))
+    "A1" = data.frame(rank_FUN = 1, rank = 1),
+    "A2" = data.frame(rank_FUN = 2, rank = 2))
 
-  expect_equal(ranking$mat, expectedRanking)
+  expect_equal(ranking$matlist$T1, expectedRanking)
 })
 
 test_that("rank-then-aggregate by mean works with two algorithms (reverse order) for one case, small values are better", {
   data <- rbind(
             data.frame(algo = "A2", value = 0.8, case = "C1"),
             data.frame(algo = "A1", value = 0.6, case = "C1"))
 
-  challenge <- as.challenge(data, algorithm = "algo", case = "case", value = "value", smallBetter = TRUE)
+  challenge <- as.challenge(data, taskName="T1", algorithm = "algo", case = "case", value = "value", smallBetter = TRUE)
 
   ranking <- challenge%>%rankThenAggregate(FUN = mean)
 
-  expectedRanking <- rbind("A2" = data.frame(rank_mean = 2, rank = 2),
-                           "A1" = data.frame(rank_mean = 1, rank = 1))
+  expectedRanking <- rbind("A2" = data.frame(rank_FUN = 2, rank = 2),
+                           "A1" = data.frame(rank_FUN = 1, rank = 1))
 
-  expect_equal(ranking$mat, expectedRanking)
+  expect_equal(ranking$matlist$T1, expectedRanking)
 })
 
 test_that("rank-then-aggregate by mean works with two algorithms for one case, large values are better", {
   data <- rbind(
     data.frame(algo="A1", value=0.6, case="C1"),
     data.frame(algo="A2", value=0.8, case="C1"))
 
-  challenge <- as.challenge(data, algorithm="algo", case="case", value="value", smallBetter = FALSE)
+  challenge <- as.challenge(data, taskName="T1", algorithm="algo", case="case", value="value", smallBetter = FALSE)
 
   ranking <- challenge%>%rankThenAggregate(FUN = mean)
 
   expectedRanking <- rbind(
-    "A1" = data.frame(rank_mean = 2, rank = 2),
-    "A2" = data.frame(rank_mean = 1, rank = 1))
+    "A1" = data.frame(rank_FUN = 2, rank = 2),
+    "A2" = data.frame(rank_FUN = 1, rank = 1))
 
-  expect_equal(ranking$mat, expectedRanking)
+  expect_equal(ranking$matlist$T1, expectedRanking)
 })
 
 test_that("rank-then-aggregate by mean works with two algorithms (reverse order) for one case, large values are better", {
   data <- rbind(
     data.frame(algo = "A2", value = 0.8, case = "C1"),
     data.frame(algo = "A1", value = 0.6, case = "C1"))
 
-  challenge <- as.challenge(data, algorithm = "algo", case = "case", value = "value", smallBetter = FALSE)
+  challenge <- as.challenge(data, taskName="T1", algorithm = "algo", case = "case", value = "value", smallBetter = FALSE)
 
   ranking <- challenge%>%rankThenAggregate(FUN = mean)
 
-  expectedRanking <- rbind("A2" = data.frame(rank_mean = 1, rank = 1),
-                           "A1" = data.frame(rank_mean = 2, rank = 2))
+  expectedRanking <- rbind("A2" = data.frame(rank_FUN = 1, rank = 1),
+                           "A1" = data.frame(rank_FUN = 2, rank = 2))
 
-  expect_equal(ranking$mat, expectedRanking)
+  expect_equal(ranking$matlist$T1, expectedRanking)
 })
 
 test_that("rank-then-aggregate raises error for invalid aggregation function", {
   data <- rbind(
     data.frame(algo="A1", value=0.6, case="C1"),
     data.frame(algo="A2", value=0.6, case="C1"))
 
-  challenge <- as.challenge(data, algorithm="algo", case="case", value="value", smallBetter = TRUE)
+  challenge <- as.challenge(data, taskName="T1", algorithm="algo", case="case", value="value", smallBetter = TRUE)
 
   expect_error(challenge%>%rankThenAggregate(FUN = meanx),
                "object 'meanx' not found", fixed = TRUE)
 })
 
 test_that("rank-then-aggregate by mean works with two algorithms for one case and 'min' as ties method", {
   data <- rbind(
     data.frame(algo="A1", value=0.6, case="C1"),
     data.frame(algo="A2", value=0.6, case="C1"))
 
-  challenge <- as.challenge(data, algorithm="algo", case="case", value="value", smallBetter = TRUE)
+  challenge <- as.challenge(data, taskName="T1", algorithm="algo", case="case", value="value", smallBetter = TRUE)
 
   ranking <- challenge%>%rankThenAggregate(FUN = mean, ties.method = "min")
 
   expectedRanking <- rbind(
-    "A1" = data.frame(rank_mean = 1, rank = 1),
-    "A2" = data.frame(rank_mean = 1, rank = 1))
+    "A1" = data.frame(rank_FUN = 1, rank = 1),
+    "A2" = data.frame(rank_FUN = 1, rank = 1))
 
-  expect_equal(ranking$mat, expectedRanking)
+  expect_equal(ranking$matlist$T1, expectedRanking)
 })
 
 test_that("rank-then-aggregate by mean works with two algorithms for one case and 'max' as ties method", {
   data <- rbind(
     data.frame(algo="A1", value=0.6, case="C1"),
     data.frame(algo="A2", value=0.6, case="C1"))
 
-  challenge <- as.challenge(data, algorithm="algo", case="case", value="value", smallBetter = TRUE)
+  challenge <- as.challenge(data, taskName="T1", algorithm="algo", case="case", value="value", smallBetter = TRUE)
 
   ranking <- challenge%>%rankThenAggregate(FUN = mean, ties.method = "max")
 
   expectedRanking <- rbind(
-    "A1" = data.frame(rank_mean = 2, rank = 2),
-    "A2" = data.frame(rank_mean = 2, rank = 2))
+    "A1" = data.frame(rank_FUN = 2, rank = 2),
+    "A2" = data.frame(rank_FUN = 2, rank = 2))
 
-  expect_equal(ranking$mat, expectedRanking)
+  expect_equal(ranking$matlist$T1, expectedRanking)
 })
 
 test_that("rank-then-aggregate raises error for invalid ties method", {
   data <- rbind(
     data.frame(algo="A1", value=0.6, case="C1"),
     data.frame(algo="A2", value=0.6, case="C1"))
 
-  challenge <- as.challenge(data, algorithm="algo", case="case", value="value", smallBetter = TRUE)
+  challenge <- as.challenge(data, taskName="T1", algorithm="algo", case="case", value="value", smallBetter = TRUE)
 
   expect_error(challenge%>%rankThenAggregate(FUN = mean, ties.method = "maxx"),
                "'arg' should be one of \"average\", \"first\", \"last\", \"random\", \"max\", \"min\"", fixed = TRUE)
 })
 
 test_that("rank-then-aggregate raises error for invalid ties method even when no ties present", {
   data <- rbind(
     data.frame(algo="A1", value=0.6, case="C1"),
     data.frame(algo="A2", value=0.8, case="C1"))
 
-  challenge <- as.challenge(data, algorithm="algo", case="case", value="value", smallBetter = TRUE)
+  challenge <- as.challenge(data, taskName="T1", algorithm="algo", case="case", value="value", smallBetter = TRUE)
 
   expect_error(challenge%>%rankThenAggregate(FUN = mean, ties.method = "maxx"),
                "'arg' should be one of \"average\", \"first\", \"last\", \"random\", \"max\", \"min\"", fixed = TRUE)
 })
 
 test_that("rank-then-aggregate by mean works with two algorithms for two cases", {
   data <- rbind(
     data.frame(algo="A1", value=0.6, case="C1"),
     data.frame(algo="A1", value=0.4, case="C2"),
     data.frame(algo="A2", value=0.8, case="C1"),
     data.frame(algo="A2", value=1.0, case="C2"))
 
-  challenge <- as.challenge(data, algorithm="algo", case="case", value="value", smallBetter = TRUE)
+  challenge <- as.challenge(data, taskName="T1", algorithm="algo", case="case", value="value", smallBetter = TRUE)
 
   ranking <- challenge%>%rankThenAggregate(FUN = mean)
 
   expectedRanking <- rbind(
-    "A1" = data.frame(rank_mean = 1, rank = 1),
-    "A2" = data.frame(rank_mean = 2, rank = 2))
+    "A1" = data.frame(rank_FUN = 1, rank = 1),
+    "A2" = data.frame(rank_FUN = 2, rank = 2))
 
-  expect_equal(ranking$mat, expectedRanking)
+  expect_equal(ranking$matlist$T1, expectedRanking)
 })
 
 test_that("rank-then-aggregate by median works with two algorithms for two cases", {
   data <- rbind(
     data.frame(algo="A1", value=0.6, case="C1"),
     data.frame(algo="A1", value=0.4, case="C2"),
     data.frame(algo="A2", value=0.8, case="C1"),
     data.frame(algo="A2", value=1.0, case="C2"))
 
-  challenge <- as.challenge(data, algorithm="algo", case="case", value="value", smallBetter = TRUE)
+  challenge <- as.challenge(data, taskName="T1", algorithm="algo", case="case", value="value", smallBetter = TRUE)
 
   ranking <- challenge%>%rankThenAggregate(FUN = median)
 
   expectedRanking <- rbind(
-    "A1" = data.frame(rank_median = 1, rank = 1),
-    "A2" = data.frame(rank_median = 2, rank = 2))
+    "A1" = data.frame(rank_FUN = 1, rank = 1),
+    "A2" = data.frame(rank_FUN = 2, rank = 2))
 
-  expect_equal(ranking$mat, expectedRanking)
+  expect_equal(ranking$matlist$T1, expectedRanking)
 })
 
 test_that("rank-then-aggregate by mean works with one algorithm for one case", {
   data <- rbind(
     data.frame(algo="A1", value=0.6, case="C1"))
 
-  challenge <- as.challenge(data, algorithm="algo", case="case", value="value", smallBetter = TRUE)
+  challenge <- as.challenge(data, taskName="T1", algorithm="algo", case="case", value="value", smallBetter = TRUE)
 
   ranking <- challenge%>%rankThenAggregate(FUN = mean)
 
   expectedRanking <- rbind(
-    "A1" = data.frame(rank_mean = 1, rank = 1))
+    "A1" = data.frame(rank_FUN = 1, rank = 1))
 
-  expect_equal(ranking$mat, expectedRanking)
+  expect_equal(ranking$matlist$T1, expectedRanking)
 })
 
 test_that("rank-then-aggregate assigns worst rank for NA", {
   data <- rbind(
     data.frame(algo="A1", value=NA, case="C1"),
     data.frame(algo="A2", value=0.8, case="C1"))
 
-  challenge <- as.challenge(data, algorithm="algo", case="case", value="value", smallBetter = FALSE)
+  challenge <- as.challenge(data, taskName="T1", algorithm="algo", case="case", value="value", smallBetter = FALSE)
 
   ranking <- challenge%>%rankThenAggregate(FUN = mean)
 
   expectedRanking <- rbind(
-    "A1" = data.frame(rank_mean = 2, rank = 2),
-    "A2" = data.frame(rank_mean = 1, rank = 1))
+    "A1" = data.frame(rank_FUN = 2, rank = 2),
+    "A2" = data.frame(rank_FUN = 1, rank = 1))
 
-  expect_equal(ranking$mat, expectedRanking)
+  expect_equal(ranking$matlist$T1, expectedRanking)
 })
 
 test_that("rank-then-aggregate raises error for unused NA treatment argument", {
   data <- rbind(
     data.frame(algo="A1", value=NA, case="C1"),
     data.frame(algo="A2", value=0.8, case="C1"))
 
-  challenge <- as.challenge(data, algorithm="algo", case="case", value="value", smallBetter = FALSE)
+  challenge <- as.challenge(data, taskName="T1", algorithm="algo", case="case", value="value", smallBetter = FALSE)
 
   expect_error(challenge%>%rankThenAggregate(FUN = mean, na.treat = 0),
                "unused argument (na.treat = 0)", fixed = TRUE)
 })
 
 test_that("rank-then-aggregate by mean works for multi-task challenge (2 tasks in data set), no missing data", {
   dataTask1 <- cbind(task="T1",
                      rbind(
                        data.frame(algo="A1", value=0.6, case="C1"),
                        data.frame(algo="A2", value=0.8, case="C1")
                      ))
 
   dataTask2 <- cbind(task="T2",
                      rbind(
                        data.frame(algo="A1", value=0.5, case="C1"),
                        data.frame(algo="A2", value=0.4, case="C1")
                      ))
 
   data <- rbind(dataTask1, dataTask2)
 
   challenge <- as.challenge(data, by="task", algorithm="algo", case="case", value="value", smallBetter = TRUE)
 
   ranking <- challenge%>%rankThenAggregate(FUN = mean)
 
   expectedRankingTask1 <- rbind(
     "A1" = data.frame(rank_FUN = 1, rank = 1),
     "A2" = data.frame(rank_FUN = 2, rank = 2))
 
   expectedRankingTask2 <- rbind(
     "A1" = data.frame(rank_FUN = 2, rank = 2),
     "A2" = data.frame(rank_FUN = 1, rank = 1))
 
   expect_equal(ranking$matlist$T1, expectedRankingTask1)
   expect_equal(ranking$matlist$T2, expectedRankingTask2)
 })
 
 test_that("rank-then-aggregate assigns worst rank for NA in multi-task challenge (2 tasks in data set)", {
   dataTask1 <- cbind(task="T1",
                      rbind(
                        data.frame(algo="A1", value=0.6, case="C1"),
                        data.frame(algo="A2", value=0.8, case="C1")
                      ))
 
   dataTask2 <- cbind(task="T2",
                      rbind(
                        data.frame(algo="A1", value=NA, case="C1"),
                        data.frame(algo="A2", value=0.4, case="C1")
                      ))
 
   data <- rbind(dataTask1, dataTask2)
 
   challenge <- as.challenge(data, by="task", algorithm="algo", case="case", value="value", smallBetter = TRUE)
 
   ranking <- challenge%>%rankThenAggregate(FUN = mean)
 
   expectedRankingTask1 <- rbind(
     "A1" = data.frame(rank_FUN = 1, rank = 1),
     "A2" = data.frame(rank_FUN = 2, rank = 2))
 
   expectedRankingTask2 <- rbind(
     "A1" = data.frame(rank_FUN = 2, rank = 2),
     "A2" = data.frame(rank_FUN = 1, rank = 1))
 
   expect_equal(ranking$matlist$T1, expectedRankingTask1)
   expect_equal(ranking$matlist$T2, expectedRankingTask2)
 })
 
 test_that("rank-then-aggregate raises error for unused NA treatment argument in multi-task challenge (2 tasks in data set)", {
   dataTask1 <- cbind(task="T1",
                      rbind(
                        data.frame(algo="A1", value=0.6, case="C1"),
                        data.frame(algo="A2", value=0.8, case="C1")
                      ))
 
   dataTask2 <- cbind(task="T2",
                      rbind(
                        data.frame(algo="A1", value=NA, case="C1"),
                        data.frame(algo="A2", value=0.4, case="C1")
                      ))
 
   data <- rbind(dataTask1, dataTask2)
 
   challenge <- as.challenge(data, by="task", algorithm="algo", case="case", value="value", smallBetter = TRUE)
 
   expect_error(challenge%>%rankThenAggregate(FUN = mean, na.treat = "na.rm"),
                "unused argument (na.treat = \"na.rm\")", fixed = TRUE)
 })