diff --git a/R/greedyOpt.R b/R/greedyOpt.R
index 75cab8ec..1441809d 100644
--- a/R/greedyOpt.R
+++ b/R/greedyOpt.R
@@ -160,6 +160,7 @@ predict.greedyMSE <- function(object, newdata, return_labels = FALSE, ...) {
 greedyMSE_caret <- function() {
   list(
     label = "Greedy Mean Squared Error Optimizer",
+    method = "greedyMSE",
     library = NULL,
     loop = NULL,
     type = c("Regression", "Classification"),
diff --git a/coverage.rds b/coverage.rds
index 247b11a1..32488b6c 100644
Binary files a/coverage.rds and b/coverage.rds differ
diff --git a/tests/testthat/test-caretList.R b/tests/testthat/test-caretList.R
index 8d2ce95e..54606c7f 100644
--- a/tests/testthat/test-caretList.R
+++ b/tests/testthat/test-caretList.R
@@ -254,3 +254,44 @@ testthat::test_that("caretList supports combined regression, binary, multiclass"
   testthat::expect_identical(nrow(stacked_p), nrow(iris))
   testthat::expect_identical(nrow(new_p), 10L)
 })
+
+testthat::test_that("caretList supports custom models", {
+  set.seed(42L)
+
+  # Use the custom greedyMSE model
+  custom_list <- list(
+    custom.mse = caretModelSpec(method = greedyMSE_caret(), tuneLength = 1L)
+  )
+
+  # Fit it reg/bin/multi (it supports all 3!)
+  reg_models <- caretList(Sepal.Length ~ Sepal.Width, iris, tuneList = custom_list)
+  bin_models <- caretList(factor(ifelse(Species == "setosa", "Y", "N")) ~ Sepal.Width, iris, tuneList = custom_list)
+  multi_models <- caretList(Species ~ Sepal.Width, iris, tuneList = custom_list)
+
+  # Check the fit
+  all_models <- c(reg_models, bin_models, multi_models)
+  testthat::expect_s3_class(all_models, "caretList")
+
+  # Check predictions
+  stacked_p <- predict(all_models)
+  new_p <- predict(all_models, newdata = iris[1L:10L, ])
+  testthat::expect_is(stacked_p, "data.table")
+  testthat::expect_is(new_p, "data.table")
+  testthat::expect_identical(nrow(stacked_p), nrow(iris))
+  testthat::expect_identical(nrow(new_p), 10L)
+
+  # Check we can stack it
+  # Note that caretStack with method=greedyMSE_caret()
+  # is what caretEnsemble does under the hood
+  ens <- caretStack(
+    all_models,
+    method = greedyMSE_caret(),
+    trControl = trainControl(method = "cv", number = 2L, savePredictions = "final")
+  )
+  stacked_p <- predict(ens)
+  new_p <- predict(ens, newdata = iris[1L:10L, ])
+  testthat::expect_is(stacked_p, "data.table")
+  testthat::expect_is(new_p, "data.table")
+  testthat::expect_identical(nrow(stacked_p), nrow(iris))
+  testthat::expect_identical(nrow(new_p), 10L)
+})