diff --git a/DESCRIPTION b/DESCRIPTION
index 03cd5cc85..8c77e9321 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -59,7 +59,6 @@ Imports:
     data.table (>= 1.16.0),
     ggplot2 (>= 3.4.0),
     methods,
-    Metrics,
     purrr,
     scoringRules (>= 1.1.3),
     stats
@@ -67,6 +66,7 @@ Suggests:
     ggdist,
     kableExtra,
     knitr,
+    Metrics,
     rmarkdown,
     testthat (>= 3.1.9),
     vdiffr
diff --git a/NAMESPACE b/NAMESPACE
index 151aa6149..414b9baf8 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -109,9 +109,6 @@ export(transform_forecasts)
 export(underprediction_quantile)
 export(underprediction_sample)
 export(wis)
-importFrom(Metrics,ae)
-importFrom(Metrics,ape)
-importFrom(Metrics,se)
 importFrom(checkmate,assert)
 importFrom(checkmate,assert_character)
 importFrom(checkmate,assert_class)
diff --git a/R/class-forecast-point.R b/R/class-forecast-point.R
index 8635b25b9..820b8c39c 100644
--- a/R/class-forecast-point.R
+++ b/R/class-forecast-point.R
@@ -75,7 +75,6 @@ is_forecast_point <- function(x) {
 }
 
 
-#' @importFrom Metrics se ae ape
 #' @importFrom stats na.omit
 #' @importFrom data.table setattr copy
 #' @rdname score
@@ -100,9 +99,9 @@ score.forecast_point <- function(forecast, metrics = get_metrics(forecast), ...)
 #'
 #' @description
 #' For point forecasts, the default scoring rules are:
-#' - "ae_point" = [ae()][Metrics::ae()]
-#' - "se_point" = [se()][Metrics::se()]
-#' - "ape" = [ape()][Metrics::ape()]
+#' - "ae_point" = absolute error
+#' - "se_point" = squared error
+#' - "ape" = absolute percentage error
 #'
 #' A note of caution: Every scoring rule for a point forecast
 #' is implicitly minimised by a specific aspect of the predictive distribution
@@ -145,9 +144,9 @@ score.forecast_point <- function(forecast, metrics = get_metrics(forecast), ...)
 #' Journal of the American Statistical Association.
 get_metrics.forecast_point <- function(x, select = NULL, exclude = NULL, ...) {
   all <- list(
-    ae_point = Metrics::ae,
-    se_point = Metrics::se,
-    ape = Metrics::ape
+    ae_point = function(actual, predicted) abs(actual - predicted),
+    se_point = function(actual, predicted) (actual - predicted)^2,
+    ape = function(actual, predicted) abs(actual - predicted) / abs(actual)
   )
   select_metrics(all, select, exclude)
 }
diff --git a/man/get_metrics.forecast_point.Rd b/man/get_metrics.forecast_point.Rd
index 1d34200e5..77b4b0c25 100644
--- a/man/get_metrics.forecast_point.Rd
+++ b/man/get_metrics.forecast_point.Rd
@@ -21,9 +21,9 @@ If \code{select} is not \code{NULL}, this argument is ignored.}
 \description{
 For point forecasts, the default scoring rules are:
 \itemize{
-\item "ae_point" = \link[Metrics:ae]{ae()}
-\item "se_point" = \link[Metrics:se]{se()}
-\item "ape" = \link[Metrics:ape]{ape()}
+\item "ae_point" = absolute error
+\item "se_point" = squared error
+\item "ape" = absolute percentage error
 }
 
 A note of caution: Every scoring rule for a point forecast
diff --git a/tests/testthat/test-class-forecast-point.R b/tests/testthat/test-class-forecast-point.R
index fb2fd161d..e2a5ea6f4 100644
--- a/tests/testthat/test-class-forecast-point.R
+++ b/tests/testthat/test-class-forecast-point.R
@@ -107,3 +107,53 @@ test_that("get_metrics.forecast_point() works as expected", {
     c("ae_point", "se_point", "ape")
   )
 })
+
+test_that("internal ae replacement produces identical results", {
+  observed <- c(1, -15, 22, 0, 5.5)
+  predicted <- c(5, 6, 7, 0, 5.5)
+  ae_fn <- get_metrics(example_point, select = "ae_point")[[1]]
+  expect_identical(ae_fn(observed, predicted), abs(observed - predicted))
+  expect_identical(ae_fn(5, 5), 0)
+  expect_identical(ae_fn(-10, 5), 15)
+})
+
+test_that("internal se replacement produces identical results", {
+  observed <- c(1, -15, 22, 0, 5.5)
+  predicted <- c(5, 6, 7, 0, 5.5)
+  se_fn <- get_metrics(example_point, select = "se_point")[[1]]
+  expect_identical(se_fn(observed, predicted), (observed - predicted)^2)
+  expect_identical(se_fn(5, 5), 0)
+  expect_identical(se_fn(-10, 5), 225)
+})
+
+test_that("internal ape replacement produces identical results", {
+  observed <- c(1, -15, 22, 5.5, 100)
+  predicted <- c(5, 6, 7, 0, 100)
+  ape_fn <- get_metrics(example_point, select = "ape")[[1]]
+  expect_equal(ape_fn(observed, predicted), abs(observed - predicted) / abs(observed))
+  expect_identical(ape_fn(5, 5), 0)
+  expect_identical(ape_fn(0, 5), Inf)
+})
+
+test_that("Metrics package is not in DESCRIPTION Imports", {
+  desc_text <- readLines(system.file("DESCRIPTION", package = "scoringutils"))
+  imports_lines <- desc_text[grepl("^Imports:|^\\s+Metrics", desc_text)]
+  expect_false(any(grepl("\\bMetrics\\b", imports_lines)))
+})
+
+test_that("score() with point forecasts produces correct results after Metrics removal", {
+  scores <- score(example_point)
+  input <- na.omit(as.data.table(example_point))
+  expect_equal(scores$ae_point, abs(input$observed - input$predicted))
+  expect_equal(scores$se_point, (input$observed - input$predicted)^2)
+  expect_equal(scores$ape, abs(input$observed - input$predicted) / abs(input$observed))
+  expect_true(all(c("ae_point", "se_point", "ape") %in% colnames(scores)))
+})
+
+test_that("get_metrics.forecast_point() returns expected functions", {
+  metrics <- get_metrics(example_point)
+  expect_type(metrics, "list")
+  expect_named(metrics, c("ae_point", "se_point", "ape"))
+  expect_true(all(vapply(metrics, is.function, logical(1))))
+  expect_true(all(vapply(metrics, function(f) length(formals(f)) == 2, logical(1))))
+})
diff --git a/vignettes/scoring-rules.Rmd b/vignettes/scoring-rules.Rmd
index f07e32a2f..292782526 100644
--- a/vignettes/scoring-rules.Rmd
+++ b/vignettes/scoring-rules.Rmd
@@ -58,11 +58,11 @@ observed <- rnorm(n, 5, 4)^2
 predicted_mu <- mean(observed)
 predicted_not_mu <- predicted_mu - rnorm(n, 10, 2)
 
-mean(Metrics::ae(observed, predicted_mu))
-mean(Metrics::ae(observed, predicted_not_mu))
+mean(abs(observed - predicted_mu))
+mean(abs(observed - predicted_not_mu))
 
-mean(Metrics::se(observed, predicted_mu))
-mean(Metrics::se(observed, predicted_not_mu))
+mean((observed - predicted_mu)^2)
+mean((observed - predicted_not_mu)^2)
 ```
 
 
@@ -73,7 +73,7 @@ mean(Metrics::se(observed, predicted_not_mu))
 
 **Forecast**: $\hat{y}$, a real number, the median of the forecaster's predictive distribution.
 
-The absolute error is the absolute difference between the predicted and the observed values. See `?Metrics::ae`.
+The absolute error is the absolute difference between the predicted and the observed values.
 
 $$\text{ae} = |y - \hat{y}|$$
 
@@ -85,7 +85,7 @@ The absolute error is only an appropriate rule if $\hat{y}$ corresponds to the m
 
 **Forecast**: $\hat{y}$, a real number, the mean of the forecaster's predictive distribution.
 
-The squared error is the squared difference between the predicted and the observed values. See `?Metrics::se`.
+The squared error is the squared difference between the predicted and the observed values.
 
 $$\text{se} = (y - \hat{y})^2$$
 The squared error is only an appropriate rule if $\hat{y}$ corresponds to the mean of the forecaster's predictive distribution. Otherwise, results will be misleading (see @gneitingMakingEvaluatingPoint2011).
@@ -96,7 +96,7 @@ The squared error is only an appropriate rule if $\hat{y}$ corresponds to the me
 
 **Forecast**: $\hat{y}$, a real number
 
-The absolute percentage error is the absolute percent difference between the predicted and the observed values. See `?Metrics::ape`.
+The absolute percentage error is the absolute percent difference between the predicted and the observed values.
 
 $$\text{ape} = \frac{|y - \hat{y}|}{|y|}$$