diff --git a/DESCRIPTION b/DESCRIPTION
index c9bfad9..5b1804a 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -48,7 +48,6 @@ Suggests:
     nnet,
     ranger,
     rmarkdown,
-    SuperLearner,
     testthat (>= 3.0.0),
     workflowsets (>= 0.1.0)
 VignetteBuilder: 
diff --git a/R/blend_predictions.R b/R/blend_predictions.R
index d82a07d..759f767 100644
--- a/R/blend_predictions.R
+++ b/R/blend_predictions.R
@@ -21,7 +21,7 @@
 #' Note that a regularized linear model is one of many possible
 #' learning algorithms that could be used to fit a stacked ensemble
 #' model. For implementations of additional ensemble learning algorithms, see
-#' [h2o::h2o.stackedEnsemble()] and [SuperLearner::SuperLearner()].
+#' [h2o::h2o.stackedEnsemble()] and `SuperLearner::SuperLearner()`.
 #' 
 #' @param data_stack A `data_stack` object
 #' @param penalty A numeric vector of proposed values for total amount of
diff --git a/man/blend_predictions.Rd b/man/blend_predictions.Rd
index edc3c05..f9cc087 100644
--- a/man/blend_predictions.Rd
+++ b/man/blend_predictions.Rd
@@ -77,7 +77,7 @@ is typically used after a number of calls to \code{\link[=add_candidates]{add_ca
 Note that a regularized linear model is one of many possible
 learning algorithms that could be used to fit a stacked ensemble
 model. For implementations of additional ensemble learning algorithms, see
-\code{\link[h2o:h2o.stackedEnsemble]{h2o::h2o.stackedEnsemble()}} and \code{\link[SuperLearner:SuperLearner]{SuperLearner::SuperLearner()}}.
+\code{\link[h2o:h2o.stackedEnsemble]{h2o::h2o.stackedEnsemble()}} and \code{SuperLearner::SuperLearner()}.
 }
 \section{Example Data}{