diff --git a/README.Rmd b/README.Rmd
index fefbe08f..0b7108c6 100644
--- a/README.Rmd
+++ b/README.Rmd
@@ -43,7 +43,7 @@ stacks is generalized with respect to:
 * Cross-validation scheme: Any resampling algorithm implemented in [rsample](https://rsample.tidymodels.org/) or adjacent packages is fair game for resampling data for use in training a model stack.
 * Error metric: Any metric function implemented in [yardstick](https://yardstick.tidymodels.org/) or adjacent packages is fair game for evaluating model stacks and their members. That package provides some infrastructure for creating your own metric functions as well!
 
-stacks uses a regularized linear model to combine predictions from ensemble members, though this model type is only one of many possible learning algorithms that could be used to fit a stacked ensemble model. For implementations of additional ensemble learning algorithms, check out [h2o](http://docs.h2o.ai/h2o/latest-stable/h2o-r/docs/reference/h2o.stackedEnsemble.html) and [SuperLearner](https://CRAN.R-project.org/package=SuperLearner).
+stacks uses a regularized linear model to combine predictions from ensemble members, though this model type is only one of many possible learning algorithms that could be used to fit a stacked ensemble model. For implementations of additional ensemble learning algorithms, check out [h2o](https://docs.h2o.ai/h2o/latest-stable/h2o-r/docs/reference/h2o.stackedEnsemble.html) and [SuperLearner](https://CRAN.R-project.org/package=SuperLearner).
 
 Rather than diving right into the implementation, we'll focus here on how the pieces fit together, conceptually, in building an ensemble with `stacks`. See the `basics` vignette for an example of the API in action!
 
diff --git a/README.md b/README.md
index a0e467c3..ee41d509 100644
--- a/README.md
+++ b/README.md
@@ -69,7 +69,7 @@ ensemble members, though this model type is only one of many possible
 learning algorithms that could be used to fit a stacked ensemble model.
 For implementations of additional ensemble learning algorithms, check
 out
-[h2o](http://docs.h2o.ai/h2o/latest-stable/h2o-r/docs/reference/h2o.stackedEnsemble.html)
+[h2o](https://docs.h2o.ai/h2o/latest-stable/h2o-r/docs/reference/h2o.stackedEnsemble.html)
 and [SuperLearner](https://CRAN.R-project.org/package=SuperLearner).
 
 Rather than diving right into the implementation, we’ll focus here on