typo

vignettes/alternative_software.qmd

@@ -775,7 +775,7 @@ The [`fmeffects` package](https://cran.r-project.org/package=fmeffects) is descr
 
 As the name says, this package is focused on "forward marginal effects" in the context of machine learning models estimated using the `mlr3` or `tidymodels` frameworks. Since version 0.16.0, `marginaleffects` also supports these machine learning frameworks, and it covers a superset of the `fmeffects` functionality. Consider a random forest model trained on the `bikes` data:
 
-```{r, message = FALSE, warning = FALSE}
+```{r, message = FALSE, warning = FALSE, eval = FALSE}
 library("mlr3verse")
 library("fmeffects")
 data("bikes", package = "fmeffects")
@@ -785,13 +785,13 @@ forest <- lrn("regr.ranger")$train(task)
 
 Now, we use the `avg_comparisons()` function to compute *forward* marginal effects:
 
-```{r}
+```{r, eval = FALSE}
 avg_comparisons(forest, variables = list(temp = 1), newdata = bikes)
 ```
 
 This is equivalent to the key quantity reported by the `fmeffects` package:
 
-```{r}
+```{r, eval = FALSE}
 fmeffects::fme(
     model = forest,
     data = bikes,
@@ -804,7 +804,7 @@ Another interesting feature of `fmeffects` is the ability treat categorical pred
 
 In the `bikes` example, we can answer the question: how does the expected number of bike rentals increases, on average, if all days were misty? With `marginaleffects`, we can use a function in the `variables` argument to specify a custom contrast:
 
-```{r}
+```{r, eval = FALSE}
 FUN <- function(x) data.frame(lo = x, hi = "misty")
 
 avg_comparisons(