diff --git a/.github/workflows/R-CMD-check.yaml b/.github/workflows/R-CMD-check.yaml
index 0cab5a68..6d14af22 100644
--- a/.github/workflows/R-CMD-check.yaml
+++ b/.github/workflows/R-CMD-check.yaml
@@ -43,11 +43,13 @@ jobs:
         with:
           extra-packages: |
             any::rcmdcheck
+            any::BH
+            any::RcppEigen
           needs: check
 
       - uses: r-lib/actions/check-r-package@v2
         with:
           upload-snapshots: true
-          args: c("--ignore-vignettes", "--no-manual", "--as-cran")
-          build_args: c("--no-build-vignettes")
+          # args: c("--ignore-vignettes", "--no-manual", "--as-cran")
+          # build_args: c("--no-build-vignettes")
 
diff --git a/R/helpers.R b/R/helpers.R
index ef3bdead..e48b9e1c 100644
--- a/R/helpers.R
+++ b/R/helpers.R
@@ -189,7 +189,8 @@ release_questions <- function() {
   c(
     "Have you updated references?",
     "Have you updated inst/CITATION?",
-    "Have you updated the vignettes?"
+    "Have you updated the vignettes?",
+    "Have you tested building the vignettes on GitHub Actions?"
   )
 }
 # nocov end
diff --git a/vignettes/loo2-lfo.Rmd b/vignettes/loo2-lfo.Rmd
index 905558e7..23ba7459 100644
--- a/vignettes/loo2-lfo.Rmd
+++ b/vignettes/loo2-lfo.Rmd
@@ -281,7 +281,7 @@ preds <- cbind(
 )
 
 ggplot(cbind(df, preds), aes(x = year, y = Estimate)) +
-  geom_smooth(aes(ymin = Q5, ymax = Q95), stat = "identity", size = 0.5) +
+  geom_smooth(aes(ymin = Q5, ymax = Q95), stat = "identity", linewidth = 0.5) +
   geom_point(aes(y = y)) + 
   labs(
     y = "Water Level (ft)", 
@@ -321,7 +321,7 @@ The initial step for the exact computation is to calculate the log-predictive
 densities by refitting the model many times:
 
 ```{r exact_loglik, results="hide"}
-loglik_exact <- matrix(nrow = nsamples(fit), ncol = N)
+loglik_exact <- matrix(nrow = ndraws(fit), ncol = N)
 for (i in L:(N - 1)) {
   past <- 1:i
   oos <- i + 1
@@ -514,7 +514,7 @@ predict.
 
 ```{r exact_loglikm, results="hide"}
 M <- 4
-loglikm <- matrix(nrow = nsamples(fit), ncol = N)
+loglikm <- matrix(nrow = ndraws(fit), ncol = N)
 for (i in L:(N - M)) {
   past <- 1:i
   oos <- (i + 1):(i + M)
diff --git a/vignettes/loo2-non-factorized.Rmd b/vignettes/loo2-non-factorized.Rmd
index a48b0256..fb79ea03 100644
--- a/vignettes/loo2-non-factorized.Rmd
+++ b/vignettes/loo2-non-factorized.Rmd
@@ -569,7 +569,7 @@ is strong. In fact, summing over the pointwise ELPD values and leaving out the
 fourth observation yields practically equivalent results for approximate and
 exact LOO-CV:
 
-````{r pt4}
+```{r pt4}
 without_pt_4 <- c(
   approx = sum(psis_loo$pointwise[-4, "elpd_loo"]),
   exact = sum(exact_elpds[-4])