diff --git a/.github/workflows/R-CMD-check-hard.yaml b/.github/workflows/R-CMD-check-hard.yaml
new file mode 100644
index 00000000..ac3bc0fd
--- /dev/null
+++ b/.github/workflows/R-CMD-check-hard.yaml
@@ -0,0 +1,59 @@
+# Workflow derived from https://github.com/r-lib/actions/tree/v2/examples
+# Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help
+#
+# NOTE: This workflow only directly installs "hard" dependencies, i.e. Depends,
+# Imports, and LinkingTo dependencies. Notably, Suggests dependencies are never
+# installed, with the exception of testthat, knitr, and rmarkdown. The cache is
+# never used to avoid accidentally restoring a cache containing a suggested
+# dependency.
+on:
+  push:
+    branches: [main, master]
+  pull_request:
+
+name: R-CMD-check-hard.yaml
+
+permissions: read-all
+
+jobs:
+  check-no-suggests:
+    runs-on: ${{ matrix.config.os }}
+
+    name: ${{ matrix.config.os }} (${{ matrix.config.r }})
+
+    strategy:
+      fail-fast: false
+      matrix:
+        config:
+          - {os: ubuntu-latest,   r: 'release'}
+
+    env:
+      GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
+      R_KEEP_PKG_SOURCE: yes
+
+    steps:
+      - uses: actions/checkout@v4
+
+      - uses: r-lib/actions/setup-pandoc@v2
+
+      - uses: r-lib/actions/setup-r@v2
+        with:
+          r-version: ${{ matrix.config.r }}
+          http-user-agent: ${{ matrix.config.http-user-agent }}
+          use-public-rspm: true
+
+      - uses: r-lib/actions/setup-r-dependencies@v2
+        with:
+          dependencies: '"hard"'
+          cache: false
+          extra-packages: |
+            any::rcmdcheck
+            any::testthat
+            any::knitr
+            any::rmarkdown
+          needs: check
+
+      - uses: r-lib/actions/check-r-package@v2
+        with:
+          upload-snapshots: true
+          build_args: 'c("--no-manual","--compact-vignettes=gs+qpdf")'
diff --git a/R/cal-estimate-beta.R b/R/cal-estimate-beta.R
index b3a524cd..13a576aa 100644
--- a/R/cal-estimate-beta.R
+++ b/R/cal-estimate-beta.R
@@ -15,9 +15,11 @@
 #' \url{https://www.tidymodels.org/learn/models/calibration/},
 #' [cal_validate_beta()]
 #' @examples
-#' # It will automatically identify the probability columns
-#' # if passed a model fitted with tidymodels
-#' cal_estimate_beta(segment_logistic, Class)
+#' if (rlang::is_installed("betacal")) {
+#'  # It will automatically identify the probability columns
+#'   # if passed a model fitted with tidymodels
+#'   cal_estimate_beta(segment_logistic, Class)
+#' }
 #' @export
 cal_estimate_beta <- function(.data,
                               truth = NULL,
diff --git a/R/cal-validate.R b/R/cal-validate.R
index 5b7f49ee..a9f43f9e 100644
--- a/R/cal-validate.R
+++ b/R/cal-validate.R
@@ -297,10 +297,11 @@ cal_validate_isotonic_boot.tune_results <- function(.data,
 #'
 #' library(dplyr)
 #'
-#' segment_logistic %>%
-#'   rsample::vfold_cv() %>%
-#'   cal_validate_beta(Class)
-#'
+#' if (rlang::is_installed("betacal")) {
+#'   segment_logistic %>%
+#'     rsample::vfold_cv() %>%
+#'     cal_validate_beta(Class)
+#' }
 #' @export
 cal_validate_beta <- function(.data,
                               truth = NULL,
diff --git a/R/conformal_infer_cv.R b/R/conformal_infer_cv.R
index 31910f66..b6ed4d42 100644
--- a/R/conformal_infer_cv.R
+++ b/R/conformal_infer_cv.R
@@ -33,7 +33,7 @@
 #' Rina Foygel Barber, Emmanuel J. Candès, Aaditya Ramdas, Ryan J. Tibshirani
 #' "Predictive inference with the jackknife+," _The Annals of Statistics_,
 #' 49(1), 486-507, 2021
-#' @examplesIf !probably:::is_cran_check()
+#' @examplesIf !probably:::is_cran_check() & rlang::is_installed(c("modeldata", "parsnip"))
 #' library(workflows)
 #' library(dplyr)
 #' library(parsnip)
diff --git a/R/conformal_infer_quantile.R b/R/conformal_infer_quantile.R
index 288ff8e5..a933d2a0 100644
--- a/R/conformal_infer_quantile.R
+++ b/R/conformal_infer_quantile.R
@@ -31,7 +31,7 @@
 #' @references
 #' Romano, Yaniv, Evan Patterson, and Emmanuel Candes. "Conformalized quantile
 #' regression." _Advances in neural information processing systems_ 32 (2019).
-#' @examplesIf !probably:::is_cran_check()
+#' @examplesIf !probably:::is_cran_check() & rlang::is_installed(c("modeldata", "parsnip", "quantregForest"))
 #' library(workflows)
 #' library(dplyr)
 #' library(parsnip)
diff --git a/R/conformal_infer_split.R b/R/conformal_infer_split.R
index b92e6507..1b9d36ac 100644
--- a/R/conformal_infer_split.R
+++ b/R/conformal_infer_split.R
@@ -28,7 +28,7 @@
 #' @references
 #' Lei, Jing, et al. "Distribution-free predictive inference for regression."
 #' _Journal of the American Statistical Association_ 113.523 (2018): 1094-1111.
-#' @examplesIf !probably:::is_cran_check()
+#' @examplesIf !probably:::is_cran_check() & rlang::is_installed(c("modeldata", "parsnip", "nnet"))
 #' library(workflows)
 #' library(dplyr)
 #' library(parsnip)
diff --git a/man/cal_estimate_beta.Rd b/man/cal_estimate_beta.Rd
index b9c032c5..612f533f 100644
--- a/man/cal_estimate_beta.Rd
+++ b/man/cal_estimate_beta.Rd
@@ -95,9 +95,11 @@ one. This final step might compromise the calibration.
 }
 
 \examples{
-# It will automatically identify the probability columns
-# if passed a model fitted with tidymodels
-cal_estimate_beta(segment_logistic, Class)
+if (rlang::is_installed("betacal")) {
+ # It will automatically identify the probability columns
+  # if passed a model fitted with tidymodels
+  cal_estimate_beta(segment_logistic, Class)
+}
 }
 \references{
 Meelis Kull, Telmo M. Silva Filho, Peter Flach "Beyond sigmoids:
diff --git a/man/cal_validate_beta.Rd b/man/cal_validate_beta.Rd
index f0013c3e..87b25e30 100644
--- a/man/cal_validate_beta.Rd
+++ b/man/cal_validate_beta.Rd
@@ -104,10 +104,11 @@ average of the metrics before, and after the calibration.
 
 library(dplyr)
 
-segment_logistic \%>\%
-  rsample::vfold_cv() \%>\%
-  cal_validate_beta(Class)
-
+if (rlang::is_installed("betacal")) {
+  segment_logistic \%>\%
+    rsample::vfold_cv() \%>\%
+    cal_validate_beta(Class)
+}
 }
 \seealso{
 \url{https://www.tidymodels.org/learn/models/calibration/},
diff --git a/man/int_conformal_cv.Rd b/man/int_conformal_cv.Rd
index 7309cbd5..dc3e66f2 100644
--- a/man/int_conformal_cv.Rd
+++ b/man/int_conformal_cv.Rd
@@ -51,7 +51,7 @@ stop the computations for other types of resamples, but we have no way of
 knowing whether the results are appropriate.
 }
 \examples{
-\dontshow{if (!probably:::is_cran_check()) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf}
+\dontshow{if (!probably:::is_cran_check() & rlang::is_installed(c("modeldata", "parsnip"))) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf}
 library(workflows)
 library(dplyr)
 library(parsnip)
diff --git a/man/int_conformal_quantile.Rd b/man/int_conformal_quantile.Rd
index cfc89070..4486985c 100644
--- a/man/int_conformal_quantile.Rd
+++ b/man/int_conformal_quantile.Rd
@@ -46,7 +46,7 @@ Note that the because of the method used to construct the interval, it is
 possible that the prediction intervals will not include the predicted value.
 }
 \examples{
-\dontshow{if (!probably:::is_cran_check()) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf}
+\dontshow{if (!probably:::is_cran_check() & rlang::is_installed(c("modeldata", "parsnip", "quantregForest"))) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf}
 library(workflows)
 library(dplyr)
 library(parsnip)
diff --git a/man/int_conformal_split.Rd b/man/int_conformal_split.Rd
index 262c3e9b..7ca75637 100644
--- a/man/int_conformal_split.Rd
+++ b/man/int_conformal_split.Rd
@@ -45,7 +45,7 @@ quantile (e.g., the 95th for 95\% interval) and should not include rows that
 were in the original training set.
 }
 \examples{
-\dontshow{if (!probably:::is_cran_check()) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf}
+\dontshow{if (!probably:::is_cran_check() & rlang::is_installed(c("modeldata", "parsnip", "nnet"))) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf}
 library(workflows)
 library(dplyr)
 library(parsnip)
diff --git a/tests/testthat/_snaps/cal-estimate.md b/tests/testthat/_snaps/cal-estimate.md
index bdb7f406..a0eef226 100644
--- a/tests/testthat/_snaps/cal-estimate.md
+++ b/tests/testthat/_snaps/cal-estimate.md
@@ -213,7 +213,7 @@
       Type: Regression
       Source class: Data Frame
       Data points: 2,000
-      Unique Predicted Values: 43
+      Unique Predicted Values: 40
       Truth variable: `outcome`
       Estimate variables:
       `.pred` ==> predictions
@@ -229,7 +229,7 @@
       Type: Regression
       Source class: Data Frame
       Data points: 2,000, split in 10 groups
-      Unique Predicted Values: 11
+      Unique Predicted Values: 16
       Truth variable: `outcome`
       Estimate variables:
       `.pred` ==> predictions
@@ -373,7 +373,7 @@
 ---
 
     Code
-      print(mtnl_isotonic)
+      print(mtnl_beta)
     Message
       
       -- Probability Calibration 
diff --git a/tests/testthat/test-cal-apply.R b/tests/testthat/test-cal-apply.R
index ae071cd4..36cbf206 100644
--- a/tests/testthat/test-cal-apply.R
+++ b/tests/testthat/test-cal-apply.R
@@ -8,6 +8,8 @@ test_that("Logistic apply works - data.frame", {
 })
 
 test_that("Logistic apply works - tune_results", {
+  skip_if_not_installed("modeldata")
+
   tct <- testthat_cal_binary()
   tl_logistic <- cal_estimate_logistic(tct, smooth = FALSE)
   tap_logistic <- cal_apply(tct, tl_logistic)
@@ -27,6 +29,8 @@ test_that("Logistic spline apply works", {
 })
 
 test_that("Logistic spline apply works - tune_results", {
+  skip_if_not_installed("modeldata")
+
   tct <- testthat_cal_binary()
   tl_gam <- cal_estimate_logistic(tct)
   tap_gam <- cal_apply(tct, tl_gam)
@@ -90,6 +94,8 @@ test_that("Isotonic apply works - data.frame", {
 })
 
 test_that("Isotonic apply works - tune_results", {
+  skip_if_not_installed("modeldata")
+
   tct <- testthat_cal_binary()
   tl_isotonic <- cal_estimate_isotonic(tct)
   tap_isotonic <- cal_apply(tct, tl_isotonic)
@@ -107,6 +113,8 @@ test_that("Isotonic Bootstrapped apply works - data.frame", {
 })
 
 test_that("Isotonic Bootstrapped apply works - tune_results", {
+  skip_if_not_installed("modeldata")
+
   tct <- testthat_cal_binary()
   tl_boot <- cal_estimate_isotonic_boot(tct)
   tap_boot <- cal_apply(tct, tl_boot)
@@ -119,6 +127,7 @@ test_that("Isotonic Bootstrapped apply works - tune_results", {
 # ------------------------------------------------------------------------------
 
 test_that("Beta apply works - data.frame", {
+  skip_if_not_installed("betacal")
   sl_beta <- cal_estimate_beta(segment_logistic, Class)
   ap_beta <- cal_apply(segment_logistic, sl_beta)
 
@@ -128,6 +137,9 @@ test_that("Beta apply works - data.frame", {
 })
 
 test_that("Beta apply works - tune_results", {
+  skip_if_not_installed("betacal")
+  skip_if_not_installed("modeldata")
+
   tct <- testthat_cal_binary()
   tl_beta <- cal_estimate_beta(tct)
   tap_beta <- cal_apply(tct, tl_beta)
@@ -148,12 +160,16 @@ test_that("Passing the data frame first returns expected abort message", {
 })
 
 test_that("Passing a tune_results without saved predictions causes error", {
+  skip_if_not_installed("betacal")
+  skip_if_not_installed("modeldata")
+
   tct <- testthat_cal_binary()
   tl_beta <- cal_estimate_beta(tct)
   expect_error(cal_apply(tune::ames_grid_search, tl_beta))
 })
 
 test_that("Passing a calibration object as the first arg fails", {
+  skip_if_not_installed("betacal")
   sl_beta <- cal_estimate_beta(segment_logistic, Class)
   expect_error(cal_apply(sl_beta, segment_logistic))
 })
diff --git a/tests/testthat/test-cal-estimate.R b/tests/testthat/test-cal-estimate.R
index f34f7b17..d1144d6c 100644
--- a/tests/testthat/test-cal-estimate.R
+++ b/tests/testthat/test-cal-estimate.R
@@ -1,5 +1,7 @@
 # --------------------------------- Logistic -----------------------------------
 test_that("Logistic estimates work - data.frame", {
+  skip_if_not_installed("modeldata")
+
   sl_logistic <- cal_estimate_logistic(segment_logistic, Class, smooth = FALSE)
   expect_cal_type(sl_logistic, "binary")
   expect_cal_method(sl_logistic, "Logistic regression")
@@ -51,6 +53,8 @@ test_that("Logistic estimates work - data.frame", {
 })
 
 test_that("Logistic estimates work - tune_results", {
+  skip_if_not_installed("modeldata")
+
   tl_logistic <- cal_estimate_logistic(testthat_cal_binary(), smooth = FALSE)
   expect_cal_type(tl_logistic, "binary")
   expect_cal_method(tl_logistic, "Logistic regression")
@@ -101,6 +105,8 @@ test_that("Logistic spline estimates work - data.frame", {
 })
 
 test_that("Logistic spline estimates work - tune_results", {
+  skip_if_not_installed("modeldata")
+
   tl_gam <- cal_estimate_logistic(testthat_cal_binary())
   expect_cal_type(tl_gam, "binary")
   expect_cal_method(tl_gam, "Generalized additive model")
@@ -116,6 +122,8 @@ test_that("Logistic spline estimates work - tune_results", {
 
 # --------------------------------- Isotonic -----------------------------------
 test_that("Isotonic estimates work - data.frame", {
+  skip_if_not_installed("modeldata")
+
   set.seed(100)
   sl_isotonic <- cal_estimate_isotonic(segment_logistic, Class)
   expect_cal_type(sl_isotonic, "binary")
@@ -154,6 +162,8 @@ test_that("Isotonic estimates work - data.frame", {
 })
 
 test_that("Isotonic estimates work - tune_results", {
+  skip_if_not_installed("modeldata")
+
   set.seed(100)
   tl_isotonic <- cal_estimate_isotonic(testthat_cal_binary())
   expect_cal_type(tl_isotonic, "binary")
@@ -189,12 +199,16 @@ test_that("Isotonic estimates errors - grouped_df", {
 })
 
 test_that("Isotonic linear estimates work - data.frame", {
+  skip_if_not_installed("modeldata")
+
+  set.seed(2983)
   sl_logistic <- cal_estimate_isotonic(boosting_predictions_oob, outcome, estimate = .pred)
   expect_cal_type(sl_logistic, "regression")
   expect_cal_method(sl_logistic, "Isotonic regression")
   expect_cal_rows(sl_logistic, 2000)
   expect_snapshot(print(sl_logistic))
 
+  set.seed(38)
   sl_logistic_group <- boosting_predictions_oob %>%
     cal_estimate_isotonic(outcome, estimate = .pred, .by = id)
 
@@ -217,6 +231,8 @@ test_that("Isotonic linear estimates work - data.frame", {
 
 # -------------------------- Isotonic Bootstrapped -----------------------------
 test_that("Isotonic Bootstrapped estimates work - data.frame", {
+  skip_if_not_installed("modeldata")
+
   set.seed(1)
   sl_boot <- cal_estimate_isotonic_boot(segment_logistic, Class)
   expect_cal_type(sl_boot, "binary")
@@ -250,6 +266,8 @@ test_that("Isotonic Bootstrapped estimates work - data.frame", {
 })
 
 test_that("Isotonic Bootstrapped estimates work - tune_results", {
+  skip_if_not_installed("modeldata")
+
   set.seed(100)
   tl_isotonic <- cal_estimate_isotonic_boot(testthat_cal_binary())
   expect_cal_type(tl_isotonic, "binary")
@@ -286,6 +304,7 @@ test_that("Isotonic Bootstrapped estimates errors - grouped_df", {
 
 # ----------------------------------- Beta -------------------------------------
 test_that("Beta estimates work - data.frame", {
+  skip_if_not_installed("betacal")
   sl_beta <- cal_estimate_beta(segment_logistic, Class, smooth = FALSE)
   expect_cal_type(sl_beta, "binary")
   expect_cal_method(sl_beta, "Beta calibration")
@@ -319,6 +338,9 @@ test_that("Beta estimates work - data.frame", {
 })
 
 test_that("Beta estimates work - tune_results", {
+  skip_if_not_installed("betacal")
+  skip_if_not_installed("modeldata")
+
   tl_beta <- cal_estimate_beta(testthat_cal_binary())
   expect_cal_type(tl_beta, "binary")
   expect_cal_method(tl_beta, "Beta calibration")
@@ -335,20 +357,21 @@ test_that("Beta estimates work - tune_results", {
 
   set.seed(100)
   suppressWarnings(
-    mtnl_isotonic <- cal_estimate_beta(testthat_cal_multiclass())
+    mtnl_beta <- cal_estimate_beta(testthat_cal_multiclass())
   )
-  expect_cal_type(mtnl_isotonic, "one_vs_all")
-  expect_cal_method(mtnl_isotonic, "Beta calibration")
-  expect_snapshot(print(mtnl_isotonic))
-  expect_true(are_groups_configs(mtnl_isotonic))
+  expect_cal_type(mtnl_beta, "one_vs_all")
+  expect_cal_method(mtnl_beta, "Beta calibration")
+  expect_snapshot(print(mtnl_beta))
+  expect_true(are_groups_configs(mtnl_beta))
 
   expect_equal(
     testthat_cal_multiclass_count(),
-    nrow(cal_apply(testthat_cal_multiclass(), mtnl_isotonic))
+    nrow(cal_apply(testthat_cal_multiclass(), mtnl_beta))
   )
 })
 
 test_that("Beta estimates errors - grouped_df", {
+  skip_if_not_installed("betacal")
   expect_snapshot_error(
     cal_estimate_beta(dplyr::group_by(mtcars, vs))
   )
@@ -356,6 +379,9 @@ test_that("Beta estimates errors - grouped_df", {
 
 # ------------------------------ Multinomial -----------------------------------
 test_that("Multinomial estimates work - data.frame", {
+  skip_if_not_installed("modeldata")
+  skip_if_not_installed("nnet")
+
   sp_multi <- cal_estimate_multinomial(species_probs, Species, smooth = FALSE)
   expect_cal_type(sp_multi, "multiclass")
   expect_cal_method(sp_multi, "Multinomial regression")
@@ -390,6 +416,9 @@ test_that("Multinomial estimates work - data.frame", {
 })
 
 test_that("Multinomial estimates work - tune_results", {
+  skip_if_not_installed("modeldata")
+  skip_if_not_installed("nnet")
+
   tl_multi <- cal_estimate_multinomial(testthat_cal_multiclass(), smooth = FALSE)
   expect_cal_type(tl_multi, "multiclass")
   expect_cal_method(tl_multi, "Multinomial regression")
@@ -421,6 +450,9 @@ test_that("Multinomial estimates work - tune_results", {
 })
 
 test_that("Multinomial estimates errors - grouped_df", {
+  skip_if_not_installed("modeldata")
+  skip_if_not_installed("nnet")
+
   expect_snapshot_error(
     cal_estimate_multinomial(dplyr::group_by(mtcars, vs))
   )
@@ -434,6 +466,8 @@ test_that("Passing a binary outcome causes error", {
 
 # --------------------------------- Linear -----------------------------------
 test_that("Linear estimates work - data.frame", {
+  skip_if_not_installed("modeldata")
+
   sl_logistic <- cal_estimate_linear(boosting_predictions_oob, outcome, smooth = FALSE)
   expect_cal_type(sl_logistic, "regression")
   expect_cal_method(sl_logistic, "Linear")
@@ -481,6 +515,8 @@ test_that("Linear estimates errors - grouped_df", {
 
 # ----------------------------- Linear Spline --------------------------------
 test_that("Linear spline estimates work - data.frame", {
+  skip_if_not_installed("modeldata")
+
   sl_gam <- cal_estimate_linear(boosting_predictions_oob, outcome)
   expect_cal_type(sl_gam, "regression")
   expect_cal_method(sl_gam, "Generalized additive model")
@@ -527,6 +563,8 @@ test_that("Linear spline estimates work - tune_results", {
 
 # ----------------------------------- Other ------------------------------------
 test_that("Non-default names used for estimate columns", {
+  skip_if_not_installed("modeldata")
+
   new_segment <- segment_logistic
   colnames(new_segment) <- c("poor", "good", "Class")
 
diff --git a/tests/testthat/test-cal-plot.R b/tests/testthat/test-cal-plot.R
index 185bc935..d4b8ab4e 100644
--- a/tests/testthat/test-cal-plot.R
+++ b/tests/testthat/test-cal-plot.R
@@ -72,6 +72,8 @@ test_that("Binary breaks functions work with group argument", {
 })
 
 test_that("Multi-class breaks functions work", {
+  skip_if_not_installed("modeldata")
+
   x10 <- .cal_table_breaks(species_probs, Species, dplyr::starts_with(".pred"))
 
   expect_equal(
@@ -123,6 +125,8 @@ test_that("breaks plot function errors - grouped_df", {
 })
 
 test_that("Binary logistic functions work", {
+  skip_if_not_installed("modeldata")
+
   x20 <- .cal_table_logistic(segment_logistic, Class, .pred_good)
 
   model20 <- mgcv::gam(Class ~ s(.pred_good, k = 10),
@@ -275,6 +279,8 @@ test_that("logistic plot function errors - grouped_df", {
 })
 
 test_that("Binary windowed functions work", {
+  skip_if_not_installed("modeldata")
+
   x30 <- .cal_table_windowed(
     segment_logistic,
     truth = Class,
diff --git a/tests/testthat/test-cal-validate.R b/tests/testthat/test-cal-validate.R
index 8c8382bb..9d82cd8c 100644
--- a/tests/testthat/test-cal-validate.R
+++ b/tests/testthat/test-cal-validate.R
@@ -1,4 +1,6 @@
 test_that("Logistic validation with data frame input", {
+  skip_if_not_installed("rsample")
+
   df <- testthat_cal_sampled()
   val_obj <- cal_validate_logistic(df, Class)
   val_with_pred <- cal_validate_logistic(df, Class, save_pred = TRUE, smooth = TRUE)
@@ -59,6 +61,9 @@ test_that("Logistic validation with data frame input", {
 
 
 test_that("Beta validation with data frame input", {
+  skip_if_not_installed("betacal")
+  skip_if_not_installed("rsample")
+
   df <- testthat_cal_sampled()
   val_obj <- cal_validate_beta(df, Class)
   val_with_pred <- cal_validate_beta(df, Class, save_pred = TRUE)
@@ -87,6 +92,8 @@ test_that("Beta validation with data frame input", {
 
 
 test_that("Isotonic validation classification with data frame input", {
+  skip_if_not_installed("rsample")
+
   df <- testthat_cal_sampled()
   val_obj <- cal_validate_isotonic(df, Class)
   val_with_pred <- cal_validate_isotonic(df, Class, save_pred = TRUE)
@@ -114,6 +121,8 @@ test_that("Isotonic validation classification with data frame input", {
 })
 
 test_that("Bootstrapped Isotonic classification validation with data frame input", {
+  skip_if_not_installed("rsample")
+
   df <- testthat_cal_sampled()
   val_obj <- cal_validate_isotonic_boot(df, Class)
   val_with_pred <- cal_validate_isotonic_boot(df, Class, save_pred = TRUE)
@@ -141,6 +150,9 @@ test_that("Bootstrapped Isotonic classification validation with data frame input
 })
 
 test_that("Multinomial classification validation with data frame input", {
+  skip_if_not_installed("modeldata")
+  skip_if_not_installed("nnet")
+
   df <- rsample::vfold_cv(testthat_cal_sim_multi())
   val_obj <- cal_validate_multinomial(df, class)
   val_with_pred <- cal_validate_multinomial(df, class, save_pred = TRUE, smooth = TRUE)
@@ -350,6 +362,7 @@ test_that("Bootstrapped isotonic classification validation with `fit_resamples`"
 })
 
 test_that("Beta calibration validation with `fit_resamples`", {
+  skip_if_not_installed("betacal")
   res <- testthat_cal_fit_rs()
   val_obj <- cal_validate_beta(res$binary)
   val_with_pred <- cal_validate_beta(res$binary, save_pred = TRUE)
@@ -382,6 +395,9 @@ test_that("Beta calibration validation with `fit_resamples`", {
 })
 
 test_that("Multinomial calibration validation with `fit_resamples`", {
+  skip_if_not_installed("modeldata")
+  skip_if_not_installed("nnet")
+
   res <- testthat_cal_fit_rs()
   val_obj <- cal_validate_multinomial(res$multin)
   val_with_pred <- cal_validate_multinomial(res$multin, save_pred = TRUE, smooth = TRUE)
@@ -564,6 +580,10 @@ test_that("Isotonic bootstrapped regression validation with `fit_resamples`", {
 
 
 test_that("validation functions error with tune_results input", {
+  skip_if_not_installed("modeldata")
+  skip_if_not_installed("nnet")
+  skip_if_not_installed("beta")
+
   expect_snapshot_error(
     cal_validate_beta(testthat_cal_binary())
   )
diff --git a/tests/testthat/test-conformal-intervals-quantile.R b/tests/testthat/test-conformal-intervals-quantile.R
index f14b081b..f866a5f8 100644
--- a/tests/testthat/test-conformal-intervals-quantile.R
+++ b/tests/testthat/test-conformal-intervals-quantile.R
@@ -1,6 +1,7 @@
 test_that("split conformal quantile intervals", {
   skip_if_not_installed("modeldata")
   skip_if_not_installed("nnet")
+  skip_if_not_installed("quantregForest")
 
   # ----------------------------------------------------------------------------
 
diff --git a/vignettes/where-to-use.Rmd b/vignettes/where-to-use.Rmd
index fbba0020..5d978c7d 100644
--- a/vignettes/where-to-use.Rmd
+++ b/vignettes/where-to-use.Rmd
@@ -12,7 +12,17 @@ editor_options:
 ---
 
 ```{r setup, include = FALSE}
+# For "no suggests" CRAN check
+if (rlang::is_installed(c("modeldata", "rsample"))) {
+  run <- TRUE
+} else {
+  run <- FALSE
+  # For in-line code
+  correct_bad <- max_j_index_threshold <- NA
+}
+
 knitr::opts_chunk$set(
+  eval = run,
   collapse = TRUE,
   comment = "#>",
   fig.width = 7,