diff --git a/DESCRIPTION b/DESCRIPTION
index d8dff46a6..0612d8264 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,7 +1,7 @@
 Type: Package
 Package: performance
 Title: Assessment of Regression Models Performance
-Version: 0.10.7.1
+Version: 0.10.7.2
 Authors@R: 
     c(person(given = "Daniel",
              family = "Lüdecke",
@@ -153,3 +153,4 @@ Config/Needs/website:
     r-lib/pkgdown,
     easystats/easystatstemplate
 Config/rcmdcheck/ignore-inconsequential-notes: true
+Remotes: easystats/insight
diff --git a/NEWS.md b/NEWS.md
index 2bb37d162..6209f55b8 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,3 +1,16 @@
+# performance 0.10.8
+
+## Changes
+
+* Changed behaviour of `check_predictions()` for models from binomial family,
+  to get comparable plots for different ways of outcome specification. Now,
+  if the outcome is a proportion, or defined as matrix of trials and successes,
+  the produced plots are the same (because the models should be the same, too).
+
+## Bug fixes
+
+* Fixed CRAN check errors.
+
 # performance 0.10.7
 
 ## Breaking changes
diff --git a/R/binned_residuals.R b/R/binned_residuals.R
index 8b5533bb0..370f33c20 100644
--- a/R/binned_residuals.R
+++ b/R/binned_residuals.R
@@ -29,6 +29,7 @@
 #'   time-consuming. By default, `show_dots = NULL`. In this case `binned_residuals()`
 #'   tries to guess whether performance will be poor due to a very large model
 #'   and thus automatically shows or hides dots.
+#' @param verbose Toggle warnings and messages.
 #' @param ... Currently not used.
 #'
 #' @return A data frame representing the data that is mapped in the accompanying
@@ -83,11 +84,20 @@ binned_residuals <- function(model,
                              ci_type = c("exact", "gaussian", "boot"),
                              residuals = c("deviance", "pearson", "response"),
                              iterations = 1000,
+                             verbose = TRUE,
                              ...) {
   # match arguments
   ci_type <- match.arg(ci_type)
   residuals <- match.arg(residuals)
 
+  # for non-bernoulli models, `"exact"` doesn't work
+  if (isFALSE(insight::model_info(model)$is_bernoulli)) {
+    ci_type <- "gaussian"
+    if (verbose) {
+      insight::format_alert("Using `ci_type = \"gaussian\"` because model is not bernoulli.")
+    }
+  }
+
   fitted_values <- stats::fitted(model)
   mf <- insight::get_data(model, verbose = FALSE)
 
@@ -186,7 +196,7 @@ binned_residuals <- function(model,
   }
   out <- out / n
 
-  quant <- stats::quantile(out, c((1 - ci) / 2, (1 + ci) / 2))
+  quant <- stats::quantile(out, c((1 - ci) / 2, (1 + ci) / 2), na.rm = TRUE)
   c(CI_low = quant[1L], CI_high = quant[2L])
 }
 
diff --git a/R/check_model.R b/R/check_model.R
index 58757468f..e261704a5 100644
--- a/R/check_model.R
+++ b/R/check_model.R
@@ -374,7 +374,7 @@ check_model.model_fit <- function(x,
   dat$INFLUENTIAL <- .influential_obs(model, threshold = threshold)
   dat$PP_CHECK <- .safe(check_predictions(model, ...))
   if (isTRUE(model_info$is_binomial)) {
-    dat$BINNED_RESID <- binned_residuals(model, ...)
+    dat$BINNED_RESID <- binned_residuals(model, verbose = verbose, ...)
   }
   if (isTRUE(model_info$is_count)) {
     dat$OVERDISPERSION <- .diag_overdispersion(model)
diff --git a/R/check_predictions.R b/R/check_predictions.R
index 832f9cc87..ac4eaf45c 100644
--- a/R/check_predictions.R
+++ b/R/check_predictions.R
@@ -197,10 +197,10 @@ pp_check.lm <- function(object,
   out <- .check_re_formula(out, object, iterations, re_formula, verbose, ...)
 
   # save information about model
-  if (!is.null(model_info)) {
-    minfo <- model_info
-  } else {
+  if (is.null(model_info)) {
     minfo <- insight::model_info(object)
+  } else {
+    minfo <- model_info
   }
 
   # glmmTMB returns column matrix for bernoulli
@@ -215,9 +215,10 @@ pp_check.lm <- function(object,
   }
 
   if (is.null(out)) {
-    insight::format_error(
-      sprintf("Could not simulate responses. Maybe there is no `simulate()` for objects of class `%s`?", class(object)[1])
-    )
+    insight::format_error(sprintf(
+      "Could not simulate responses. Maybe there is no `simulate()` for objects of class `%s`?",
+      class(object)[1]
+    ))
   }
 
   # get response data, and response term, to check for transformations
@@ -263,7 +264,7 @@ pp_check.glm <- function(object,
   out <- tryCatch(
     {
       matrix_sim <- stats::simulate(object, nsim = iterations, re.form = re_formula, ...)
-      as.data.frame(sapply(matrix_sim, function(i) i[, 1] / i[, 2], simplify = TRUE))
+      as.data.frame(sapply(matrix_sim, function(i) i[, 1] / rowSums(i, na.rm = TRUE), simplify = TRUE))
     },
     error = function(e) {
       NULL
@@ -274,9 +275,10 @@ pp_check.glm <- function(object,
   out <- .check_re_formula(out, object, iterations, re_formula, verbose, ...)
 
   if (is.null(out)) {
-    insight::format_error(
-      sprintf("Could not simulate responses. Maybe there is no `simulate()` for objects of class `%s`?", class(object)[1])
-    )
+    insight::format_error(sprintf(
+      "Could not simulate responses. Maybe there is no `simulate()` for objects of class `%s`?",
+      class(object)[1]
+    ))
   }
 
   # get response data, and response term
@@ -285,13 +287,13 @@ pp_check.glm <- function(object,
   )
   resp_string <- insight::find_terms(object)$response
 
-  out$y <- response[, 1] / response[, 2]
+  out$y <- response[, 1] / rowSums(response, na.rm = TRUE)
 
   # safe information about model
-  if (!is.null(model_info)) {
-    minfo <- model_info
-  } else {
+  if (is.null(model_info)) {
     minfo <- insight::model_info(object)
+  } else {
+    minfo <- model_info
   }
 
   attr(out, "check_range") <- check_range
@@ -363,14 +365,20 @@ print.performance_pp_check <- function(x, verbose = TRUE, ...) {
     if (is.numeric(original)) {
       if (min(replicated) > min(original)) {
         insight::print_color(
-          insight::format_message("Warning: Minimum value of original data is not included in the replicated data.", "Model may not capture the variation of the data."),
+          insight::format_message(
+            "Warning: Minimum value of original data is not included in the replicated data.",
+            "Model may not capture the variation of the data."
+          ),
           "red"
         )
       }
 
       if (max(replicated) < max(original)) {
         insight::print_color(
-          insight::format_message("Warning: Maximum value of original data is not included in the replicated data.", "Model may not capture the variation of the data."),
+          insight::format_message(
+            "Warning: Maximum value of original data is not included in the replicated data.",
+            "Model may not capture the variation of the data."
+          ),
           "red"
         )
       }
diff --git a/R/looic.R b/R/looic.R
index 8f0a0c66e..4ded6ccd7 100644
--- a/R/looic.R
+++ b/R/looic.R
@@ -12,6 +12,7 @@
 #' @return A list with four elements, the ELPD, LOOIC and their standard errors.
 #'
 #' @examplesIf require("rstanarm")
+#' \donttest{
 #' model <- suppressWarnings(rstanarm::stan_glm(
 #'   mpg ~ wt + cyl,
 #'   data = mtcars,
@@ -20,6 +21,7 @@
 #'   refresh = 0
 #' ))
 #' looic(model)
+#' }
 #' @export
 looic <- function(model, verbose = TRUE) {
   insight::check_if_installed("loo")
diff --git a/R/r2_bayes.R b/R/r2_bayes.R
index fc007489c..24e8269ba 100644
--- a/R/r2_bayes.R
+++ b/R/r2_bayes.R
@@ -30,67 +30,63 @@
 #'   `r2_posterior()` is the actual workhorse for `r2_bayes()` and
 #'   returns a posterior sample of Bayesian R2 values.
 #'
-#' @examples
+#' @examplesIf require("rstanarm") && require("rstantools") && require("BayesFactor") && require("brms")
 #' library(performance)
-#' if (require("rstanarm") && require("rstantools")) {
-#'   model <- suppressWarnings(stan_glm(
-#'     mpg ~ wt + cyl,
-#'     data = mtcars,
-#'     chains = 1,
-#'     iter = 500,
-#'     refresh = 0,
-#'     show_messages = FALSE
-#'   ))
-#'   r2_bayes(model)
+#' \donttest{
+#' model <- suppressWarnings(rstanarm::stan_glm(
+#'   mpg ~ wt + cyl,
+#'   data = mtcars,
+#'   chains = 1,
+#'   iter = 500,
+#'   refresh = 0,
+#'   show_messages = FALSE
+#' ))
+#' r2_bayes(model)
 #'
-#'   model <- suppressWarnings(stan_lmer(
-#'     Petal.Length ~ Petal.Width + (1 | Species),
-#'     data = iris,
-#'     chains = 1,
-#'     iter = 500,
-#'     refresh = 0
-#'   ))
-#'   r2_bayes(model)
+#' model <- suppressWarnings(rstanarm::stan_lmer(
+#'   Petal.Length ~ Petal.Width + (1 | Species),
+#'   data = iris,
+#'   chains = 1,
+#'   iter = 500,
+#'   refresh = 0
+#' ))
+#' r2_bayes(model)
 #' }
 #'
-#' if (require("BayesFactor")) {
-#'   BFM <- generalTestBF(mpg ~ qsec + gear, data = mtcars, progress = FALSE)
-#'   FM <- lmBF(mpg ~ qsec + gear, data = mtcars)
+#' BFM <- BayesFactor::generalTestBF(mpg ~ qsec + gear, data = mtcars, progress = FALSE)
+#' FM <- BayesFactor::lmBF(mpg ~ qsec + gear, data = mtcars)
 #'
-#'   r2_bayes(FM)
-#'   r2_bayes(BFM[3])
-#'   r2_bayes(BFM, average = TRUE) # across all models
+#' r2_bayes(FM)
+#' r2_bayes(BFM[3])
+#' r2_bayes(BFM, average = TRUE) # across all models
 #'
-#'   # with random effects:
-#'   mtcars$gear <- factor(mtcars$gear)
-#'   model <- lmBF(
-#'     mpg ~ hp + cyl + gear + gear:wt,
-#'     mtcars,
-#'     progress = FALSE,
-#'     whichRandom = c("gear", "gear:wt")
-#'   )
+#' # with random effects:
+#' mtcars$gear <- factor(mtcars$gear)
+#' model <- BayesFactor::lmBF(
+#'   mpg ~ hp + cyl + gear + gear:wt,
+#'   mtcars,
+#'   progress = FALSE,
+#'   whichRandom = c("gear", "gear:wt")
+#' )
 #'
-#'   r2_bayes(model)
-#' }
+#' r2_bayes(model)
 #'
 #' \donttest{
-#' if (require("brms")) {
-#'   model <- suppressWarnings(brms::brm(
-#'     mpg ~ wt + cyl,
-#'     data = mtcars,
-#'     silent = 2,
-#'     refresh = 0
-#'   ))
-#'   r2_bayes(model)
+#' model <- suppressWarnings(brms::brm(
+#'   mpg ~ wt + cyl,
+#'   data = mtcars,
+#'   silent = 2,
+#'   refresh = 0
+#' ))
+#' r2_bayes(model)
 #'
-#'   model <- suppressWarnings(brms::brm(
-#'     Petal.Length ~ Petal.Width + (1 | Species),
-#'     data = iris,
-#'     silent = 2,
-#'     refresh = 0
-#'   ))
-#'   r2_bayes(model)
-#' }
+#' model <- suppressWarnings(brms::brm(
+#'   Petal.Length ~ Petal.Width + (1 | Species),
+#'   data = iris,
+#'   silent = 2,
+#'   refresh = 0
+#' ))
+#' r2_bayes(model)
 #' }
 #' @references
 #' Gelman, A., Goodrich, B., Gabry, J., and Vehtari, A. (2018).
@@ -114,7 +110,7 @@ r2_bayes <- function(model, robust = TRUE, ci = 0.95, verbose = TRUE, ...) {
           mean(i)
         }
       }),
-      "SE" = rapply(r2_bayesian, function(i) {
+      SE = rapply(r2_bayesian, function(i) {
         if (robust) {
           stats::mad(i)
         } else {
@@ -122,9 +118,9 @@ r2_bayes <- function(model, robust = TRUE, ci = 0.95, verbose = TRUE, ...) {
         }
       }),
       # "Estimates" = rapply(r2_bayesian, bayestestR::point_estimate, centrality = "all", dispersion = TRUE),
-      "CI" = rapply(r2_bayesian, bayestestR::hdi, ci = ci),
-      "ci_method" = "HDI",
-      "robust" = robust
+      CI = rapply(r2_bayesian, bayestestR::hdi, ci = ci),
+      ci_method = "HDI",
+      robust = robust
     )
   } else {
     structure(
@@ -136,17 +132,17 @@ r2_bayes <- function(model, robust = TRUE, ci = 0.95, verbose = TRUE, ...) {
           mean(i)
         }
       }),
-      "SE" = lapply(r2_bayesian, function(i) {
+      SE = lapply(r2_bayesian, function(i) {
         if (robust) {
           stats::mad(i)
         } else {
           stats::sd(i)
         }
       }),
-      # "Estimates" = lapply(r2_bayesian, bayestestR::point_estimate, centrality = "all", dispersion = TRUE),
-      "CI" = lapply(r2_bayesian, bayestestR::hdi, ci = ci),
-      "ci_method" = "HDI",
-      "robust" = robust
+      # Estimates = lapply(r2_bayesian, bayestestR::point_estimate, centrality = "all", dispersion = TRUE),
+      CI = lapply(r2_bayesian, bayestestR::hdi, ci = ci),
+      ci_method = "HDI",
+      robust = robust
     )
   }
 }
@@ -178,13 +174,13 @@ r2_posterior.brmsfit <- function(model, verbose = TRUE, ...) {
         res <- insight::find_response(model)
         if (mi[[1]]$is_mixed) {
           br2_mv <- list(
-            "R2_Bayes" = rstantools::bayes_R2(
+            R2_Bayes = rstantools::bayes_R2(
               model,
               re.form = NULL,
               re_formula = NULL,
               summary = FALSE
             ),
-            "R2_Bayes_marginal" = rstantools::bayes_R2(
+            R2_Bayes_marginal = rstantools::bayes_R2(
               model,
               re.form = NA,
               re_formula = NA,
@@ -193,28 +189,28 @@ r2_posterior.brmsfit <- function(model, verbose = TRUE, ...) {
           )
           br2 <- lapply(seq_along(res), function(x) {
             list(
-              "R2_Bayes" = unname(as.vector(br2_mv$R2_Bayes[, x])),
-              "R2_Bayes_marginal" = unname(as.vector(br2_mv$R2_Bayes_marginal[, x]))
+              R2_Bayes = unname(as.vector(br2_mv$R2_Bayes[, x])),
+              R2_Bayes_marginal = unname(as.vector(br2_mv$R2_Bayes_marginal[, x]))
             )
           })
           names(br2) <- res
         } else {
-          br2_mv <- list("R2_Bayes" = rstantools::bayes_R2(model, summary = FALSE))
+          br2_mv <- list(R2_Bayes = rstantools::bayes_R2(model, summary = FALSE))
           br2 <- lapply(seq_along(res), function(x) {
-            list("R2_Bayes" = unname(as.vector(br2_mv$R2_Bayes[, x])))
+            list(R2_Bayes = unname(as.vector(br2_mv$R2_Bayes[, x])))
           })
           names(br2) <- res
         }
       } else {
         if (mi$is_mixed) {
           br2 <- list(
-            "R2_Bayes" = as.vector(rstantools::bayes_R2(
+            R2_Bayes = as.vector(rstantools::bayes_R2(
               model,
               re.form = NULL,
               re_formula = NULL,
               summary = FALSE
             )),
-            "R2_Bayes_marginal" = as.vector(rstantools::bayes_R2(
+            R2_Bayes_marginal = as.vector(rstantools::bayes_R2(
               model,
               re.form = NA,
               re_formula = NA,
@@ -224,7 +220,7 @@ r2_posterior.brmsfit <- function(model, verbose = TRUE, ...) {
           names(br2$R2_Bayes) <- rep("Conditional R2", length(br2$R2_Bayes))
           names(br2$R2_Bayes_marginal) <- rep("Marginal R2", length(br2$R2_Bayes))
         } else {
-          br2 <- list("R2_Bayes" = as.vector(rstantools::bayes_R2(model, summary = FALSE)))
+          br2 <- list(R2_Bayes = as.vector(rstantools::bayes_R2(model, summary = FALSE)))
           names(br2$R2_Bayes) <- rep("R2", length(br2$R2_Bayes))
         }
       }
@@ -339,10 +335,10 @@ r2_posterior.BFBayesFactor <- function(model,
 
 
   # Compute posterior model probabilities
-  if (!is.null(prior_odds)) {
-    prior_odds <- c(1, prior_odds)
-  } else {
+  if (is.null(prior_odds)) {
     prior_odds <- rep(1, nrow(BFMods))
+  } else {
+    prior_odds <- c(1, prior_odds)
   }
   posterior_odds <- prior_odds * BFMods$BF
   posterior_odds <- posterior_odds[-1] / posterior_odds[1]
@@ -416,7 +412,7 @@ as.data.frame.r2_bayes <- function(x, ...) {
     if (utils::packageVersion("BayesFactor") < package_version("0.9.12.4.3")) {
       insight::format_error("R2 for BayesFactor models with random effects requires BayesFactor v0.9.12.4.3 or higher.")
     }
-    insight::format_error("Woops, you seem to have stumbled on some weird edge case. Please file an issue at {.url https://github.com/easystats/performance/issues}")
+    insight::format_error("Woops, you seem to have stumbled on some weird edge case. Please file an issue at {.url https://github.com/easystats/performance/issues}") # nolint
   }
 
   out <- list(
diff --git a/man/binned_residuals.Rd b/man/binned_residuals.Rd
index 33e710f11..632e2a050 100644
--- a/man/binned_residuals.Rd
+++ b/man/binned_residuals.Rd
@@ -13,6 +13,7 @@ binned_residuals(
   ci_type = c("exact", "gaussian", "boot"),
   residuals = c("deviance", "pearson", "response"),
   iterations = 1000,
+  verbose = TRUE,
   ...
 )
 }
@@ -51,6 +52,8 @@ available.}
 \item{iterations}{Integer, the number of iterations to use for the
 bootstrap method. Only used if \code{ci_type = "boot"}.}
 
+\item{verbose}{Toggle warnings and messages.}
+
 \item{...}{Currently not used.}
 }
 \value{
diff --git a/man/looic.Rd b/man/looic.Rd
index 742ac3482..7f985f4a2 100644
--- a/man/looic.Rd
+++ b/man/looic.Rd
@@ -22,6 +22,7 @@ indicative of a better fit.
 }
 \examples{
 \dontshow{if (require("rstanarm")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf}
+\donttest{
 model <- suppressWarnings(rstanarm::stan_glm(
   mpg ~ wt + cyl,
   data = mtcars,
@@ -30,5 +31,6 @@ model <- suppressWarnings(rstanarm::stan_glm(
   refresh = 0
 ))
 looic(model)
+}
 \dontshow{\}) # examplesIf}
 }
diff --git a/man/r2_bayes.Rd b/man/r2_bayes.Rd
index 63b082e08..edc78150e 100644
--- a/man/r2_bayes.Rd
+++ b/man/r2_bayes.Rd
@@ -62,67 +62,65 @@ R2 takes both the fixed and random effects into account.
 returns a posterior sample of Bayesian R2 values.
 }
 \examples{
+\dontshow{if (require("rstanarm") && require("rstantools") && require("BayesFactor") && require("brms")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf}
 library(performance)
-if (require("rstanarm") && require("rstantools")) {
-  model <- suppressWarnings(stan_glm(
-    mpg ~ wt + cyl,
-    data = mtcars,
-    chains = 1,
-    iter = 500,
-    refresh = 0,
-    show_messages = FALSE
-  ))
-  r2_bayes(model)
-
-  model <- suppressWarnings(stan_lmer(
-    Petal.Length ~ Petal.Width + (1 | Species),
-    data = iris,
-    chains = 1,
-    iter = 500,
-    refresh = 0
-  ))
-  r2_bayes(model)
+\donttest{
+model <- suppressWarnings(rstanarm::stan_glm(
+  mpg ~ wt + cyl,
+  data = mtcars,
+  chains = 1,
+  iter = 500,
+  refresh = 0,
+  show_messages = FALSE
+))
+r2_bayes(model)
+
+model <- suppressWarnings(rstanarm::stan_lmer(
+  Petal.Length ~ Petal.Width + (1 | Species),
+  data = iris,
+  chains = 1,
+  iter = 500,
+  refresh = 0
+))
+r2_bayes(model)
 }
 
-if (require("BayesFactor")) {
-  BFM <- generalTestBF(mpg ~ qsec + gear, data = mtcars, progress = FALSE)
-  FM <- lmBF(mpg ~ qsec + gear, data = mtcars)
+BFM <- BayesFactor::generalTestBF(mpg ~ qsec + gear, data = mtcars, progress = FALSE)
+FM <- BayesFactor::lmBF(mpg ~ qsec + gear, data = mtcars)
 
-  r2_bayes(FM)
-  r2_bayes(BFM[3])
-  r2_bayes(BFM, average = TRUE) # across all models
+r2_bayes(FM)
+r2_bayes(BFM[3])
+r2_bayes(BFM, average = TRUE) # across all models
 
-  # with random effects:
-  mtcars$gear <- factor(mtcars$gear)
-  model <- lmBF(
-    mpg ~ hp + cyl + gear + gear:wt,
-    mtcars,
-    progress = FALSE,
-    whichRandom = c("gear", "gear:wt")
-  )
+# with random effects:
+mtcars$gear <- factor(mtcars$gear)
+model <- BayesFactor::lmBF(
+  mpg ~ hp + cyl + gear + gear:wt,
+  mtcars,
+  progress = FALSE,
+  whichRandom = c("gear", "gear:wt")
+)
 
-  r2_bayes(model)
-}
+r2_bayes(model)
 
 \donttest{
-if (require("brms")) {
-  model <- suppressWarnings(brms::brm(
-    mpg ~ wt + cyl,
-    data = mtcars,
-    silent = 2,
-    refresh = 0
-  ))
-  r2_bayes(model)
-
-  model <- suppressWarnings(brms::brm(
-    Petal.Length ~ Petal.Width + (1 | Species),
-    data = iris,
-    silent = 2,
-    refresh = 0
-  ))
-  r2_bayes(model)
-}
+model <- suppressWarnings(brms::brm(
+  mpg ~ wt + cyl,
+  data = mtcars,
+  silent = 2,
+  refresh = 0
+))
+r2_bayes(model)
+
+model <- suppressWarnings(brms::brm(
+  Petal.Length ~ Petal.Width + (1 | Species),
+  data = iris,
+  silent = 2,
+  refresh = 0
+))
+r2_bayes(model)
 }
+\dontshow{\}) # examplesIf}
 }
 \references{
 Gelman, A., Goodrich, B., Gabry, J., and Vehtari, A. (2018).
diff --git a/tests/testthat/test-binned_residuals.R b/tests/testthat/test-binned_residuals.R
index 6f0155092..e23404452 100644
--- a/tests/testthat/test-binned_residuals.R
+++ b/tests/testthat/test-binned_residuals.R
@@ -155,3 +155,22 @@ test_that("binned_residuals, bootstrapped CI", {
     tolerance = 1e-4
   )
 })
+
+test_that("binned_residuals, msg for non-bernoulli", {
+  skip_on_cran()
+  tot <- rep(10, 100)
+  suc <- rbinom(100, prob = 0.9, size = tot)
+
+  dat <- data.frame(tot, suc)
+  dat$prop <- suc / tot
+  dat$x1 <- as.factor(sample(1:5, 100, replace = TRUE))
+
+  mod <- glm(prop ~ x1,
+    family = binomial,
+    data = dat,
+    weights = tot
+  )
+
+  expect_message(binned_residuals(mod), regex = "Using `ci_type = \"gaussian\"`")
+  expect_silent(binned_residuals(mod, verbose = FALSE))
+})
diff --git a/tests/testthat/test-check_predictions.R b/tests/testthat/test-check_predictions.R
index 61d97f9b9..a840894dc 100644
--- a/tests/testthat/test-check_predictions.R
+++ b/tests/testthat/test-check_predictions.R
@@ -78,3 +78,53 @@ test_that("check_predictions, glmmTMB", {
   )
   expect_true(attributes(out)$model_info$is_bernoulli)
 })
+
+
+test_that("check_predictions, glm, binomial", {
+  skip_if(packageVersion("insight") <= "0.19.6")
+  data(mtcars)
+  set.seed(1)
+  tot <- rep(10, 100)
+  suc <- rbinom(100, prob = 0.9, size = tot)
+  dat <- data.frame(tot, suc)
+  dat$prop <- suc / tot
+
+  mod1 <- glm(cbind(suc, tot - suc) ~ 1,
+    family = binomial,
+    data = dat
+  )
+
+  mod2 <- glm(prop ~ 1,
+    family = binomial,
+    data = dat,
+    weights = tot
+  )
+
+  mod3 <- glm(cbind(suc, tot) ~ 1,
+    family = binomial,
+    data = dat
+  )
+
+  mod4 <- glm(am ~ 1,
+    family = binomial,
+    data = mtcars
+  )
+
+  set.seed(1)
+  out1 <- check_predictions(mod1)
+  set.seed(1)
+  out2 <- check_predictions(mod2)
+  set.seed(1)
+  out3 <- check_predictions(mod3)
+  set.seed(1)
+  out4 <- check_predictions(mod4)
+
+  expect_equal(head(out1$sim_1), c(1, 0.9, 0.9, 0.8, 1, 0.8), tolerance = 1e-4)
+  expect_false(attributes(out1)$model_info$is_bernoulli)
+  expect_equal(head(out2$sim_1), c(1, 0.9, 0.9, 0.8, 1, 0.8), tolerance = 1e-4)
+  expect_false(attributes(out2)$model_info$is_bernoulli)
+  expect_equal(head(out3$sim_1), c(0.4, 0.42105, 0.47368, 0.61111, 0.4, 0.61111), tolerance = 1e-3)
+  expect_false(attributes(out3)$model_info$is_bernoulli)
+  expect_equal(head(out4$sim_1), c(0, 0, 0, 1, 0, 1), tolerance = 1e-4)
+  expect_true(attributes(out4)$model_info$is_bernoulli)
+})