diff --git a/R/model_performance.mixed.R b/R/model_performance.mixed.R
index 2c9d0ea1b..499196dab 100644
--- a/R/model_performance.mixed.R
+++ b/R/model_performance.mixed.R
@@ -35,11 +35,9 @@
 #'   on returned indices.
 #' }
 #'
-#' @examples
-#' if (require("lme4")) {
-#'   model <- lmer(Petal.Length ~ Sepal.Length + (1 | Species), data = iris)
-#'   model_performance(model)
-#' }
+#' @examplesIf require("lme4")
+#' model <- lme4::lmer(Petal.Length ~ Sepal.Length + (1 | Species), data = iris)
+#' model_performance(model)
 #' @export
 model_performance.merMod <- function(model,
                                      metrics = "all",
diff --git a/R/performance_rmse.R b/R/performance_rmse.R
index b5044044f..0cc5eac90 100644
--- a/R/performance_rmse.R
+++ b/R/performance_rmse.R
@@ -20,16 +20,15 @@
 #'
 #' @return Numeric, the root mean squared error.
 #'
-#' @examples
-#' if (require("nlme")) {
-#'   m <- lme(distance ~ age, data = Orthodont)
+#' @examplesIf require("nlme")
+#' data(Orthodont, package = "nlme")
+#' m <- nlme::lme(distance ~ age, data = Orthodont)
 #'
-#'   # RMSE
-#'   performance_rmse(m, normalized = FALSE)
+#' # RMSE
+#' performance_rmse(m, normalized = FALSE)
 #'
-#'   # normalized RMSE
-#'   performance_rmse(m, normalized = TRUE)
-#' }
+#' # normalized RMSE
+#' performance_rmse(m, normalized = TRUE)
 #' @export
 performance_rmse <- function(model, normalized = FALSE, verbose = TRUE) {
   tryCatch(
diff --git a/R/performance_score.R b/R/performance_score.R
index 7c606c71f..58a701069 100644
--- a/R/performance_score.R
+++ b/R/performance_score.R
@@ -32,7 +32,7 @@
 #'
 #' @seealso [`performance_logloss()`]
 #'
-#' @examples
+#' @examplesIf require("glmmTMB")
 #' ## Dobson (1990) Page 93: Randomized Controlled Trial :
 #' counts <- c(18, 17, 15, 20, 10, 20, 25, 13, 12)
 #' outcome <- gl(3, 1, 9)
@@ -41,17 +41,15 @@
 #'
 #' performance_score(model)
 #' \dontrun{
-#' if (require("glmmTMB")) {
-#'   data(Salamanders)
-#'   model <- glmmTMB(
-#'     count ~ spp + mined + (1 | site),
-#'     zi = ~ spp + mined,
-#'     family = nbinom2(),
-#'     data = Salamanders
-#'   )
+#' data(Salamanders, package = "glmmTMB")
+#' model <- glmmTMB::glmmTMB(
+#'   count ~ spp + mined + (1 | site),
+#'   zi = ~ spp + mined,
+#'   family = nbinom2(),
+#'   data = Salamanders
+#' )
 #'
-#'   performance_score(model)
-#' }
+#' performance_score(model)
 #' }
 #' @export
 performance_score <- function(model, verbose = TRUE, ...) {
diff --git a/man/model_performance.merMod.Rd b/man/model_performance.merMod.Rd
index 2145ec379..519f1ee0a 100644
--- a/man/model_performance.merMod.Rd
+++ b/man/model_performance.merMod.Rd
@@ -58,8 +58,8 @@ on returned indices.
 }
 }
 \examples{
-if (require("lme4")) {
-  model <- lmer(Petal.Length ~ Sepal.Length + (1 | Species), data = iris)
-  model_performance(model)
-}
+\dontshow{if (require("lme4")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf}
+model <- lme4::lmer(Petal.Length ~ Sepal.Length + (1 | Species), data = iris)
+model_performance(model)
+\dontshow{\}) # examplesIf}
 }
diff --git a/man/performance_rmse.Rd b/man/performance_rmse.Rd
index cd9b84e87..bea4534b5 100644
--- a/man/performance_rmse.Rd
+++ b/man/performance_rmse.Rd
@@ -35,13 +35,14 @@ range of the response variable. Hence, lower values indicate
 less residual variance.
 }
 \examples{
-if (require("nlme")) {
-  m <- lme(distance ~ age, data = Orthodont)
+\dontshow{if (require("nlme")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf}
+data(Orthodont, package = "nlme")
+m <- nlme::lme(distance ~ age, data = Orthodont)
 
-  # RMSE
-  performance_rmse(m, normalized = FALSE)
+# RMSE
+performance_rmse(m, normalized = FALSE)
 
-  # normalized RMSE
-  performance_rmse(m, normalized = TRUE)
-}
+# normalized RMSE
+performance_rmse(m, normalized = TRUE)
+\dontshow{\}) # examplesIf}
 }
diff --git a/man/performance_score.Rd b/man/performance_score.Rd
index 2dc85faf5..162f3ed13 100644
--- a/man/performance_score.Rd
+++ b/man/performance_score.Rd
@@ -38,6 +38,7 @@ Code is partially based on
 \href{https://drizopoulos.github.io/GLMMadaptive/reference/scoring_rules.html}{GLMMadaptive::scoring_rules()}.
 }
 \examples{
+\dontshow{if (require("glmmTMB")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf}
 ## Dobson (1990) Page 93: Randomized Controlled Trial :
 counts <- c(18, 17, 15, 20, 10, 20, 25, 13, 12)
 outcome <- gl(3, 1, 9)
@@ -46,18 +47,17 @@ model <- glm(counts ~ outcome + treatment, family = poisson())
 
 performance_score(model)
 \dontrun{
-if (require("glmmTMB")) {
-  data(Salamanders)
-  model <- glmmTMB(
-    count ~ spp + mined + (1 | site),
-    zi = ~ spp + mined,
-    family = nbinom2(),
-    data = Salamanders
-  )
+data(Salamanders, package = "glmmTMB")
+model <- glmmTMB::glmmTMB(
+  count ~ spp + mined + (1 | site),
+  zi = ~ spp + mined,
+  family = nbinom2(),
+  data = Salamanders
+)
 
-  performance_score(model)
-}
+performance_score(model)
 }
+\dontshow{\}) # examplesIf}
 }
 \references{
 Carvalho, A. (2016). An overview of applications of proper scoring rules.