diff --git a/.gitignore b/.gitignore
index 10dcfe05d..ded75a1d6 100644
--- a/.gitignore
+++ b/.gitignore
@@ -13,6 +13,7 @@
 
 # Output files from R CMD check
 /*.Rcheck/
+revdep/
 
 # RStudio files
 .Rproj.user/
diff --git a/CRAN-SUBMISSION b/CRAN-SUBMISSION
index b8b2c021f..be214bfbb 100644
--- a/CRAN-SUBMISSION
+++ b/CRAN-SUBMISSION
@@ -1,3 +1,3 @@
-Version: 0.21.1
-Date: 2023-05-26 09:34:52 UTC
-SHA: 7ad4819088d188babebe5de6880d1230782b32ec
+Version: 0.21.2
+Date: 2023-09-16 12:58:15 UTC
+SHA: a1a80b72020f0287b81563a419eddb36f59fa18d
diff --git a/DESCRIPTION b/DESCRIPTION
index b5a22cec2..d77d1950d 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,7 +1,7 @@
 Type: Package
 Package: parameters
 Title: Processing of Model Parameters
-Version: 0.21.1.9
+Version: 0.21.2
 Authors@R:
     c(person(given = "Daniel",
              family = "Lüdecke",
@@ -81,7 +81,7 @@ Depends:
 Imports:
     bayestestR (>= 0.13.0),
     datawizard (>= 0.7.0),
-    insight (>= 0.19.3.2),
+    insight (>= 0.19.4),
     graphics,
     methods,
     stats,
@@ -213,4 +213,3 @@ 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 e7e71641c..c52c21fde 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -11,7 +11,7 @@
   accepts the `exponentiate` argument.
 
 * The `print()`, `print_html()`, `print_md()` and `format()` methods for
-  `model_parameters()` get an `add_reference` argument, to add the reference
+  `model_parameters()` get an `include_reference` argument, to add the reference
   category of categorical predictors to the parameters table.
 
 ## Bug fixes
diff --git a/R/1_model_parameters.R b/R/1_model_parameters.R
index cd73dc40b..160de9f1c 100644
--- a/R/1_model_parameters.R
+++ b/R/1_model_parameters.R
@@ -418,29 +418,25 @@ parameters <- model_parameters
 #'
 #' @inheritSection model_parameters Confidence intervals and approximation of degrees of freedom
 #'
-#' @examples
+#' @examplesIf require("boot", quietly = TRUE) && require("sandwich") && require("clubSandwich") && require("brglm2")
 #' library(parameters)
 #' model <- lm(mpg ~ wt + cyl, data = mtcars)
 #'
 #' model_parameters(model)
 #'
 #' # bootstrapped parameters
-#' if (require("boot", quietly = TRUE)) {
-#'   model_parameters(model, bootstrap = TRUE)
-#' }
+#' model_parameters(model, bootstrap = TRUE)
 #'
 #' # standardized parameters
 #' model_parameters(model, standardize = "refit")
 #'
 #' # robust, heteroskedasticity-consistent standard errors
-#' if (require("sandwich") && require("clubSandwich")) {
-#'   model_parameters(model, vcov = "HC3")
+#' model_parameters(model, vcov = "HC3")
 #'
-#'   model_parameters(model,
-#'     vcov = "vcovCL",
-#'     vcov_args = list(cluster = mtcars$cyl)
-#'   )
-#' }
+#' model_parameters(model,
+#'   vcov = "vcovCL",
+#'   vcov_args = list(cluster = mtcars$cyl)
+#' )
 #'
 #' # different p-value style in output
 #' model_parameters(model, p_digits = 5)
@@ -454,15 +450,13 @@ parameters <- model_parameters
 #' model_parameters(model, exponentiate = TRUE)
 #'
 #' # bias-corrected logistic regression with penalized maximum likelihood
-#' if (require("brglm2")) {
-#'   model <- glm(
-#'     vs ~ wt + cyl,
-#'     data = mtcars,
-#'     family = "binomial",
-#'     method = "brglmFit"
-#'   )
-#'   model_parameters(model)
-#' }
+#' model <- glm(
+#'   vs ~ wt + cyl,
+#'   data = mtcars,
+#'   family = "binomial",
+#'   method = "brglmFit"
+#' )
+#' model_parameters(model)
 #' }
 #' @return A data frame of indices related to the model's parameters.
 #' @export
diff --git a/R/2_ci.R b/R/2_ci.R
index 94dd1a278..df2b3b966 100644
--- a/R/2_ci.R
+++ b/R/2_ci.R
@@ -31,20 +31,19 @@
 #'
 #' @inheritSection model_parameters Confidence intervals and approximation of degrees of freedom
 #'
-#' @examples
+#' @examplesIf require("glmmTMB")
 #' \donttest{
 #' library(parameters)
-#' if (require("glmmTMB")) {
-#'   model <- glmmTMB(
-#'     count ~ spp + mined + (1 | site),
-#'     ziformula = ~mined,
-#'     family = poisson(),
-#'     data = Salamanders
-#'   )
+#' data(Salamanders, package = "glmmTMB")
+#' model <- glmmTMB::glmmTMB(
+#'   count ~ spp + mined + (1 | site),
+#'   ziformula = ~mined,
+#'   family = poisson(),
+#'   data = Salamanders
+#' )
 #'
-#'   ci(model)
-#'   ci(model, component = "zi")
-#' }
+#' ci(model)
+#' ci(model, component = "zi")
 #' }
 #' @export
 ci.default <- function(x, ci = 0.95, dof = NULL, method = NULL, ...) {
diff --git a/R/bootstrap_model.R b/R/bootstrap_model.R
index a4c03d160..2f53c2895 100644
--- a/R/bootstrap_model.R
+++ b/R/bootstrap_model.R
@@ -40,18 +40,14 @@
 #'
 #' @seealso [`bootstrap_parameters()`], [`simulate_model()`], [`simulate_parameters()`]
 #'
-#' @examples
+#' @examplesIf require("boot", quietly = TRUE) && require("emmeans", quietly = TRUE)
 #' \dontrun{
-#' if (require("boot", quietly = TRUE)) {
-#'   model <- lm(mpg ~ wt + factor(cyl), data = mtcars)
-#'   b <- bootstrap_model(model)
-#'   print(head(b))
+#' model <- lm(mpg ~ wt + factor(cyl), data = mtcars)
+#' b <- bootstrap_model(model)
+#' print(head(b))
 #'
-#'   if (require("emmeans", quietly = TRUE)) {
-#'     est <- emmeans(b, consec ~ cyl)
-#'     print(model_parameters(est))
-#'   }
-#' }
+#' est <- emmeans::emmeans(b, consec ~ cyl)
+#' print(model_parameters(est))
 #' }
 #' @export
 bootstrap_model <- function(model,
diff --git a/R/bootstrap_parameters.R b/R/bootstrap_parameters.R
index 851f157e6..c02b1a627 100644
--- a/R/bootstrap_parameters.R
+++ b/R/bootstrap_parameters.R
@@ -33,19 +33,15 @@
 #'   p-values can be biased, and it is suggested to use proper permutation tests
 #'   to obtain non-parametric p-values.
 #'
-#' @examples
+#' @examplesIf require("boot", quietly = TRUE) && require("emmeans", quietly = TRUE)
 #' \dontrun{
-#' if (require("boot", quietly = TRUE)) {
-#'   set.seed(2)
-#'   model <- lm(Sepal.Length ~ Species * Petal.Width, data = iris)
-#'   b <- bootstrap_parameters(model)
-#'   print(b)
+#' set.seed(2)
+#' model <- lm(Sepal.Length ~ Species * Petal.Width, data = iris)
+#' b <- bootstrap_parameters(model)
+#' print(b)
 #'
-#'   if (require("emmeans")) {
-#'     est <- emmeans(b, trt.vs.ctrl ~ Species)
-#'     print(model_parameters(est))
-#'   }
-#' }
+#' est <- emmeans::emmeans(b, trt.vs.ctrl ~ Species)
+#' print(model_parameters(est))
 #' }
 #' @export
 bootstrap_parameters <- function(model,
diff --git a/R/cluster_discrimination.R b/R/cluster_discrimination.R
index e8cd5ccbd..9e0265199 100644
--- a/R/cluster_discrimination.R
+++ b/R/cluster_discrimination.R
@@ -14,14 +14,12 @@
 #' [`performance::check_clusterstructure()`] to check suitability of data for
 #' clustering.
 #'
-#' @examples
-#' if (requireNamespace("MASS", quietly = TRUE)) {
-#'   # Retrieve group classification from hierarchical cluster analysis
-#'   clustering <- cluster_analysis(iris[, 1:4], n = 3)
+#' @examplesIf requireNamespace("MASS", quietly = TRUE)
+#' # Retrieve group classification from hierarchical cluster analysis
+#' clustering <- cluster_analysis(iris[, 1:4], n = 3)
 #'
-#'   # Goodness of group classification
-#'   cluster_discrimination(clustering)
-#' }
+#' # Goodness of group classification
+#' cluster_discrimination(clustering)
 #' @export
 cluster_discrimination <- function(x, cluster_groups = NULL, ...) {
   UseMethod("cluster_discrimination")
diff --git a/R/cluster_performance.R b/R/cluster_performance.R
index 3752fc85e..9c7f506db 100644
--- a/R/cluster_performance.R
+++ b/R/cluster_performance.R
@@ -61,14 +61,12 @@ cluster_performance.hclust <- function(model, data, clusters, ...) {
 
 
 #' @rdname cluster_performance
-#' @examples
+#' @examplesIf require("dbscan", quietly = TRUE)
 #' # DBSCAN
-#' if (require("dbscan", quietly = TRUE)) {
-#'   model <- dbscan::dbscan(iris[1:4], eps = 1.45, minPts = 10)
+#' model <- dbscan::dbscan(iris[1:4], eps = 1.45, minPts = 10)
 #'
-#'   rez <- cluster_performance(model, iris[1:4])
-#'   rez
-#' }
+#' rez <- cluster_performance(model, iris[1:4])
+#' rez
 #' @export
 cluster_performance.dbscan <- function(model, data, ...) {
   if (is.null(data)) {
diff --git a/R/convert_efa_to_cfa.R b/R/convert_efa_to_cfa.R
index 6d79fa5d9..739144bee 100644
--- a/R/convert_efa_to_cfa.R
+++ b/R/convert_efa_to_cfa.R
@@ -8,22 +8,21 @@
 #' @param max_per_dimension Maximum number of variables to keep per dimension.
 #' @inheritParams principal_components
 #'
-#' @examples
+#' @examplesIf require("psych") && require("lavaan")
 #' \donttest{
 #' library(parameters)
-#' if (require("psych") && require("lavaan")) {
-#'   efa <- psych::fa(attitude, nfactors = 3)
+#' data(attitude)
+#' efa <- psych::fa(attitude, nfactors = 3)
 #'
-#'   model1 <- efa_to_cfa(efa)
-#'   model2 <- efa_to_cfa(efa, threshold = 0.3)
-#'   model3 <- efa_to_cfa(efa, max_per_dimension = 2)
+#' model1 <- efa_to_cfa(efa)
+#' model2 <- efa_to_cfa(efa, threshold = 0.3)
+#' model3 <- efa_to_cfa(efa, max_per_dimension = 2)
 #'
-#'   suppressWarnings(anova(
-#'     lavaan::cfa(model1, data = attitude),
-#'     lavaan::cfa(model2, data = attitude),
-#'     lavaan::cfa(model3, data = attitude)
-#'   ))
-#' }
+#' suppressWarnings(anova(
+#'   lavaan::cfa(model1, data = attitude),
+#'   lavaan::cfa(model2, data = attitude),
+#'   lavaan::cfa(model3, data = attitude)
+#' ))
 #' }
 #' @return Converted index.
 #' @export
@@ -42,9 +41,10 @@ convert_efa_to_cfa.fa <- function(model,
                                   max_per_dimension = NULL,
                                   ...) {
   .efa_to_cfa(model_parameters(model, threshold = threshold, ...),
-              names = names,
-              max_per_dimension = max_per_dimension,
-              ...)
+    names = names,
+    max_per_dimension = max_per_dimension,
+    ...
+  )
 }
 
 #' @export
@@ -78,7 +78,7 @@ efa_to_cfa <- convert_efa_to_cfa
 
 
 #' @keywords internal
-.efa_to_cfa <- function(loadings, names = NULL, max_per_dimension=NULL, ...) {
+.efa_to_cfa <- function(loadings, names = NULL, max_per_dimension = NULL, ...) {
   loadings <- attributes(loadings)$loadings_long
 
   # Get dimension names
@@ -100,7 +100,6 @@ efa_to_cfa <- convert_efa_to_cfa
   cfa <- NULL
   # Iterate over dimensions
   for (i in seq_along(names)) {
-
     # Find correct subset
     items <- loadings[loadings$Component == unique(loadings$Component)[i], ]
 
diff --git a/R/display.R b/R/display.R
index e9549821a..036d28154 100644
--- a/R/display.R
+++ b/R/display.R
@@ -91,7 +91,7 @@ display.parameters_model <- function(object,
                                      font_size = "100%",
                                      line_padding = 4,
                                      column_labels = NULL,
-                                     add_reference = FALSE,
+                                     include_reference = FALSE,
                                      verbose = TRUE,
                                      ...) {
   if (identical(format, "html")) {
diff --git a/R/dominance_analysis.R b/R/dominance_analysis.R
index 98e83914d..90d991ecb 100644
--- a/R/dominance_analysis.R
+++ b/R/dominance_analysis.R
@@ -31,9 +31,9 @@
 #' @param contrasts A named list of [`contrasts`] used by the model object.
 #' This list is required in order for the correct mapping of parameters to
 #' predictors in the output when the model creates indicator codes for factor
-#' variables using [`model_matrix`]. By default, the `contrast` element from
-#' the model object submitted is used. If the model object does not have a
-#' `contrast` element the user can supply this named list.
+#' variables using [`insight::get_modelmatrix()`]. By default, the `contrast`
+#' element from the model object submitted is used. If the model object does
+#' not have a `contrast` element the user can supply this named list.
 #'
 #' @param ...  Not used at current.
 #'
@@ -115,24 +115,22 @@
 #'
 #' @author Joseph Luchman
 #'
-#' @examples
-#' if (require("domir") && require("performance")) {
-#'   data(mtcars)
+#' @examplesIf require("domir") && require("performance")
+#' data(mtcars)
 #'
-#'   # Dominance Analysis with Logit Regression
-#'   model <- glm(vs ~ cyl + carb + mpg, data = mtcars, family = binomial())
+#' # Dominance Analysis with Logit Regression
+#' model <- glm(vs ~ cyl + carb + mpg, data = mtcars, family = binomial())
 #'
-#'   performance::r2(model)
-#'   dominance_analysis(model)
+#' performance::r2(model)
+#' dominance_analysis(model)
 #'
-#'   # Dominance Analysis with Weighted Logit Regression
-#'   model_wt <- glm(vs ~ cyl + carb + mpg,
-#'     data = mtcars,
-#'     weights = wt, family = quasibinomial()
-#'   )
+#' # Dominance Analysis with Weighted Logit Regression
+#' model_wt <- glm(vs ~ cyl + carb + mpg,
+#'   data = mtcars,
+#'   weights = wt, family = quasibinomial()
+#' )
 #'
-#'   dominance_analysis(model_wt, quote_args = "weights")
-#' }
+#' dominance_analysis(model_wt, quote_args = "weights")
 #' @export
 dominance_analysis <- function(model, sets = NULL, all = NULL,
                                conditional = TRUE, complete = TRUE,
diff --git a/R/format.R b/R/format.R
index d006b6447..5c09ca3d0 100644
--- a/R/format.R
+++ b/R/format.R
@@ -15,7 +15,7 @@ format.parameters_model <- function(x,
                                     zap_small = FALSE,
                                     format = NULL,
                                     groups = NULL,
-                                    add_reference = FALSE,
+                                    include_reference = FALSE,
                                     ...) {
   # save attributes
   coef_name <- attributes(x)$coefficient_name
@@ -168,7 +168,7 @@ format.parameters_model <- function(x,
       ci_width = ci_width,
       ci_brackets = ci_brackets,
       zap_small = zap_small,
-      add_reference = add_reference,
+      include_reference = include_reference,
       ...
     )
   } else {
@@ -185,7 +185,7 @@ format.parameters_model <- function(x,
       format = format,
       coef_name = coef_name,
       zap_small = zap_small,
-      add_reference = add_reference,
+      include_reference = include_reference,
       ...
     )
   }
diff --git a/R/n_factors.R b/R/n_factors.R
index 342fe7398..8d4d1261e 100644
--- a/R/n_factors.R
+++ b/R/n_factors.R
@@ -124,7 +124,9 @@ n_factors <- function(x,
   }
 
   # Get number of observations
-  if (!is.data.frame(x)) {
+  if (is.data.frame(x)) {
+    nobs <- nrow(x)
+  } else {
     if (is.numeric(x) && !is.null(cor)) {
       nobs <- x
       package <- package[!package %in% c("pcdimension", "PCDimension")]
@@ -133,8 +135,6 @@ n_factors <- function(x,
         "Please input the correlation matrix via the `cor` argument and the number of rows / observations via the first argument."
       )
     }
-  } else {
-    nobs <- nrow(x)
   }
 
   # Get only numeric
@@ -577,7 +577,6 @@ print.n_clusters <- print.n_factors
                                 cor = NULL,
                                 nobs = NULL,
                                 type = "FA") {
-
   # Altnerative version of parralel analysis
   # Not used because already included in nFactors
 
diff --git a/R/principal_components.R b/R/principal_components.R
index 4245332a0..371befa30 100644
--- a/R/principal_components.R
+++ b/R/principal_components.R
@@ -420,6 +420,14 @@ principal_components.data.frame <- function(x,
   } else if (n >= ncol(x)) {
     n <- ncol(x) - 1
   }
+
+  ## TODO: the next if-statement was removed by Dom, but this breaks
+  ## performance code. Need to check, so we for now add this back
+
+  # sanity check - we need at least two factors
+  if (n < 2 && ncol(x) >= 2) {
+    n <- 2
+  }
   n
 }
 
diff --git a/R/print.parameters_model.R b/R/print.parameters_model.R
index debe8dd20..95910b731 100644
--- a/R/print.parameters_model.R
+++ b/R/print.parameters_model.R
@@ -87,7 +87,7 @@
 #'   labels will be used as parameters names. The latter only works for "labelled"
 #'   data, i.e. if the data used to fit the model had `"label"` and `"labels"`
 #'   attributes. See also section _Global Options to Customize Messages when Printing_.
-#' @param add_reference Logical, if `TRUE`, the reference level of factors will
+#' @param include_reference Logical, if `TRUE`, the reference level of factors will
 #'   be added to the parameters table. This is only relevant for models with
 #'   categorical predictors. The coefficient for the reference level is always
 #'   `0` (except when `exponentiate = TRUE`, then the coefficient will be `1`),
@@ -238,7 +238,7 @@ print.parameters_model <- function(x,
                                    groups = NULL,
                                    column_width = NULL,
                                    ci_brackets = c("[", "]"),
-                                   add_reference = FALSE,
+                                   include_reference = FALSE,
                                    ...) {
   # save original input
   orig_x <- x
@@ -288,7 +288,7 @@ print.parameters_model <- function(x,
     ci_brackets = ci_brackets,
     format = "text",
     groups = groups,
-    add_reference = add_reference,
+    include_reference = include_reference,
     ...
   )
 
@@ -385,7 +385,7 @@ print.parameters_random <- function(x, digits = 2, ...) {
                         ci_brackets = TRUE,
                         format = "text",
                         group = NULL,
-                        add_reference = FALSE,
+                        include_reference = FALSE,
                         ...) {
   format(
     x,
@@ -400,7 +400,7 @@ print.parameters_random <- function(x, digits = 2, ...) {
     zap_small = zap_small,
     format = format,
     group = group,
-    add_reference = add_reference,
+    include_reference = include_reference,
     ...
   )
 }
diff --git a/R/print_html.R b/R/print_html.R
index 5be9f9b98..42bae3da7 100644
--- a/R/print_html.R
+++ b/R/print_html.R
@@ -22,7 +22,7 @@ print_html.parameters_model <- function(x,
                                         font_size = "100%",
                                         line_padding = 4,
                                         column_labels = NULL,
-                                        add_reference = FALSE,
+                                        include_reference = FALSE,
                                         verbose = TRUE,
                                         ...) {
   # check if user supplied digits attributes
@@ -83,7 +83,7 @@ print_html.parameters_model <- function(x,
     ci_brackets = ci_brackets,
     format = "html",
     groups = groups,
-    add_reference = add_reference,
+    include_reference = include_reference,
     ...
   )
 
diff --git a/R/print_md.R b/R/print_md.R
index d95e7fe58..264a42bef 100644
--- a/R/print_md.R
+++ b/R/print_md.R
@@ -19,7 +19,7 @@ print_md.parameters_model <- function(x,
                                       show_formula = FALSE,
                                       zap_small = FALSE,
                                       groups = NULL,
-                                      add_reference = FALSE,
+                                      include_reference = FALSE,
                                       verbose = TRUE,
                                       ...) {
   # check if user supplied digits attributes
@@ -67,7 +67,7 @@ print_md.parameters_model <- function(x,
     ci_brackets = ci_brackets,
     format = "markdown",
     groups = groups,
-    add_reference = add_reference,
+    include_reference = include_reference,
     ...
   )
 
diff --git a/R/reduce_parameters.R b/R/reduce_parameters.R
index e4827b65a..331b44f55 100644
--- a/R/reduce_parameters.R
+++ b/R/reduce_parameters.R
@@ -204,7 +204,9 @@ principal_components.merMod <- principal_components.lm
 
   insight::check_if_installed("DRR")
 
-  junk <- utils::capture.output(suppressMessages(rez <- DRR::drr(x, n)))
+  junk <- utils::capture.output(suppressMessages({
+    rez <- DRR::drr(x, n)
+  }))
 
   features <- as.data.frame(rez$fitted.data)
   names(features) <- paste0("DRR", seq_len(ncol(features)))
diff --git a/R/utils_format.R b/R/utils_format.R
index 144f139ab..11dc10983 100644
--- a/R/utils_format.R
+++ b/R/utils_format.R
@@ -280,7 +280,7 @@
                                              format = NULL,
                                              coef_name = NULL,
                                              zap_small = FALSE,
-                                             add_reference = FALSE,
+                                             include_reference = FALSE,
                                              ...) {
   # default brackets are parenthesis for HTML / MD
   if ((is.null(ci_brackets) || isTRUE(ci_brackets)) && (identical(format, "html") || identical(format, "markdown"))) {
@@ -310,7 +310,7 @@
   }
 
   # add the coefficient for the base-(reference)-level of factors?
-  if (add_reference) {
+  if (include_reference) {
     x <- .add_reference_level(x)
   }
 
@@ -851,7 +851,7 @@
                                                 ci_width = "auto",
                                                 ci_brackets = TRUE,
                                                 zap_small = FALSE,
-                                                add_reference = FALSE,
+                                                include_reference = FALSE,
                                                 ...) {
   final_table <- list()
 
@@ -927,7 +927,7 @@
   tables <- split(x, f = split_by)
 
   # sanity check - only preserve tables with any data in data frames
-  tables <- tables[sapply(tables, nrow) > 0]
+  tables <- tables[vapply(tables, nrow, numeric(1)) > 0]
 
 
   # fix table names for random effects, when we only have random
@@ -993,10 +993,10 @@
     # if (all(is.na(tables[[type]]$CI_high))) tables[[type]]$CI_high <- NULL
 
     # Don't print if empty col
-    tables[[type]][sapply(colnames(tables[[type]]), function(x) {
+    tables[[type]][vapply(colnames(tables[[type]]), function(x) {
       col <- tables[[type]][[x]]
       (all(col == "") | all(is.na(col))) && !grepl("_CI_(high|low)$", x)
-    })] <- NULL
+    }, logical(1))] <- NULL
 
     attr(tables[[type]], "digits") <- digits
     attr(tables[[type]], "ci_digits") <- ci_digits
@@ -1052,7 +1052,7 @@
     }
 
     # add the coefficient for the base-(reference)-level of factors?
-    if (add_reference) {
+    if (include_reference) {
       tables[[type]] <- .add_reference_level(tables[[type]])
     }
 
@@ -1151,7 +1151,7 @@
   }
 
   # then check for correct column length
-  col_len <- sapply(final_table, function(i) length(colnames(i)))
+  col_len <- vapply(final_table, function(i) length(colnames(i)), numeric(1))
 
   # remove non matching columns
   if (!all(col_len) == max(col_len)) {
diff --git a/cran-comments.md b/cran-comments.md
index 249895727..54662aff3 100644
--- a/cran-comments.md
+++ b/cran-comments.md
@@ -1,4 +1,4 @@
-Regular update.
+Maintainance release.
 
 ## revdepcheck results
 
diff --git a/man/bootstrap_model.Rd b/man/bootstrap_model.Rd
index b538af287..39c329979 100644
--- a/man/bootstrap_model.Rd
+++ b/man/bootstrap_model.Rd
@@ -81,18 +81,16 @@ to obtain non-parametric p-values.
 }
 
 \examples{
+\dontshow{if (require("boot", quietly = TRUE) && require("emmeans", quietly = TRUE)) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf}
 \dontrun{
-if (require("boot", quietly = TRUE)) {
-  model <- lm(mpg ~ wt + factor(cyl), data = mtcars)
-  b <- bootstrap_model(model)
-  print(head(b))
+model <- lm(mpg ~ wt + factor(cyl), data = mtcars)
+b <- bootstrap_model(model)
+print(head(b))
 
-  if (require("emmeans", quietly = TRUE)) {
-    est <- emmeans(b, consec ~ cyl)
-    print(model_parameters(est))
-  }
-}
+est <- emmeans::emmeans(b, consec ~ cyl)
+print(model_parameters(est))
 }
+\dontshow{\}) # examplesIf}
 }
 \seealso{
 \code{\link[=bootstrap_parameters]{bootstrap_parameters()}}, \code{\link[=simulate_model]{simulate_model()}}, \code{\link[=simulate_parameters]{simulate_parameters()}}
diff --git a/man/bootstrap_parameters.Rd b/man/bootstrap_parameters.Rd
index 26e7d6ccf..1041eb8ac 100644
--- a/man/bootstrap_parameters.Rd
+++ b/man/bootstrap_parameters.Rd
@@ -76,19 +76,17 @@ to obtain non-parametric p-values.
 }
 
 \examples{
+\dontshow{if (require("boot", quietly = TRUE) && require("emmeans", quietly = TRUE)) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf}
 \dontrun{
-if (require("boot", quietly = TRUE)) {
-  set.seed(2)
-  model <- lm(Sepal.Length ~ Species * Petal.Width, data = iris)
-  b <- bootstrap_parameters(model)
-  print(b)
+set.seed(2)
+model <- lm(Sepal.Length ~ Species * Petal.Width, data = iris)
+b <- bootstrap_parameters(model)
+print(b)
 
-  if (require("emmeans")) {
-    est <- emmeans(b, trt.vs.ctrl ~ Species)
-    print(model_parameters(est))
-  }
-}
+est <- emmeans::emmeans(b, trt.vs.ctrl ~ Species)
+print(model_parameters(est))
 }
+\dontshow{\}) # examplesIf}
 }
 \references{
 Davison, A. C., & Hinkley, D. V. (1997). Bootstrap methods and their
diff --git a/man/ci.default.Rd b/man/ci.default.Rd
index ed0a161d8..57ffe3539 100644
--- a/man/ci.default.Rd
+++ b/man/ci.default.Rd
@@ -225,18 +225,19 @@ which is converted into a p-value using \code{\link[bayestestR:pd_to_p]{bayestes
 }
 
 \examples{
+\dontshow{if (require("glmmTMB")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf}
 \donttest{
 library(parameters)
-if (require("glmmTMB")) {
-  model <- glmmTMB(
-    count ~ spp + mined + (1 | site),
-    ziformula = ~mined,
-    family = poisson(),
-    data = Salamanders
-  )
+data(Salamanders, package = "glmmTMB")
+model <- glmmTMB::glmmTMB(
+  count ~ spp + mined + (1 | site),
+  ziformula = ~mined,
+  family = poisson(),
+  data = Salamanders
+)
 
-  ci(model)
-  ci(model, component = "zi")
-}
+ci(model)
+ci(model, component = "zi")
 }
+\dontshow{\}) # examplesIf}
 }
diff --git a/man/cluster_discrimination.Rd b/man/cluster_discrimination.Rd
index 6d0d1392f..161dd9dd2 100644
--- a/man/cluster_discrimination.Rd
+++ b/man/cluster_discrimination.Rd
@@ -20,13 +20,13 @@ determines the goodness of classification for each cluster group. See \code{MASS
 for details.
 }
 \examples{
-if (requireNamespace("MASS", quietly = TRUE)) {
-  # Retrieve group classification from hierarchical cluster analysis
-  clustering <- cluster_analysis(iris[, 1:4], n = 3)
+\dontshow{if (requireNamespace("MASS", quietly = TRUE)) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf}
+# Retrieve group classification from hierarchical cluster analysis
+clustering <- cluster_analysis(iris[, 1:4], n = 3)
 
-  # Goodness of group classification
-  cluster_discrimination(clustering)
-}
+# Goodness of group classification
+cluster_discrimination(clustering)
+\dontshow{\}) # examplesIf}
 }
 \seealso{
 \code{\link[=n_clusters]{n_clusters()}} to determine the number of clusters to extract,
diff --git a/man/cluster_performance.Rd b/man/cluster_performance.Rd
index 1ad7f2f3c..90e3ff15c 100644
--- a/man/cluster_performance.Rd
+++ b/man/cluster_performance.Rd
@@ -41,13 +41,13 @@ clusters <- cutree(model, 3)
 
 rez <- cluster_performance(model, data, clusters)
 rez
+\dontshow{if (require("dbscan", quietly = TRUE)) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf}
 # DBSCAN
-if (require("dbscan", quietly = TRUE)) {
-  model <- dbscan::dbscan(iris[1:4], eps = 1.45, minPts = 10)
+model <- dbscan::dbscan(iris[1:4], eps = 1.45, minPts = 10)
 
-  rez <- cluster_performance(model, iris[1:4])
-  rez
-}
+rez <- cluster_performance(model, iris[1:4])
+rez
+\dontshow{\}) # examplesIf}
 # Retrieve performance from parameters
 params <- model_parameters(kmeans(iris[1:4], 3))
 cluster_performance(params)
diff --git a/man/convert_efa_to_cfa.Rd b/man/convert_efa_to_cfa.Rd
index bdaa4c51e..57871b866 100644
--- a/man/convert_efa_to_cfa.Rd
+++ b/man/convert_efa_to_cfa.Rd
@@ -41,20 +41,21 @@ Enables a conversion between Exploratory Factor Analysis (EFA) and
 Confirmatory Factor Analysis (CFA) \code{lavaan}-ready structure.
 }
 \examples{
+\dontshow{if (require("psych") && require("lavaan")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf}
 \donttest{
 library(parameters)
-if (require("psych") && require("lavaan")) {
-  efa <- psych::fa(attitude, nfactors = 3)
-
-  model1 <- efa_to_cfa(efa)
-  model2 <- efa_to_cfa(efa, threshold = 0.3)
-  model3 <- efa_to_cfa(efa, max_per_dimension = 2)
-
-  suppressWarnings(anova(
-    lavaan::cfa(model1, data = attitude),
-    lavaan::cfa(model2, data = attitude),
-    lavaan::cfa(model3, data = attitude)
-  ))
-}
+data(attitude)
+efa <- psych::fa(attitude, nfactors = 3)
+
+model1 <- efa_to_cfa(efa)
+model2 <- efa_to_cfa(efa, threshold = 0.3)
+model3 <- efa_to_cfa(efa, max_per_dimension = 2)
+
+suppressWarnings(anova(
+  lavaan::cfa(model1, data = attitude),
+  lavaan::cfa(model2, data = attitude),
+  lavaan::cfa(model3, data = attitude)
+))
 }
+\dontshow{\}) # examplesIf}
 }
diff --git a/man/display.parameters_model.Rd b/man/display.parameters_model.Rd
index 263fe680a..a94a13408 100644
--- a/man/display.parameters_model.Rd
+++ b/man/display.parameters_model.Rd
@@ -33,7 +33,7 @@
   font_size = "100\%",
   line_padding = 4,
   column_labels = NULL,
-  add_reference = FALSE,
+  include_reference = FALSE,
   verbose = TRUE,
   ...
 )
@@ -75,7 +75,7 @@
   zap_small = FALSE,
   format = NULL,
   groups = NULL,
-  add_reference = FALSE,
+  include_reference = FALSE,
   ...
 )
 
@@ -100,7 +100,7 @@
   font_size = "100\%",
   line_padding = 4,
   column_labels = NULL,
-  add_reference = FALSE,
+  include_reference = FALSE,
   verbose = TRUE,
   ...
 )
@@ -123,7 +123,7 @@
   show_formula = FALSE,
   zap_small = FALSE,
   groups = NULL,
-  add_reference = FALSE,
+  include_reference = FALSE,
   verbose = TRUE,
   ...
 )
@@ -236,7 +236,7 @@ places than \code{digits} are printed in scientific notation.}
 \item{column_labels}{Labels of columns for HTML tables. If \code{NULL}, automatic
 column names are generated. See 'Examples'.}
 
-\item{add_reference}{Logical, if \code{TRUE}, the reference level of factors will
+\item{include_reference}{Logical, if \code{TRUE}, the reference level of factors will
 be added to the parameters table. This is only relevant for models with
 categorical predictors. The coefficient for the reference level is always
 \code{0} (except when \code{exponentiate = TRUE}, then the coefficient will be \code{1}),
diff --git a/man/dominance_analysis.Rd b/man/dominance_analysis.Rd
index 70a1473b4..18732f606 100644
--- a/man/dominance_analysis.Rd
+++ b/man/dominance_analysis.Rd
@@ -51,9 +51,9 @@ from being evaluated before being applied to the model and causing an error.}
 \item{contrasts}{A named list of \code{\link{contrasts}} used by the model object.
 This list is required in order for the correct mapping of parameters to
 predictors in the output when the model creates indicator codes for factor
-variables using \code{\link{model_matrix}}. By default, the \code{contrast} element from
-the model object submitted is used. If the model object does not have a
-\code{contrast} element the user can supply this named list.}
+variables using \code{\link[insight:get_modelmatrix]{insight::get_modelmatrix()}}. By default, the \code{contrast}
+element from the model object submitted is used. If the model object does
+not have a \code{contrast} element the user can supply this named list.}
 
 \item{...}{Not used at current.}
 }
@@ -124,23 +124,23 @@ When \code{performance::r2()} returns multiple values, only the first is used
 by default.
 }
 \examples{
-if (require("domir") && require("performance")) {
-  data(mtcars)
+\dontshow{if (require("domir") && require("performance")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf}
+data(mtcars)
 
-  # Dominance Analysis with Logit Regression
-  model <- glm(vs ~ cyl + carb + mpg, data = mtcars, family = binomial())
+# Dominance Analysis with Logit Regression
+model <- glm(vs ~ cyl + carb + mpg, data = mtcars, family = binomial())
 
-  performance::r2(model)
-  dominance_analysis(model)
+performance::r2(model)
+dominance_analysis(model)
 
-  # Dominance Analysis with Weighted Logit Regression
-  model_wt <- glm(vs ~ cyl + carb + mpg,
-    data = mtcars,
-    weights = wt, family = quasibinomial()
-  )
+# Dominance Analysis with Weighted Logit Regression
+model_wt <- glm(vs ~ cyl + carb + mpg,
+  data = mtcars,
+  weights = wt, family = quasibinomial()
+)
 
-  dominance_analysis(model_wt, quote_args = "weights")
-}
+dominance_analysis(model_wt, quote_args = "weights")
+\dontshow{\}) # examplesIf}
 }
 \references{
 \itemize{
diff --git a/man/model_parameters.default.Rd b/man/model_parameters.default.Rd
index 43dbc0039..7684b34a1 100644
--- a/man/model_parameters.default.Rd
+++ b/man/model_parameters.default.Rd
@@ -390,28 +390,25 @@ which is converted into a p-value using \code{\link[bayestestR:pd_to_p]{bayestes
 }
 
 \examples{
+\dontshow{if (require("boot", quietly = TRUE) && require("sandwich") && require("clubSandwich") && require("brglm2")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf}
 library(parameters)
 model <- lm(mpg ~ wt + cyl, data = mtcars)
 
 model_parameters(model)
 
 # bootstrapped parameters
-if (require("boot", quietly = TRUE)) {
-  model_parameters(model, bootstrap = TRUE)
-}
+model_parameters(model, bootstrap = TRUE)
 
 # standardized parameters
 model_parameters(model, standardize = "refit")
 
 # robust, heteroskedasticity-consistent standard errors
-if (require("sandwich") && require("clubSandwich")) {
-  model_parameters(model, vcov = "HC3")
+model_parameters(model, vcov = "HC3")
 
-  model_parameters(model,
-    vcov = "vcovCL",
-    vcov_args = list(cluster = mtcars$cyl)
-  )
-}
+model_parameters(model,
+  vcov = "vcovCL",
+  vcov_args = list(cluster = mtcars$cyl)
+)
 
 # different p-value style in output
 model_parameters(model, p_digits = 5)
@@ -425,16 +422,15 @@ model_parameters(model)
 model_parameters(model, exponentiate = TRUE)
 
 # bias-corrected logistic regression with penalized maximum likelihood
-if (require("brglm2")) {
-  model <- glm(
-    vs ~ wt + cyl,
-    data = mtcars,
-    family = "binomial",
-    method = "brglmFit"
-  )
-  model_parameters(model)
-}
+model <- glm(
+  vs ~ wt + cyl,
+  data = mtcars,
+  family = "binomial",
+  method = "brglmFit"
+)
+model_parameters(model)
 }
+\dontshow{\}) # examplesIf}
 }
 \seealso{
 \code{\link[insight:standardize_names]{insight::standardize_names()}} to
diff --git a/man/print.parameters_model.Rd b/man/print.parameters_model.Rd
index b66d83cd7..8df368867 100644
--- a/man/print.parameters_model.Rd
+++ b/man/print.parameters_model.Rd
@@ -22,7 +22,7 @@
   groups = NULL,
   column_width = NULL,
   ci_brackets = c("[", "]"),
-  add_reference = FALSE,
+  include_reference = FALSE,
   ...
 )
 
@@ -132,7 +132,7 @@ columns across all table components are adjusted to have the same width.}
 \item{ci_brackets}{Logical, if \code{TRUE} (default), CI-values are
 encompassed in square brackets (else in parentheses).}
 
-\item{add_reference}{Logical, if \code{TRUE}, the reference level of factors will
+\item{include_reference}{Logical, if \code{TRUE}, the reference level of factors will
 be added to the parameters table. This is only relevant for models with
 categorical predictors. The coefficient for the reference level is always
 \code{0} (except when \code{exponentiate = TRUE}, then the coefficient will be \code{1}),
diff --git a/tests/testthat/test-model_parameters.anova.R b/tests/testthat/test-model_parameters.anova.R
index a05a82c8d..9533031d4 100644
--- a/tests/testthat/test-model_parameters.anova.R
+++ b/tests/testthat/test-model_parameters.anova.R
@@ -8,7 +8,7 @@ test_that("model_parameters.anova", {
   a <- anova(m, test = "Chisq")
   mp <- model_parameters(a)
 
-  expect_equal(colnames(mp), c("Parameter", "df", "Deviance", "df_error", "Deviance_error", "p"))
+  expect_named(mp, c("Parameter", "df", "Deviance", "df_error", "Deviance_error", "p"))
   expect_equal(mp$Deviance_error, c(43.22973, 29.67517, 19.23255, 10.48692), tolerance = 1e-3)
   expect_equal(mp$p, c(NA, 0.00023, 0.00123, 0.01262), tolerance = 1e-3)
   expect_snapshot(mp)
@@ -18,7 +18,7 @@ test_that("model_parameters.anova", {
   skip_if_not_installed("car")
   a <- car::Anova(m, type = 3, test.statistic = "F")
   mp <- model_parameters(a)
-  expect_equal(colnames(mp), c("Parameter", "Sum_Squares", "df", "Mean_Square", "F", "p"))
+  expect_named(mp, c("Parameter", "Sum_Squares", "df", "Mean_Square", "F", "p"))
   expect_equal(mp[["F"]], c(53.40138, 60.42944, 13.96887, NA), tolerance = 1e-3)
 })
 
@@ -38,13 +38,13 @@ test_that("linear hypothesis tests", {
   expect_equal(p1, p2, ignore_attr = TRUE)
   expect_equal(p1, p3, ignore_attr = TRUE)
   expect_equal(p1, p4, ignore_attr = TRUE)
-  expect_equal(nrow(p1), 2)
-  expect_equal(p1$Parameter, c("(Intercept) = 0", "repwt = 1"))
+  expect_identical(nrow(p1), 2L)
+  expect_identical(p1$Parameter, c("(Intercept) = 0", "repwt = 1"))
 
   mod.duncan <- lm(prestige ~ income + education, data = Duncan)
   p <- parameters(car::linearHypothesis(mod.duncan, "1*income - 1*education + 1 = 1"))
-  expect_equal(nrow(p), 1)
-  expect_equal(p$Parameter, "income - education = 0")
+  expect_identical(nrow(p), 1L)
+  expect_identical(p$Parameter, "income - education = 0")
 })
 
 test_that("print-model_parameters", {
@@ -62,7 +62,7 @@ test_that("model_parameters_Anova.mlm", {
   a <- car::Anova(m, type = 3, test.statistic = "Pillai")
   mp <- model_parameters(a, verbose = FALSE)
 
-  expect_equal(colnames(mp), c("Parameter", "df", "Statistic", "df_num", "df_error", "F", "p"))
+  expect_named(mp, c("Parameter", "df", "Statistic", "df_num", "df_error", "F", "p"))
   expect_equal(mp[["F"]], c(158.2578, 6.60593, 3.71327, 3.28975), tolerance = 1e-3)
   expect_equal(mp$Statistic, c(0.9268, 0.67387, 0.22903, 0.4039), tolerance = 1e-3)
 })
@@ -75,7 +75,7 @@ test_that("model_parameters_Anova.mlm", {
   m <- MASS::polr(Sat ~ Infl + Type + Cont, weights = Freq, data = housing)
   a <- car::Anova(m)
   mp <- model_parameters(a)
-  expect_equal(colnames(mp), c("Parameter", "Chi2", "df", "p"))
+  expect_named(mp, c("Parameter", "Chi2", "df", "p"))
   expect_equal(mp$Chi2, c(108.2392, 55.91008, 14.30621), tolerance = 1e-3)
 })
 
@@ -118,16 +118,16 @@ test_that("anova type | lm", {
   m <- lm(mpg ~ factor(cyl) * hp + disp, mtcars)
 
   a1 <- aov(m)
-  expect_equal(attr(model_parameters(a1), "anova_type"), 1)
+  expect_identical(attr(model_parameters(a1), "anova_type"), 1)
 
   a1 <- anova(m)
-  expect_equal(attr(model_parameters(a1), "anova_type"), 1)
+  expect_identical(attr(model_parameters(a1), "anova_type"), 1)
 
   a2 <- car::Anova(m, type = 2)
   a3 <- car::Anova(m, type = 3)
-  expect_equal(attr(model_parameters(a2), "anova_type"), 2)
+  expect_identical(attr(model_parameters(a2), "anova_type"), 2)
   expect_message(
-    expect_equal(attr(model_parameters(a3), "anova_type"), 3),
+    expect_identical(attr(model_parameters(a3), "anova_type"), 3),
     "Type 3 ANOVAs only give"
   )
 
@@ -151,15 +151,15 @@ test_that("anova type | mlm", {
   m <- lm(cbind(mpg, drat) ~ factor(cyl) * hp + disp, mtcars)
 
   a1 <- aov(m)
-  expect_equal(attr(model_parameters(a1), "anova_type"), 1)
+  expect_identical(attr(model_parameters(a1), "anova_type"), 1)
 
   a1 <- anova(m)
-  expect_equal(attr(model_parameters(a1), "anova_type"), 1)
+  expect_identical(attr(model_parameters(a1), "anova_type"), 1)
 
   a2 <- car::Anova(m, type = 2)
   a3 <- car::Anova(m, type = 3)
-  expect_equal(attr(model_parameters(a2), "anova_type"), 2)
-  expect_equal(attr(model_parameters(a3, verbose = FALSE), "anova_type"), 3)
+  expect_identical(attr(model_parameters(a2), "anova_type"), 2)
+  expect_identical(attr(model_parameters(a3, verbose = FALSE), "anova_type"), 3)
 })
 
 test_that("anova type | glm", {
@@ -168,13 +168,13 @@ test_that("anova type | glm", {
   m <- suppressWarnings(glm(am ~ factor(cyl) * hp + disp, mtcars, family = binomial()))
 
   a1 <- anova(m)
-  expect_equal(attr(model_parameters(a1), "anova_type"), 1)
+  expect_identical(attr(model_parameters(a1), "anova_type"), 1)
 
   a2 <- suppressWarnings(car::Anova(m, type = 2))
   a3 <- suppressWarnings(car::Anova(m, type = 3))
-  expect_equal(attr(model_parameters(a2), "anova_type"), 2)
+  expect_identical(attr(model_parameters(a2), "anova_type"), 2)
   expect_message(
-    expect_equal(attr(model_parameters(a3), "anova_type"), 3),
+    expect_identical(attr(model_parameters(a3), "anova_type"), 3),
     "Type 3 ANOVAs only give"
   )
 })
@@ -185,37 +185,37 @@ test_that("anova type | lme4", {
   skip_if_not_installed("car")
 
   m1 <- lme4::lmer(mpg ~ factor(cyl) * hp + disp + (1 | gear), mtcars)
-  suppressMessages(
+  suppressMessages({
     m2 <- lme4::glmer(carb ~ factor(cyl) * hp + disp + (1 | gear), mtcars,
       family = poisson()
     )
-  )
+  })
 
   a1 <- anova(m1)
-  expect_equal(attr(model_parameters(a1), "anova_type"), 1)
+  expect_identical(attr(model_parameters(a1), "anova_type"), 1)
 
   a1 <- anova(m2)
-  expect_equal(attr(model_parameters(a1), "anova_type"), 1)
+  expect_identical(attr(model_parameters(a1), "anova_type"), 1)
 
   a3 <- anova(lmerTest::as_lmerModLmerTest(m1))
   expect_message(
-    expect_equal(attr(model_parameters(a3), "anova_type"), 3),
+    expect_identical(attr(model_parameters(a3), "anova_type"), 3),
     "Type 3 ANOVAs only give"
   )
 
   a2 <- car::Anova(m1, type = 2)
   a3 <- car::Anova(m1, type = 3)
-  expect_equal(attr(model_parameters(a2), "anova_type"), 2)
+  expect_identical(attr(model_parameters(a2), "anova_type"), 2)
   expect_message(
-    expect_equal(attr(model_parameters(a3), "anova_type"), 3),
+    expect_identical(attr(model_parameters(a3), "anova_type"), 3),
     "Type 3 ANOVAs only give"
   )
 
   a2 <- car::Anova(m2, type = 2)
   a3 <- car::Anova(m2, type = 3)
-  expect_equal(attr(model_parameters(a2), "anova_type"), 2)
+  expect_identical(attr(model_parameters(a2), "anova_type"), 2)
   expect_message(
-    expect_equal(attr(model_parameters(a3), "anova_type"), 3),
+    expect_identical(attr(model_parameters(a3), "anova_type"), 3),
     "Type 3 ANOVAs only give"
   )
 })
@@ -225,15 +225,15 @@ test_that("anova type | afex + Anova.mlm", {
 
   data(obk.long, package = "afex")
 
-  suppressMessages(
+  suppressMessages({
     m <- afex::aov_ez("id", "value", obk.long,
       between = c("treatment", "gender"),
       within = c("phase", "hour"), observed = "gender"
     )
-  )
+  })
 
-  expect_equal(attr(model_parameters(m), "anova_type"), 3)
-  expect_equal(attr(model_parameters(m$Anova, verbose = FALSE), "anova_type"), 3)
+  expect_identical(attr(model_parameters(m), "anova_type"), 3)
+  expect_identical(attr(model_parameters(m$Anova, verbose = FALSE), "anova_type"), 3)
 })
 
 test_that("anova rms", {
@@ -242,20 +242,21 @@ test_that("anova rms", {
   a <- anova(m)
   mp <- model_parameters(a)
 
-  expect_equal(attr(mp, "anova_type"), 2)
-  expect_equal(mp$Parameter, c("cyl", "disp", "hp", "drat", "Total", "Residuals"))
-  expect_equal(colnames(mp), c("Parameter", "Sum_Squares_Partial", "df", "Mean_Square", "F", "p"))
+  expect_identical(attr(mp, "anova_type"), 2)
+  expect_identical(mp$Parameter, c("cyl", "disp", "hp", "drat", "Total", "Residuals"))
+  expect_identical(colnames(mp), c("Parameter", "Sum_Squares_Partial", "df", "Mean_Square", "F", "p"))
   expect_equal(mp$Sum_Squares_Partial, data.frame(a)$Partial.SS, tolerance = 1e-3)
 })
 
 test_that("anova rms", {
   skip_if_not_installed("rms")
+  skip_if(getRversion() < "4.2.0")
   m <- rms::orm(mpg ~ cyl + disp + hp + drat, data = mtcars)
   a <- anova(m)
   mp <- model_parameters(a)
 
-  expect_equal(attr(mp, "anova_type"), 2)
-  expect_equal(mp$Parameter, c("cyl", "disp", "hp", "drat", "Total"))
-  expect_equal(colnames(mp), c("Parameter", "Chi2", "df", "p"))
+  expect_identical(attr(mp, "anova_type"), 2)
+  expect_identical(mp$Parameter, c("cyl", "disp", "hp", "drat", "Total"))
+  expect_named(mp, c("Parameter", "Chi2", "df", "p"))
   expect_equal(mp$Chi2, data.frame(a)$Chi.Square, tolerance = 1e-3)
 })
diff --git a/tests/testthat/test-printing_reference_level.R b/tests/testthat/test-printing_reference_level.R
index d847fcd4e..bec8507ea 100644
--- a/tests/testthat/test-printing_reference_level.R
+++ b/tests/testthat/test-printing_reference_level.R
@@ -5,7 +5,7 @@
 #   d <<- PlantGrowth
 #   m <- lm(weight ~ group, data = d)
 #   mp <- model_parameters(m)
-#   expect_snapshot(print(mp, add_reference = TRUE))
+#   expect_snapshot(print(mp, include_reference = TRUE))
 
 #   data(mtcars)
 #   d <<- mtcars
@@ -13,19 +13,19 @@
 #   d$am <- as.factor(d$am)
 #   m <- lm(mpg ~ hp + cyl + gear + am, data = d)
 #   mp <- model_parameters(m)
-#   expect_snapshot(print(mp, add_reference = TRUE))
+#   expect_snapshot(print(mp, include_reference = TRUE))
 
 #   data(iris)
 #   d <<- iris
 #   m <- lm(Sepal.Length ~ Sepal.Width * Species, data = d)
 #   mp <- model_parameters(m)
-#   expect_snapshot(print(mp, add_reference = TRUE))
+#   expect_snapshot(print(mp, include_reference = TRUE))
 
 #   data(mtcars)
 #   d <<- mtcars
 #   d$gear <- as.factor(d$gear)
 #   m <- glm(vs ~ wt + gear, data = d, family = "binomial")
-#   expect_snapshot(print(model_parameters(m, exponentiate = TRUE, drop = "(Intercept)"), add_reference = TRUE))
+#   expect_snapshot(print(model_parameters(m, exponentiate = TRUE, drop = "(Intercept)"), include_reference = TRUE))
 # })
 
 # test_that("reference for models with multiple components", {
@@ -40,5 +40,5 @@
 #     family = glmmTMB::truncated_poisson()
 #   )
 
-#   print(model_parameters(m1), add_reference = TRUE)
+#   print(model_parameters(m1), include_reference = TRUE)
 # })
diff --git a/tests/testthat/test-quantreg.R b/tests/testthat/test-quantreg.R
index 4fba67d15..2fa1ea166 100644
--- a/tests/testthat/test-quantreg.R
+++ b/tests/testthat/test-quantreg.R
@@ -1,4 +1,5 @@
 skip_on_cran()
+skip_if(getRversion() < "4.2.0")
 
 # rqss ---------
 
@@ -20,7 +21,7 @@ skip_on_cran()
 test_that("mp_rq", {
   skip_if_not_installed("quantreg")
   data(stackloss)
-  m1 <- quantreg::rq(stack.loss ~ Air.Flow + Water.Temp, data = stackloss, tau = .25)
+  m1 <- quantreg::rq(stack.loss ~ Air.Flow + Water.Temp, data = stackloss, tau = 0.25)
 
   mp <- suppressWarnings(model_parameters(m1))
   expect_identical(mp$Parameter, c("(Intercept)", "Air.Flow", "Water.Temp"))