diff --git a/DESCRIPTION b/DESCRIPTION
index 89c9a9d57..b1ce379bf 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,7 +1,7 @@
 Type: Package
 Package: performance
 Title: Assessment of Regression Models Performance
-Version: 0.12.4.6
+Version: 0.12.4.7
 Authors@R:
     c(person(given = "Daniel",
              family = "Lüdecke",
diff --git a/NEWS.md b/NEWS.md
index 9eb5ded26..1bddce2ce 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -4,6 +4,11 @@
 
 * Deprecated arguments and alias-function-names have been removed.
 
+* Argument names in `check_model()` that refer to plot-aesthetics (like
+  `dot_size`) are now harmonized across *easystats* packages, meaning that
+  these have been renamed. They now follow the pattern `aesthetic_type`, e.g.
+  `size_dot` (instead of `dot_size`).
+
 ## Changes
 
 * Increased accuracy for `check_convergence()` for *glmmTMB* models.
diff --git a/R/check_model.R b/R/check_model.R
index baa224feb..11562a351 100644
--- a/R/check_model.R
+++ b/R/check_model.R
@@ -10,8 +10,8 @@
 #' get hints about possible problems.
 #'
 #' @param x A model object.
-#' @param dot_size,line_size Size of line and dot-geoms.
-#' @param base_size,title_size,axis_title_size Base font size for axis and plot titles.
+#' @param size_dot,size_line Size of line and dot-geoms.
+#' @param base_size,size_title,size_axis_title Base font size for axis and plot titles.
 #' @param panel Logical, if `TRUE`, plots are arranged as panels; else,
 #' single plots for each diagnostic are returned.
 #' @param check Character vector, indicating which checks for should be performed
@@ -23,7 +23,7 @@
 #' `"linearity"`, and checks for non-constant variance, i.e. for
 #' heteroscedasticity, as well as the linear relationship. By default, all
 #' possible checks are performed and plotted.
-#' @param alpha,dot_alpha The alpha level of the confidence bands and dot-geoms.
+#' @param alpha,alpha_dot The alpha level of the confidence bands and dot-geoms.
 #' Scalar from 0 to 1.
 #' @param colors Character vector with color codes (hex-format). Must be of
 #' length 3. First color is usually used for reference lines, second color
@@ -196,13 +196,13 @@ check_model.default <- function(x,
                                 type = "density",
                                 residual_type = NULL,
                                 show_dots = NULL,
-                                dot_size = 2,
-                                line_size = 0.8,
-                                title_size = 12,
-                                axis_title_size = base_size,
+                                size_dot = 2,
+                                size_line = 0.8,
+                                size_title = 12,
+                                size_axis_title = base_size,
                                 base_size = 10,
                                 alpha = 0.2,
-                                dot_alpha = 0.8,
+                                alpha_dot = 0.8,
                                 colors = c("#3aaf85", "#1b6ca8", "#cd201f"),
                                 theme = "see::theme_lucid",
                                 verbose = FALSE,
@@ -277,14 +277,14 @@ check_model.default <- function(x,
   }
 
   attr(assumptions_data, "panel") <- panel
-  attr(assumptions_data, "dot_size") <- dot_size
-  attr(assumptions_data, "line_size") <- line_size
+  attr(assumptions_data, "dot_size") <- size_dot
+  attr(assumptions_data, "line_size") <- size_line
   attr(assumptions_data, "base_size") <- base_size
-  attr(assumptions_data, "axis_title_size") <- axis_title_size
-  attr(assumptions_data, "title_size") <- title_size
+  attr(assumptions_data, "axis_title_size") <- size_axis_title
+  attr(assumptions_data, "title_size") <- size_title
   attr(assumptions_data, "check") <- check
   attr(assumptions_data, "alpha") <- alpha
-  attr(assumptions_data, "dot_alpha") <- dot_alpha
+  attr(assumptions_data, "dot_alpha") <- alpha_dot
   attr(assumptions_data, "show_dots") <- isTRUE(show_dots)
   attr(assumptions_data, "detrend") <- detrend
   attr(assumptions_data, "colors") <- colors
@@ -328,28 +328,28 @@ check_model.stanreg <- function(x,
                                 type = "density",
                                 residual_type = NULL,
                                 show_dots = NULL,
-                                dot_size = 2,
-                                line_size = 0.8,
-                                title_size = 12,
-                                axis_title_size = base_size,
+                                size_dot = 2,
+                                size_line = 0.8,
+                                size_title = 12,
+                                size_axis_title = base_size,
                                 base_size = 10,
                                 alpha = 0.2,
-                                dot_alpha = 0.8,
+                                alpha_dot = 0.8,
                                 colors = c("#3aaf85", "#1b6ca8", "#cd201f"),
                                 theme = "see::theme_lucid",
                                 verbose = FALSE,
                                 ...) {
   check_model(bayestestR::bayesian_as_frequentist(x),
-    dot_size = dot_size,
-    line_size = line_size,
+    size_dot = size_dot,
+    size_line = size_line,
     panel = panel,
     check = check,
     alpha = alpha,
-    dot_alpha = dot_alpha,
+    alpha_dot = alpha_dot,
     colors = colors,
     theme = theme,
     base_size = base_size,
-    axis_title_size = axis_title_size,
+    size_axis_title = size_axis_title,
     detrend = detrend,
     show_dots = show_dots,
     bandwidth = bandwidth,
@@ -374,26 +374,26 @@ check_model.model_fit <- function(x,
                                   type = "density",
                                   residual_type = NULL,
                                   show_dots = NULL,
-                                  dot_size = 2,
-                                  line_size = 0.8,
-                                  title_size = 12,
-                                  axis_title_size = base_size,
+                                  size_dot = 2,
+                                  size_line = 0.8,
+                                  size_title = 12,
+                                  size_axis_title = base_size,
                                   base_size = 10,
                                   alpha = 0.2,
-                                  dot_alpha = 0.8,
+                                  alpha_dot = 0.8,
                                   colors = c("#3aaf85", "#1b6ca8", "#cd201f"),
                                   theme = "see::theme_lucid",
                                   verbose = FALSE,
                                   ...) {
   check_model(
     x$fit,
-    dot_size = dot_size,
-    line_size = line_size,
+    size_dot = size_dot,
+    size_line = size_line,
     panel = panel,
     check = check,
     alpha = alpha,
-    axis_title_size = axis_title_size,
-    dot_alpha = dot_alpha,
+    size_axis_title = size_axis_title,
+    alpha_dot = alpha_dot,
     colors = colors,
     theme = theme,
     base_size = base_size,
@@ -417,26 +417,26 @@ check_model.performance_simres <- function(x,
                                            type = "density",
                                            residual_type = NULL,
                                            show_dots = NULL,
-                                           dot_size = 2,
-                                           line_size = 0.8,
-                                           title_size = 12,
-                                           axis_title_size = base_size,
+                                           size_dot = 2,
+                                           size_line = 0.8,
+                                           size_title = 12,
+                                           size_axis_title = base_size,
                                            base_size = 10,
                                            alpha = 0.2,
-                                           dot_alpha = 0.8,
+                                           alpha_dot = 0.8,
                                            colors = c("#3aaf85", "#1b6ca8", "#cd201f"),
                                            theme = "see::theme_lucid",
                                            verbose = FALSE,
                                            ...) {
   check_model(
     x$fittedModel,
-    dot_size = dot_size,
-    line_size = line_size,
+    size_dot = size_dot,
+    size_line = size_line,
     panel = panel,
     check = check,
     alpha = alpha,
-    dot_alpha = dot_alpha,
-    axis_title_size = axis_title_size,
+    alpha_dot = alpha_dot,
+    size_axis_title = size_axis_title,
     colors = colors,
     theme = theme,
     base_size = base_size,
@@ -462,35 +462,35 @@ check_model.DHARMa <- check_model.performance_simres
 
   # multicollinearity --------------
   if (any(c("all", "vif") %in% check)) {
-    dat$VIF <- .diag_vif(model, verbose = verbose)
+    dat$VIF <- .model_diagnostic_vif(model, verbose = verbose)
   }
 
   # Q-Q plot (normality/uniformity of residuals) --------------
   if (any(c("all", "qq") %in% check)) {
     dat$QQ <- switch(residual_type,
       simulated = .safe(simulate_residuals(model, ...)),
-      .diag_qq(model, model_info = model_info, verbose = verbose)
+      .model_diagnostic_qq(model, model_info = model_info, verbose = verbose)
     )
   }
 
   # Random Effects Q-Q plot (normality of BLUPs) --------------
   if (any(c("all", "reqq") %in% check)) {
-    dat$REQQ <- .diag_reqq(model, level = 0.95, model_info = model_info, verbose = verbose)
+    dat$REQQ <- .model_diagnostic_ranef_qq(model, level = 0.95, model_info = model_info, verbose = verbose)
   }
 
   # normal-curve plot (normality of residuals) --------------
   if (any(c("all", "normality") %in% check)) {
-    dat$NORM <- .diag_norm(model, verbose = verbose)
+    dat$NORM <- .model_diagnostic_normality(model, verbose = verbose)
   }
 
   # non-constant variance (heteroskedasticity, liniearity) --------------
   if (any(c("all", "ncv", "linearity") %in% check)) {
-    dat$NCV <- .diag_ncv(model, verbose = verbose)
+    dat$NCV <- .model_diagnostic_ncv(model, verbose = verbose)
   }
 
   # homogeneity of variance --------------
   if (any(c("all", "homogeneity") %in% check)) {
-    dat$HOMOGENEITY <- .diag_homogeneity(model, verbose = verbose)
+    dat$HOMOGENEITY <- .model_diagnostic_homogeneity(model, verbose = verbose)
   }
 
   # outliers --------------
@@ -501,7 +501,7 @@ check_model.DHARMa <- check_model.performance_simres
     } else {
       threshold <- attributes(dat$OUTLIERS)$threshold$cook
     }
-    dat$INFLUENTIAL <- .influential_obs(model, threshold = threshold)
+    dat$INFLUENTIAL <- .safe(.model_diagnostic_outlier(model, threshold = threshold))
   }
 
   # posterior predictive checks --------------
@@ -523,25 +523,25 @@ check_model.DHARMa <- check_model.performance_simres
 
   # multicollinearity --------------
   if (any(c("all", "vif") %in% check)) {
-    dat$VIF <- .diag_vif(model, verbose = verbose)
+    dat$VIF <- .model_diagnostic_vif(model, verbose = verbose)
   }
 
   # Q-Q plot (normality/uniformity of residuals) --------------
   if (any(c("all", "qq") %in% check)) {
     dat$QQ <- switch(residual_type,
       simulated = .safe(simulate_residuals(model, ...)),
-      .diag_qq(model, model_info = model_info, verbose = verbose)
+      .model_diagnostic_qq(model, model_info = model_info, verbose = verbose)
     )
   }
 
   # homogeneity of variance --------------
   if (any(c("all", "homogeneity") %in% check)) {
-    dat$HOMOGENEITY <- .diag_homogeneity(model, verbose = verbose)
+    dat$HOMOGENEITY <- .model_diagnostic_homogeneity(model, verbose = verbose)
   }
 
   # Random Effects Q-Q plot (normality of BLUPs) --------------
   if (any(c("all", "reqq") %in% check)) {
-    dat$REQQ <- .diag_reqq(model, level = 0.95, model_info = model_info, verbose = verbose)
+    dat$REQQ <- .model_diagnostic_ranef_qq(model, level = 0.95, model_info = model_info, verbose = verbose)
   }
 
   # outliers --------------
@@ -552,7 +552,7 @@ check_model.DHARMa <- check_model.performance_simres
     } else {
       threshold <- attributes(dat$OUTLIERS)$threshold$cook
     }
-    dat$INFLUENTIAL <- .influential_obs(model, threshold = threshold)
+    dat$INFLUENTIAL <- .safe(.model_diagnostic_outlier(model, threshold = threshold))
   }
 
   # posterior predictive checks --------------
@@ -567,7 +567,7 @@ check_model.DHARMa <- check_model.performance_simres
 
   # misspecified dispersion and zero-inflation --------------
   if (isTRUE(model_info$is_count) && any(c("all", "overdispersion") %in% check)) {
-    dat$OVERDISPERSION <- .diag_overdispersion(model)
+    dat$OVERDISPERSION <- .model_diagnostic_overdispersion(model)
   }
 
   dat <- insight::compact_list(dat)
diff --git a/R/check_model_diagnostics.R b/R/check_model_diagnostics.R
index 8897c685e..9595ed968 100644
--- a/R/check_model_diagnostics.R
+++ b/R/check_model_diagnostics.R
@@ -1,6 +1,6 @@
 # prepare data for VIF plot ----------------------------------
 
-.diag_vif <- function(model, verbose = TRUE) {
+.model_diagnostic_vif <- function(model, verbose = TRUE) {
   out <- check_collinearity(model, verbose = verbose)
   dat <- insight::compact_list(out)
   if (is.null(dat)) {
@@ -35,7 +35,7 @@
 
 # prepare data for QQ plot ----------------------------------
 
-.diag_qq <- function(model, model_info = NULL, verbose = TRUE) {
+.model_diagnostic_qq <- function(model, model_info = NULL, verbose = TRUE) {
   if (inherits(model, c("lme", "lmerMod", "merMod", "gam"))) {
     res_ <- stats::residuals(model)
   } else if (inherits(model, "geeglm")) {
@@ -98,7 +98,7 @@
 
 # prepare data for random effects QQ plot ----------------------------------
 
-.diag_reqq <- function(model, level = 0.95, model_info = NULL, verbose = TRUE) {
+.model_diagnostic_ranef_qq <- function(model, level = 0.95, model_info = NULL, verbose = TRUE) {
   # check if we have mixed model
   if (is.null(model_info) || !model_info$is_mixed) {
     return(NULL)
@@ -161,7 +161,7 @@
 
 # prepare data for normality of residuals plot ----------------------------------
 
-.diag_norm <- function(model, verbose = TRUE) {
+.model_diagnostic_normality <- function(model, verbose = TRUE) {
   r <- try(as.numeric(stats::residuals(model)), silent = TRUE)
 
   if (inherits(r, "try-error")) {
@@ -181,7 +181,7 @@
 
 # prepare data for influential obs plot ----------------------------------
 
-.diag_influential_obs <- function(model, threshold = NULL) {
+.model_diagnostic_outlier <- function(model, threshold = NULL) {
   s <- summary(model)
 
   if (inherits(model, "lm", which = TRUE) == 1) {
@@ -220,7 +220,7 @@
 
 # prepare data for non-constant variance plot ----------------------------------
 
-.diag_ncv <- function(model, verbose = TRUE) {
+.model_diagnostic_ncv <- function(model, verbose = TRUE) {
   ncv <- tryCatch(
     data.frame(
       x = as.numeric(stats::fitted(model)),
@@ -248,7 +248,7 @@
 
 # prepare data for homogeneity of variance plot ----------------------------------
 
-.diag_homogeneity <- function(model, verbose = TRUE) {
+.model_diagnostic_homogeneity <- function(model, verbose = TRUE) {
   faminfo <- insight::model_info(model)
   r <- tryCatch(
     if (inherits(model, "merMod")) {
@@ -367,7 +367,7 @@
 
 
 
-.diag_overdispersion <- function(model, ...) {
+.model_diagnostic_overdispersion <- function(model, ...) {
   faminfo <- insight::model_info(model)
 
   # data for poisson models
diff --git a/R/check_normality.R b/R/check_normality.R
index 7ea6970ec..ffd4ba824 100644
--- a/R/check_normality.R
+++ b/R/check_normality.R
@@ -231,7 +231,7 @@ check_normality.merMod <- function(x, effects = c("fixed", "random"), ...) {
           p.val <- c(p.val, .check_normality(re[[i]][[j]], x, "random effects"))
         }
       }
-      attr(p.val, "re_qq") <- .diag_reqq(x, level = 0.95, model_info = info)
+      attr(p.val, "re_qq") <- .model_diagnostic_ranef_qq(x, level = 0.95, model_info = info)
       attr(p.val, "type") <- "random effects"
       attr(p.val, "re_groups") <- re_groups
     }
diff --git a/R/check_outliers.R b/R/check_outliers.R
index 071bd39a3..7729f9931 100644
--- a/R/check_outliers.R
+++ b/R/check_outliers.R
@@ -588,7 +588,7 @@ check_outliers.default <- function(x,
   attr(outlier, "threshold") <- thresholds
   attr(outlier, "method") <- method
   attr(outlier, "text_size") <- 3
-  attr(outlier, "influential_obs") <- .influential_obs(x, threshold = unlist(thresholds))
+  attr(outlier, "influential_obs") <- .safe(.model_diagnostic_outlier(x, threshold = unlist(thresholds))) # nolint
   attr(outlier, "variables") <- "(Whole model)"
   attr(outlier, "raw_data") <- my_data
   attr(outlier, "outlier_var") <- outlier_var
@@ -2036,14 +2036,6 @@ check_outliers.DHARMa <- check_outliers.performance_simres
 
 
 
-# influential observations data --------
-
-.influential_obs <- function(x, threshold = NULL) {
-  .safe(.diag_influential_obs(x, threshold = threshold))
-}
-
-
-
 # Non-supported model classes ---------------------------------------
 
 #' @export
diff --git a/R/check_overdispersion.R b/R/check_overdispersion.R
index 0d877b0ad..fc8e25e32 100644
--- a/R/check_overdispersion.R
+++ b/R/check_overdispersion.R
@@ -101,7 +101,7 @@ plot.check_overdisp <- function(x, ...) {
     }
   }
   if (!is.null(model)) {
-    x <- .diag_overdispersion(model)
+    x <- .model_diagnostic_overdispersion(model)
     class(x) <- c("see_check_overdisp", "data.frame")
     attr(x, "colors") <- list(...)$colors
     attr(x, "line_size") <- list(...)$size_line
diff --git a/man/check_model.Rd b/man/check_model.Rd
index c07add5f2..5d06a7192 100644
--- a/man/check_model.Rd
+++ b/man/check_model.Rd
@@ -16,13 +16,13 @@ check_model(x, ...)
   type = "density",
   residual_type = NULL,
   show_dots = NULL,
-  dot_size = 2,
-  line_size = 0.8,
-  title_size = 12,
-  axis_title_size = base_size,
+  size_dot = 2,
+  size_line = 0.8,
+  size_title = 12,
+  size_axis_title = base_size,
   base_size = 10,
   alpha = 0.2,
-  dot_alpha = 0.8,
+  alpha_dot = 0.8,
   colors = c("#3aaf85", "#1b6ca8", "#cd201f"),
   theme = "see::theme_lucid",
   verbose = FALSE,
@@ -79,11 +79,11 @@ time-consuming. By default, \code{show_dots = NULL}. In this case \code{check_mo
 tries to guess whether performance will be poor due to a very large model
 and thus automatically shows or hides dots.}
 
-\item{dot_size, line_size}{Size of line and dot-geoms.}
+\item{size_dot, size_line}{Size of line and dot-geoms.}
 
-\item{base_size, title_size, axis_title_size}{Base font size for axis and plot titles.}
+\item{base_size, size_title, size_axis_title}{Base font size for axis and plot titles.}
 
-\item{alpha, dot_alpha}{The alpha level of the confidence bands and dot-geoms.
+\item{alpha, alpha_dot}{The alpha level of the confidence bands and dot-geoms.
 Scalar from 0 to 1.}
 
 \item{colors}{Character vector with color codes (hex-format). Must be of
diff --git a/vignettes/check_model_practical.Rmd b/vignettes/check_model_practical.Rmd
index 53ca7ab58..5b6673910 100644
--- a/vignettes/check_model_practical.Rmd
+++ b/vignettes/check_model_practical.Rmd
@@ -1,6 +1,6 @@
 ---
 title: "How to arrive at the best model fit"
-output: 
+output:
   rmarkdown::html_vignette:
     toc: true
 tags: [r, performance]
@@ -8,7 +8,7 @@ vignette: >
   \usepackage[utf8]{inputenc}
   %\VignetteIndexEntry{How to arrive at the best model fit}
   %\VignetteEngine{knitr::rmarkdown}
-editor_options: 
+editor_options:
   chunk_output_type: console
 ---
 
@@ -82,7 +82,7 @@ In *performance*, we can conduct a comprehensive visual inspection of our model
 For now, we want to focus on the _posterior predictive checks_, _dispersion and zero-inflation_ as well as the Q-Q plot (_uniformity of residuals_).
 
 ```{r fig.height=12, fig.width=10}
-check_model(model1, dot_size = 1.2)
+check_model(model1, size_dot = 1.2)
 ```
 
 Note that unlike `plot()`, which is a base R function to create diagnostic plots, `check_model()` relies on *simulated residuals* for the Q-Q plot, which is more accurate for non-Gaussian models. See [this vignette](https://easystats.github.io/performance/articles/simulate_residuals.html) and the documentation of `simulate_residuals()` for further details.
@@ -108,7 +108,7 @@ model2 <- glmmTMB::glmmTMB(
   family = poisson,
   data = glmmTMB::Salamanders
 )
-check_model(model2, dot_size = 1.2)
+check_model(model2, size_dot = 1.2)
 ```
 
 Looking at the above plots, the zero-inflation seems to be addressed properly (see especially _posterior predictive checks_ and _uniformity of residuals_, the Q-Q plot). However, the overdispersion still could be present. We can check for these problems using `check_overdispersion()` and `check_zeroinflation()` again.
@@ -132,7 +132,7 @@ model3 <- glmmTMB::glmmTMB(
   family = glmmTMB::nbinom1,
   data = glmmTMB::Salamanders
 )
-check_model(model3, dot_size = 1.2)
+check_model(model3, size_dot = 1.2)
 ```
 
 Now we see that the plot showing _misspecified dispersion and zero-inflation_ suggests that the overdispersion is better addressed than before. Let us check again:
@@ -195,7 +195,7 @@ model4 <- glmmTMB::glmmTMB(
   family = glmmTMB::nbinom2,
   data = glmmTMB::Salamanders
 )
-check_model(model4, dot_size = 1.2)
+check_model(model4, size_dot = 1.2)
 
 check_overdispersion(model4)
 
diff --git a/vignettes/simulate_residuals.Rmd b/vignettes/simulate_residuals.Rmd
index 89f826ba6..8842205a8 100644
--- a/vignettes/simulate_residuals.Rmd
+++ b/vignettes/simulate_residuals.Rmd
@@ -1,6 +1,6 @@
 ---
 title: "Checking simulated residuals"
-output: 
+output:
   rmarkdown::html_vignette:
     toc: true
 tags: [r, performance]
@@ -8,7 +8,7 @@ vignette: >
   \usepackage[utf8]{inputenc}
   %\VignetteIndexEntry{Checking simulated residuals}
   %\VignetteEngine{knitr::rmarkdown}
-editor_options: 
+editor_options:
   chunk_output_type: console
 ---
 
@@ -93,10 +93,10 @@ check_outliers(simulated_residuals)
 
 The above three functions internally call `simulate_residuals()` for more complex models automatically, so you don't need to call `simulate_residuals()` yourself. Simulated residuals are usually more reliable than the standard residuals, especially for complex models.
 
-Finally, you can even perform a visual check for the entire model, either by passing the model object directly, or the object returned from `simulate_residuals()`. 
+Finally, you can even perform a visual check for the entire model, either by passing the model object directly, or the object returned from `simulate_residuals()`.
 
 ```{r fig.height=12, fig.width=10}
-check_model(simulated_residuals, dot_size = 1.5)
+check_model(simulated_residuals, size_dot = 1.5)
 ```
 
 The `check_model()` function is the main reason we don't want to prematurely extract the residuals in `simulate_residuals()`, because if we do then the simulated residual won't contain the model fit (`fittedModel` in the output below), so we won't be able to do all of the checks we would want to do using the model (e.g., posterior predictive checks).