diff --git a/DESCRIPTION b/DESCRIPTION
index 6898ecf90..083fc3e17 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,7 +1,7 @@
 Type: Package
 Package: modelbased
 Title: Estimation of Model-Based Predictions, Contrasts and Means
-Version: 0.16.0.6
+Version: 0.16.0.7
 Authors@R:
     c(person(given = "Dominique",
              family = "Makowski",
diff --git a/R/tinyplot.R b/R/tinyplot.R
index 1b9f2e678..bb3fbe00d 100644
--- a/R/tinyplot.R
+++ b/R/tinyplot.R
@@ -5,6 +5,25 @@
 #' @param dodge Dodge value for grouped plots. If `NULL` (the default), then
 #' the dodging behavior is determined by the number of groups and
 #' `getOption("modelbased_tinyplot_dodge")`.
+#' @param size_title,size_axis_title,size_axis_text Numeric, set the size of
+#' plot title, axis title or axis labels. If not `NULL`, `par()` is called
+#' temporarily to set `cex.main`, `cex.axis` and `cex.lab`. The original values
+#' are restored afterwards. The default size is `1`. Larger values increase text
+#' sizes and vice versa.
+#' @param size_point,size_line Size of points and lines in the plot. Default is
+#' `1`. Larger values increase point/line sizes and vice versa. If argument
+#' `cex` is used, `size_point` will be ignored. Same for argument `lwd`, which
+#' overrides `size_line`.
+#' @param colors Colors or color palette used for plotting. Following options
+#' are allowed:
+#' - A string corresponding to one of the many palettes listed by either
+#'   `palette.pals()` or `hcl.pals()`.
+#' - The `palette.colors()` function, e.g.
+#'   `palette.colors(palette = "Okabe-Ito", alpha = 0.5)`.
+#' - A vector or list of colours, e.g. `c("darkorange", "purple", "cyan4")`. If
+#'   too few colours are provided, they will be recycled (for discrete palettes)
+#'   or a gradient palette will be interpolated for continuous palettes.
+#' If the `palette` argument is used, `colors` will be ignored.
 #' @param ... Other arguments passed to \code{\link[tinyplot]{tinyplot}}.
 #'
 #' @examplesIf all(insight::check_if_installed(c("tinyplot", "marginaleffects"), quietly = TRUE))
@@ -43,25 +62,38 @@
 #' # Reset to default theme
 #' tinytheme()
 #'
-#' # facets, grids, legends
+#' # facets
+#' # ----------------------
+#' # when using facets, the `tinyplot()` method checks if the legend is
+#' # redundant (because it already appears in the facets), and if so, it
+#' # removes the legend. Set `legend = TRUE` to add it back.
+#'
 #' data(efc, package = "modelbased")
+#' # convert to factors, assign labels. we use datawizard::to_factor() in
+#' # the second row to automatically assign value labels as factor levels.
+#' # because labels are too long for `c172code`, we assign new labels using
+#' # `as.factor()`
 #' efc$c172code <- factor(efc$c172code, labels = c("low", "mid", "high"))
+#' efc$e42dep <- datawizard::to_factor(efc$e42dep)
+#' # fit model
 #' m <- lm(neg_c_7 ~ c172code * e42dep, data = efc)
 #' em <- estimate_means(m, c("c172code", "e42dep"))
 #'
 #' # for facets, it can be useful to remove dodging
 #' plt(em, facet = ~e42dep, dodge = 0, theme = "float")
 #'
-#' # remove x-axis limits adjustments with `xlim`, remove legend
+#' # remove x-axis limits adjustments with `xlim`
 #' plt(
 #'   em,
 #'   facet = ~e42dep,
 #'   dodge = 0,
 #'   theme = "float",
 #'   xlim = c(1, 3),
-#'   grid = TRUE,
-#'   legend = FALSE
+#'   grid = TRUE
 #' )
+#'
+#' # add back legend
+#' plt(em, facet = ~e42dep, legend = TRUE)
 #' }
 #' @exportS3Method tinyplot::tinyplot
 tinyplot.estimate_means <- function(
@@ -71,6 +103,12 @@ tinyplot.estimate_means <- function(
   show_data = FALSE,
   collapse_group = NULL,
   numeric_as_discrete = NULL,
+  colors = NULL,
+  size_title = NULL,
+  size_axis_title = NULL,
+  size_axis_text = NULL,
+  size_point = NULL,
+  size_line = NULL,
   ...
 ) {
   insight::check_if_installed("tinyplot")
@@ -146,14 +184,6 @@ tinyplot.estimate_means <- function(
     dots$frame <- FALSE
   }
 
-  # move geoms on x-axis closer together
-  if (is.null(dots$xlim) && !is.numeric(data[[aes$x]])) {
-    n_categories <- insight::n_unique(data[[aes$x]])
-    if (!is.null(n_categories)) {
-      dots$xlim <- c(0.5, n_categories + 0.5)
-    }
-  }
-
   # add remaining aesthetics to the plot description as symbols
   elements <- c("xmin", "xmax", "ymin", "ymax")
   plot_args <- lapply(elements, function(el) {
@@ -164,6 +194,26 @@ tinyplot.estimate_means <- function(
   })
   names(plot_args) <- elements
 
+  # margins -------------------------------
+
+  # move geoms on x-axis closer together
+  if (is.null(dots$xlim) && !is.numeric(data[[aes$x]])) {
+    n_categories <- insight::n_unique(data[[aes$x]])
+    if (!is.null(n_categories)) {
+      dots$xlim <- c(0.5, n_categories + 0.5)
+    }
+  }
+
+  # geom sizes -------------------------------
+
+  if (is.null(dots$cex) && !is.null(size_point)) {
+    dots$cex <- size_point
+  }
+
+  if (is.null(dots$lwd) && !is.null(size_line)) {
+    dots$lwd <- size_line
+  }
+
   # dodging -------------------------------
 
   # Set dodge value for grouped point or pointrange plots.
@@ -188,11 +238,27 @@ tinyplot.estimate_means <- function(
   dots$xlab <- aes$labs$x
   dots$ylab <- aes$labs$y
 
+  # color palette --------------------------------
+
+  if (is.null(dots$palette) && !is.null(colors)) {
+    dots$palette <- colors
+  }
+
   # legend labels --------------------------------
 
   # we also need to account for custom legend options passed through dots
   if (is.null(dots$legend)) {
-    dots$legend <- list(title = aes$labs$colour)
+    # check if legend is already in facets - if so, we don't need a legend
+    if (
+      !is.null(dots$facet) &&
+        !is.null(aes$color) &&
+        length(all.vars(dots$facet)) > 0 &&
+        all(all.vars(dots$facet) %in% aes$color)
+    ) {
+      dots$legend <- FALSE
+    } else {
+      dots$legend <- list(title = aes$labs$colour)
+    }
   } else if (inherits(dots$legend, "list")) {
     if (!("title" %in% names(dots$legend))) {
       dots$legend <- utils::modifyList(
@@ -253,6 +319,18 @@ tinyplot.estimate_means <- function(
     }
   }
 
+  # set text sizes --------------------------------
+  text_sizes <- insight::compact_list(list(
+    cex.axis = size_axis_text,
+    cex.main = size_title,
+    cex.lab = size_axis_title
+  ))
+
+  if (!is.null(text_sizes) && length(text_sizes)) {
+    old_pars <- graphics::par(text_sizes)
+    on.exit(graphics::par(old_pars), add = TRUE)
+  }
+
   # plot it!
   suppressWarnings(do.call(tinyplot::tinyplot, plot_args))
 }
diff --git a/R/visualisation_recipe.R b/R/visualisation_recipe.R
index d90f54383..e712c8008 100644
--- a/R/visualisation_recipe.R
+++ b/R/visualisation_recipe.R
@@ -70,7 +70,9 @@
 #'
 #' @return An object of class `visualisation_recipe` that describes the layers
 #' used to create a plot based on `{ggplot2}`. The related `plot()` method is in
-#' the `{see}` package.
+#' the `{see}` package. For `tinyplot()` (or its alias `plt()`), a base graphics
+#' R plot is created, which can be modified further using classic R graphics
+#' code.
 #'
 #' @section Global Options to Customize Plots:
 #' Some arguments for `plot()` can get global defaults using `options()`:
diff --git a/man/visualisation_recipe.estimate_predicted.Rd b/man/visualisation_recipe.estimate_predicted.Rd
index e5d536f8c..4b5351816 100644
--- a/man/visualisation_recipe.estimate_predicted.Rd
+++ b/man/visualisation_recipe.estimate_predicted.Rd
@@ -20,6 +20,12 @@
   show_data = FALSE,
   collapse_group = NULL,
   numeric_as_discrete = NULL,
+  colors = NULL,
+  size_title = NULL,
+  size_axis_title = NULL,
+  size_axis_text = NULL,
+  size_point = NULL,
+  size_line = NULL,
   ...
 )
 
@@ -96,6 +102,30 @@ predictor. Use \code{FALSE} to always use continuous color scales for numeric
 predictors. It is possible to set a global default value using \code{options()},
 e.g. \code{options(modelbased_numeric_as_discrete = 10)}.}
 
+\item{colors}{Colors or color palette used for plotting. Following options
+are allowed:
+\itemize{
+\item A string corresponding to one of the many palettes listed by either
+\code{palette.pals()} or \code{hcl.pals()}.
+\item The \code{palette.colors()} function, e.g.
+\code{palette.colors(palette = "Okabe-Ito", alpha = 0.5)}.
+\item A vector or list of colours, e.g. \code{c("darkorange", "purple", "cyan4")}. If
+too few colours are provided, they will be recycled (for discrete palettes)
+or a gradient palette will be interpolated for continuous palettes.
+If the \code{palette} argument is used, \code{colors} will be ignored.
+}}
+
+\item{size_title, size_axis_title, size_axis_text}{Numeric, set the size of
+plot title, axis title or axis labels. If not \code{NULL}, \code{par()} is called
+temporarily to set \code{cex.main}, \code{cex.axis} and \code{cex.lab}. The original values
+are restored afterwards. The default size is \code{1}. Larger values increase text
+sizes and vice versa.}
+
+\item{size_point, size_line}{Size of points and lines in the plot. Default is
+\code{1}. Larger values increase point/line sizes and vice versa. If argument
+\code{cex} is used, \code{size_point} will be ignored. Same for argument \code{lwd}, which
+overrides \code{size_line}.}
+
 \item{show_residuals}{Logical, if \code{TRUE}, display residuals of the model as a
 background to the model-based estimation. Residuals will be computed for the
 predictors in the data grid, using \code{\link[=residualize_over_grid]{residualize_over_grid()}}. For mixed
@@ -114,7 +144,9 @@ and error bars are shown. It is possible to set a global default value using
 \value{
 An object of class \code{visualisation_recipe} that describes the layers
 used to create a plot based on \code{{ggplot2}}. The related \code{plot()} method is in
-the \code{{see}} package.
+the \code{{see}} package. For \code{tinyplot()} (or its alias \code{plt()}), a base graphics
+R plot is created, which can be modified further using classic R graphics
+code.
 }
 \description{
 Most \strong{modelbased} objects can be visualized using either the \code{plot()}
@@ -203,25 +235,38 @@ plt_add(type = "l", lty = 2)
 # Reset to default theme
 tinytheme()
 
-# facets, grids, legends
+# facets
+# ----------------------
+# when using facets, the `tinyplot()` method checks if the legend is
+# redundant (because it already appears in the facets), and if so, it
+# removes the legend. Set `legend = TRUE` to add it back.
+
 data(efc, package = "modelbased")
+# convert to factors, assign labels. we use datawizard::to_factor() in
+# the second row to automatically assign value labels as factor levels.
+# because labels are too long for `c172code`, we assign new labels using
+# `as.factor()`
 efc$c172code <- factor(efc$c172code, labels = c("low", "mid", "high"))
+efc$e42dep <- datawizard::to_factor(efc$e42dep)
+# fit model
 m <- lm(neg_c_7 ~ c172code * e42dep, data = efc)
 em <- estimate_means(m, c("c172code", "e42dep"))
 
 # for facets, it can be useful to remove dodging
 plt(em, facet = ~e42dep, dodge = 0, theme = "float")
 
-# remove x-axis limits adjustments with `xlim`, remove legend
+# remove x-axis limits adjustments with `xlim`
 plt(
   em,
   facet = ~e42dep,
   dodge = 0,
   theme = "float",
   xlim = c(1, 3),
-  grid = TRUE,
-  legend = FALSE
+  grid = TRUE
 )
+
+# add back legend
+plt(em, facet = ~e42dep, legend = TRUE)
 }
 \dontshow{\}) # examplesIf}
 \dontshow{if (all(insight::check_if_installed(c("marginaleffects", "see", "ggplot2", "lme4"), quietly = TRUE)) && getRversion() >= "4.1.0") withAutoprint(\{ # examplesIf}