diff --git a/NAMESPACE b/NAMESPACE
index c4e3a3ad2..02ff443b3 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -1,6 +1,8 @@
 # Generated by roxygen2: do not edit by hand
 
 S3method(plot,scoringutils_available_forecasts)
+S3method(plot,scoringutils_correlation)
+S3method(plot,scoringutils_pairwise)
 S3method(print,scoringutils_check)
 export(abs_error)
 export(add_coverage)
diff --git a/NEWS.md b/NEWS.md
index a3104d27d..86956fdd5 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -3,11 +3,18 @@
 This minor update addresses comments made by review from the Journal of Statistical Software (see preprint of the manuscript [here](https://arxiv.org/abs/2205.07090)).
 
 ## Package updates
-- changes to `avail_forecasts()` and `plot_avail_forecasts()`:
-  - the function `avail_forecasts()` was renamed to `available_forecasts()` for consistency with `available_metrics()`. The old function, `avail_forecasts()` is still available as an alias, but will be removed in the future. 
-  - For clarity, the output column in `avail_forecasts()` was renamed from "Number forecasts" to "count". 
-  - `available_forecasts()` now also displays combinations where there are 0 forecasts, instead of silently dropping corresponding rows. 
-  - `plot_avail_forecasts()` has been deprecated in favour of an S3 method for `plot()`. An alias is still available, but will be removed in the future. 
+
+### changes to `avail_forecasts()` and `plot_avail_forecasts()`:
+- the function `avail_forecasts()` was renamed to `available_forecasts()` for consistency with `available_metrics()`. The old function, `avail_forecasts()` is still available as an alias, but will be removed in the future. 
+- For clarity, the output column in `avail_forecasts()` was renamed from "Number forecasts" to "count". 
+- `available_forecasts()` now also displays combinations where there are 0 forecasts, instead of silently dropping corresponding rows. 
+
+### New S3 plotting methods
+- `plot_avail_forecasts()` has been deprecated in favour of an S3 method for `plot()`. An alias is still available, but will be removed in the future. 
+- `plot_correlation()` has been deprecated in favour of an S3 method for `plot()`. An alias is still available, but will be removed in the future. 
+- `plot_pairwise_comparison()` has been deprecated in favour of an S3 method for `plot()`. An alias is still available, but will be removed in the future. 
+
+### other
 - the deprecated `..density..` was replaced with `after_stat(density)` in ggplot calls.
 - files ending in ".Rda" were renamed to ".rds" where appropriate when used together with `saveRDS()` or readRDS()`. 
 
diff --git a/R/avail_forecasts.R b/R/avail_forecasts.R
index b34180ca9..672fe8794 100644
--- a/R/avail_forecasts.R
+++ b/R/avail_forecasts.R
@@ -20,11 +20,14 @@
 #' that a single forecast only gets counted once.
 #'
 #' @return A data.table with columns as specified in `by` and an additional
-#' column "count" with the number of forecasts.
+#' column "count" with the number of forecasts. In addition the output has class
+#' `scoringutils_available_forecasts` and can e.g. be visualised
+#' using [plot()] (which dispatches [plot.scoringutils_available_forecasts()].
 #'
 #' @inheritParams score
 #' @importFrom data.table .I .N nafill
 #' @export
+#' @seealso [plot.scoringutils_available_forecasts()]
 #' @keywords check-forecasts
 #' @examples
 #' data.table::setDTthreads(1) # only needed to avoid issues on CRAN
diff --git a/R/correlations.R b/R/correlations.R
index 5b8233576..580176e82 100644
--- a/R/correlations.R
+++ b/R/correlations.R
@@ -2,20 +2,25 @@
 #'
 #' @description
 #' Calculate the correlation between different metrics for a data.frame of
-#' scores as produced by [score()].
+#' scores as produced by [score()]. You can visualise results
 #'
 #' @param metrics A character vector with the metrics to show. If set to
 #' `NULL` (default), all metrics present in `scores` will
 #' be shown
 #' @inheritParams pairwise_comparison
-#' @return A data.table with correlations for the different metrics
+#' @return A data.table with correlations for the different metrics. In addition,
+#' the output is of class `scoringutils_correlation` and can e.g. be visualised
+#' using [plot()] (which dispatches [plot.scoringutils_correlation()].
+#'
 #' @importFrom data.table setDT
 #' @importFrom stats cor na.omit
 #' @export
 #' @keywords scoring
+#' @seealso [plot.scoringutils_correlation()]
 #' @examples
 #' scores <- score(example_quantile)
-#' correlation(scores)
+#' corrs <- correlation(scores)
+#' plot(corrs)
 correlation <- function(scores,
                         metrics = NULL) {
   metrics <- check_metrics(metrics)
@@ -49,6 +54,8 @@ correlation <- function(scores,
     keep.rownames = TRUE
   )[, metric := rn][, rn := NULL]
 
+  class(correlations) <- c("scoringutils_correlation", class(correlations))
+
   return(correlations[])
 }
 
diff --git a/R/pairwise-comparisons.R b/R/pairwise-comparisons.R
index efce335eb..ec94bcaba 100644
--- a/R/pairwise-comparisons.R
+++ b/R/pairwise-comparisons.R
@@ -43,7 +43,9 @@
 #' model against which to compare other models.
 #' @param ... additional arguments for the comparison between two models. See
 #' [compare_two_models()] for more information.
-#' @return A ggplot2 object with a coloured table of summarised scores
+#' @return A data.table with pairwise comparisons. In addition, the output is
+#' of class `scoringutils_pairwise` and can be visualised using [plot()] (which
+#' dispatches [plot.scoringutils_pairwise()].
 #' @importFrom data.table as.data.table data.table setnames copy
 #' @importFrom stats sd rbinom wilcox.test p.adjust
 #' @importFrom utils combn
@@ -51,6 +53,7 @@
 #' @author Nikos Bosse \email{nikosbosse@@gmail.com}
 #' @author Johannes Bracher, \email{johannes.bracher@@kit.edu}
 #' @keywords scoring
+#' @seealso [plot.scoringutils_pairwise()]
 #' @examples
 #' data.table::setDTthreads(1) # only needed to avoid issues on CRAN
 #'
@@ -58,7 +61,7 @@
 #' pairwise <- pairwise_comparison(scores, by = "target_type")
 #'
 #' library(ggplot2)
-#' plot_pairwise_comparison(pairwise, type = "mean_scores_ratio") +
+#' plot(pairwise, type = "mean_scores_ratio") +
 #'   facet_wrap(~target_type)
 
 pairwise_comparison <- function(scores,
@@ -139,6 +142,8 @@ pairwise_comparison <- function(scores,
 
   out <- data.table::rbindlist(results)
 
+  class(out) <- c("scoringutils_pairwise", class(out))
+
   return(out[])
 }
 
diff --git a/R/plot.R b/R/plot.R
index cf6607b0c..6e97901f4 100644
--- a/R/plot.R
+++ b/R/plot.R
@@ -19,6 +19,7 @@
 #' @importFrom ggplot2 ggplot aes element_blank element_text labs coord_cartesian coord_flip
 #' @importFrom data.table setDT melt
 #' @importFrom stats sd
+#' @family plotting functions
 #' @export
 #'
 #' @examples
@@ -148,6 +149,7 @@ plot_score_table <- function(scores,
 #' scale_fill_discrete
 #' theme theme_light unit guides guide_legend .data
 #' @export
+#' @family plotting functions
 #' @examples
 #' library(ggplot2)
 #' scores <- score(example_quantile)
@@ -233,6 +235,7 @@ plot_wis <- function(scores,
 #' @importFrom ggplot2 ggplot aes aes geom_point geom_line
 #' expand_limits theme theme_light element_text scale_color_continuous labs
 #' @export
+#' @family plotting functions
 #' @examples
 #' library(ggplot2)
 #' scores <- score(example_quantile)
@@ -296,6 +299,7 @@ plot_ranges <- function(scores,
 #' @importFrom ggplot2 ggplot  aes geom_tile geom_text .data
 #' scale_fill_gradient2 labs element_text coord_cartesian
 #' @export
+#' @family plotting functions
 #' @examples
 #' scores <- score(example_quantile)
 #' scores <- summarise_scores(scores, by = c("model", "target_type", "range"))
@@ -352,6 +356,7 @@ plot_heatmap <- function(scores,
 #' @importFrom data.table dcast
 #' @importFrom ggdist geom_lineribbon
 #' @export
+#' @family plotting functions
 #' @examples
 #' library(ggplot2)
 #' library(magrittr)
@@ -581,6 +586,7 @@ make_na <- make_NA
 #' facet_wrap facet_grid geom_polygon
 #' @importFrom data.table dcast
 #' @export
+#' @family plotting functions
 #' @examples
 #' data.table::setDTthreads(1) # only needed to avoid issues on CRAN
 #' scores <- score(example_quantile)
@@ -637,6 +643,7 @@ plot_interval_coverage <- function(scores,
 #' scale_y_continuous
 #' @importFrom data.table dcast
 #' @export
+#' @family plotting functions
 #' @examples
 #' scores <- score(example_quantile)
 #' scores <- summarise_scores(scores, by = c("model", "quantile"))
@@ -687,18 +694,22 @@ plot_quantile_coverage <- function(scores,
   return(p2)
 }
 
+
 #' @title Plot Heatmap of Pairwise Comparisons
 #'
 #' @description
 #' Creates a heatmap of the ratios or pvalues from a pairwise comparison
 #' between models
 #'
-#' @param comparison_result A data.frame as produced by
+#' @param x An S3 object as produced by
 #' [pairwise_comparison()]
 #' @param type character vector of length one that is either
 #'  "mean_scores_ratio" or "pval". This denotes whether to
 #' visualise the ratio or the p-value of the pairwise comparison.
 #' Default is "mean_scores_ratio".
+#' @inheritParams print.scoringutils_check
+#' @return A data.table with pairwise comparisons. In addition, the output is
+#' of class `scoringutils_pairwise` and can be visualised using [plot()].
 #' @importFrom ggplot2 ggplot aes geom_tile geom_text labs coord_cartesian
 #' scale_fill_gradient2 theme_light element_text
 #' @importFrom data.table as.data.table setnames rbindlist
@@ -706,16 +717,18 @@ plot_quantile_coverage <- function(scores,
 #' @importFrom ggplot2 labs coord_cartesian facet_wrap facet_grid theme
 #' element_text element_blank
 #' @export
+#' @family plotting functions
 #' @examples
 #' library(ggplot2)
 #' scores <- score(example_quantile)
 #' pairwise <- pairwise_comparison(scores, by = "target_type")
-#' plot_pairwise_comparison(pairwise, type = "mean_scores_ratio") +
+#' plot(pairwise, type = "mean_scores_ratio") +
 #'   facet_wrap(~target_type)
 
-plot_pairwise_comparison <- function(comparison_result,
-                                     type = c("mean_scores_ratio", "pval")) {
-  comparison_result <- data.table::as.data.table(comparison_result)
+plot.scoringutils_pairwise <- function(x,
+                                       type = c("mean_scores_ratio", "pval"),
+                                       ...) {
+  comparison_result <- data.table::as.data.table(x)
 
   comparison_result[, model := reorder(model, -relative_skill)]
   levels <- levels(comparison_result$model)
@@ -810,6 +823,28 @@ plot_pairwise_comparison <- function(comparison_result,
 }
 
 
+#' @title `r lifecycle::badge("deprecated")` Plot Heatmap of Pairwise Comparisons
+#'
+#' @description
+#' Deprecated version of [plot.scoringutils_pairwise()]
+#'
+#' @param comparison_result A data.frame as produced by
+#' [pairwise_comparison()]
+#' @inherit plot.scoringutils_pairwise
+#' @export
+plot_pairwise_comparison <- function(comparison_result,
+                                     type = c("mean_scores_ratio", "pval")) {
+
+  lifecycle::deprecate_warn(
+    "1.2.2", "plot_pairwise_comparison()",
+    "plot()"
+  )
+
+  plot.scoringutils_pairwise(x = comparison_result,
+                             type = type)
+}
+
+
 #' @title PIT Histogram
 #'
 #' @description
@@ -833,6 +868,7 @@ plot_pairwise_comparison <- function(comparison_result,
 #' @importFrom stats as.formula
 #' @importFrom ggplot2 geom_col
 #' @importFrom stats density
+#' @family plotting functions
 #' @return vector with the scoring values
 #' @examples
 #' data.table::setDTthreads(1) # only needed to avoid issues on CRAN
@@ -937,6 +973,7 @@ plot_pit <- function(pit,
   return(hist)
 }
 
+
 #' @title Visualise Where Forecasts Are Available
 #'
 #' @description
@@ -958,6 +995,7 @@ plot_pit <- function(pit,
 #' geom_tile scale_fill_gradient .data
 #' @importFrom data.table dcast .I .N
 #' @export
+#' @family plotting functions
 #' @examples
 #' library(ggplot2)
 #' available_forecasts <- available_forecasts(
@@ -1047,33 +1085,32 @@ plot_avail_forecasts <- function(available_forecasts,
 }
 
 
-
-
 #' @title Plot Correlation Between Metrics
-#'
 #' @description
 #' Plots a heatmap of correlations between different metrics
 #'
-#' @param correlations A data.table of correlations between scores as produced
-#' by [correlation()].
+#' @param x An S3 object with correlations between scores as produced by
+#' [correlation()].
+#' @inheritParams print.scoringutils_check
 #' @return A ggplot2 object showing a coloured matrix of correlations
 #' between metrics
 #' @importFrom ggplot2 ggplot geom_tile geom_text aes scale_fill_gradient2
 #' element_text labs coord_cartesian theme element_blank
 #' @importFrom data.table setDT melt
 #' @export
+#' @family plotting functions
 #' @examples
 #' scores <- score(example_quantile)
 #' correlations <- correlation(
 #'  summarise_scores(scores)
 #' )
-#' plot_correlation(correlations)
+#' plot(correlations)
 
-plot_correlation <- function(correlations) {
+plot.scoringutils_correlation <- function(x, ...) {
 
-  metrics <- names(correlations)[names(correlations) %in% available_metrics()]
+  metrics <- names(x)[names(x) %in% available_metrics()]
 
-  lower_triangle <- get_lower_tri(correlations[, .SD, .SDcols = metrics])
+  lower_triangle <- get_lower_tri(x[, .SD, .SDcols = metrics])
   rownames(lower_triangle) <- colnames(lower_triangle)
 
   # get plot data.frame
@@ -1111,6 +1148,23 @@ plot_correlation <- function(correlations) {
   return(plot)
 }
 
+
+#' @title `r lifecycle::badge("deprecated")` Plot Correlation Between Metrics
+#'
+#' @description
+#' Deprecated version of [plot.scoringutils_correlation()] for compatibility.
+#' @param correlations A data.table with correalations as produced by
+#' [correlation()]
+#' @export
+plot_correlation <- function(correlations) {
+  lifecycle::deprecate_warn(
+    "1.2.2", "plot_correlation()",
+    "plot()"
+  )
+  plot.scoringutils_correlation(x = correlations)
+}
+
+
 #' @title Scoringutils ggplot2 theme
 #'
 #' @description
diff --git a/inst/manuscript/R/00-standalone-Figure-replication.R b/inst/manuscript/R/00-standalone-Figure-replication.R
index f6d801697..297b0c8b7 100644
--- a/inst/manuscript/R/00-standalone-Figure-replication.R
+++ b/inst/manuscript/R/00-standalone-Figure-replication.R
@@ -660,4 +660,4 @@ correlations |>
   glimpse()
 
 correlations |>
-  plot_correlation()
+  plot()
diff --git a/inst/manuscript/manuscript.Rmd b/inst/manuscript/manuscript.Rmd
index b0b14f728..c87026c91 100644
--- a/inst/manuscript/manuscript.Rmd
+++ b/inst/manuscript/manuscript.Rmd
@@ -527,7 +527,7 @@ Using the function \fct{plot\_pairwise\_comparison} we can visualise the mean sc
 score(example_quantile) |>
   pairwise_comparison(by = c("model", "target_type"), 
                       baseline = "EuroCOVIDhub-baseline") |>
-  plot_pairwise_comparison() + 
+  plot() + 
   facet_wrap(~ target_type)
 ```
 
@@ -642,8 +642,7 @@ correlations <- example_quantile |>
 correlations |>
   glimpse()
 
-correlations |>
-  plot_correlation()
+plot(correlations)
 ```
 
 ## Summary and discussion
diff --git a/man/available_forecasts.Rd b/man/available_forecasts.Rd
index a35e7d891..8e5217cd6 100644
--- a/man/available_forecasts.Rd
+++ b/man/available_forecasts.Rd
@@ -48,7 +48,9 @@ that a single forecast only gets counted once.}
 }
 \value{
 A data.table with columns as specified in \code{by} and an additional
-column "count" with the number of forecasts.
+column "count" with the number of forecasts. In addition the output has class
+\code{scoringutils_available_forecasts} and can e.g. be visualised
+using \code{\link[=plot]{plot()}} (which dispatches \code{\link[=plot.scoringutils_available_forecasts]{plot.scoringutils_available_forecasts()}}.
 }
 \description{
 Given a data set with forecasts, count the number of available forecasts
@@ -64,4 +66,7 @@ available_forecasts(example_quantile,
   by = c("model", "target_type")
 )
 }
+\seealso{
+\code{\link[=plot.scoringutils_available_forecasts]{plot.scoringutils_available_forecasts()}}
+}
 \keyword{check-forecasts}
diff --git a/man/correlation.Rd b/man/correlation.Rd
index 77de3a469..4e93d34bb 100644
--- a/man/correlation.Rd
+++ b/man/correlation.Rd
@@ -14,14 +14,20 @@ correlation(scores, metrics = NULL)
 be shown}
 }
 \value{
-A data.table with correlations for the different metrics
+A data.table with correlations for the different metrics. In addition,
+the output is of class \code{scoringutils_correlation} and can e.g. be visualised
+using \code{\link[=plot]{plot()}} (which dispatches \code{\link[=plot.scoringutils_correlation]{plot.scoringutils_correlation()}}.
 }
 \description{
 Calculate the correlation between different metrics for a data.frame of
-scores as produced by \code{\link[=score]{score()}}.
+scores as produced by \code{\link[=score]{score()}}. You can visualise results
 }
 \examples{
 scores <- score(example_quantile)
-correlation(scores)
+corrs <- correlation(scores)
+plot(corrs)
+}
+\seealso{
+\code{\link[=plot.scoringutils_correlation]{plot.scoringutils_correlation()}}
 }
 \keyword{scoring}
diff --git a/man/pairwise_comparison.Rd b/man/pairwise_comparison.Rd
index 4c14b975e..d94ec6e2e 100644
--- a/man/pairwise_comparison.Rd
+++ b/man/pairwise_comparison.Rd
@@ -36,7 +36,9 @@ model against which to compare other models.}
 \code{\link[=compare_two_models]{compare_two_models()}} for more information.}
 }
 \value{
-A ggplot2 object with a coloured table of summarised scores
+A data.table with pairwise comparisons. In addition, the output is
+of class \code{scoringutils_pairwise} and can be visualised using \code{\link[=plot]{plot()}} (which
+dispatches \code{\link[=plot.scoringutils_pairwise]{plot.scoringutils_pairwise()}}.
 }
 \description{
 Compute relative scores between different models making pairwise
@@ -70,9 +72,12 @@ scores <- score(example_quantile)
 pairwise <- pairwise_comparison(scores, by = "target_type")
 
 library(ggplot2)
-plot_pairwise_comparison(pairwise, type = "mean_scores_ratio") +
+plot(pairwise, type = "mean_scores_ratio") +
   facet_wrap(~target_type)
 }
+\seealso{
+\code{\link[=plot.scoringutils_pairwise]{plot.scoringutils_pairwise()}}
+}
 \author{
 Nikos Bosse \email{nikosbosse@gmail.com}
 
diff --git a/man/plot.scoringutils_available_forecasts.Rd b/man/plot.scoringutils_available_forecasts.Rd
index f11e0dc5d..26cb3fda8 100644
--- a/man/plot.scoringutils_available_forecasts.Rd
+++ b/man/plot.scoringutils_available_forecasts.Rd
@@ -48,3 +48,17 @@ plot(
 ) +
  facet_wrap("target_type")
 }
+\seealso{
+Other plotting functions: 
+\code{\link{plot.scoringutils_correlation}()},
+\code{\link{plot.scoringutils_pairwise}()},
+\code{\link{plot_heatmap}()},
+\code{\link{plot_interval_coverage}()},
+\code{\link{plot_pit}()},
+\code{\link{plot_predictions}()},
+\code{\link{plot_quantile_coverage}()},
+\code{\link{plot_ranges}()},
+\code{\link{plot_score_table}()},
+\code{\link{plot_wis}()}
+}
+\concept{plotting functions}
diff --git a/man/plot.scoringutils_correlation.Rd b/man/plot.scoringutils_correlation.Rd
new file mode 100644
index 000000000..f56e4eea2
--- /dev/null
+++ b/man/plot.scoringutils_correlation.Rd
@@ -0,0 +1,42 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/plot.R
+\name{plot.scoringutils_correlation}
+\alias{plot.scoringutils_correlation}
+\title{Plot Correlation Between Metrics}
+\usage{
+\method{plot}{scoringutils_correlation}(x, ...)
+}
+\arguments{
+\item{x}{An S3 object with correlations between scores as produced by
+\code{\link[=correlation]{correlation()}}.}
+
+\item{...}{additional arguments (not used here)}
+}
+\value{
+A ggplot2 object showing a coloured matrix of correlations
+between metrics
+}
+\description{
+Plots a heatmap of correlations between different metrics
+}
+\examples{
+scores <- score(example_quantile)
+correlations <- correlation(
+ summarise_scores(scores)
+)
+plot(correlations)
+}
+\seealso{
+Other plotting functions: 
+\code{\link{plot.scoringutils_available_forecasts}()},
+\code{\link{plot.scoringutils_pairwise}()},
+\code{\link{plot_heatmap}()},
+\code{\link{plot_interval_coverage}()},
+\code{\link{plot_pit}()},
+\code{\link{plot_predictions}()},
+\code{\link{plot_quantile_coverage}()},
+\code{\link{plot_ranges}()},
+\code{\link{plot_score_table}()},
+\code{\link{plot_wis}()}
+}
+\concept{plotting functions}
diff --git a/man/plot.scoringutils_pairwise.Rd b/man/plot.scoringutils_pairwise.Rd
new file mode 100644
index 000000000..d4a4d5809
--- /dev/null
+++ b/man/plot.scoringutils_pairwise.Rd
@@ -0,0 +1,48 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/plot.R
+\name{plot.scoringutils_pairwise}
+\alias{plot.scoringutils_pairwise}
+\title{Plot Heatmap of Pairwise Comparisons}
+\usage{
+\method{plot}{scoringutils_pairwise}(x, type = c("mean_scores_ratio", "pval"), ...)
+}
+\arguments{
+\item{x}{An S3 object as produced by
+\code{\link[=pairwise_comparison]{pairwise_comparison()}}}
+
+\item{type}{character vector of length one that is either
+"mean_scores_ratio" or "pval". This denotes whether to
+visualise the ratio or the p-value of the pairwise comparison.
+Default is "mean_scores_ratio".}
+
+\item{...}{additional arguments (not used here)}
+}
+\value{
+A data.table with pairwise comparisons. In addition, the output is
+of class \code{scoringutils_pairwise} and can be visualised using \code{\link[=plot]{plot()}}.
+}
+\description{
+Creates a heatmap of the ratios or pvalues from a pairwise comparison
+between models
+}
+\examples{
+library(ggplot2)
+scores <- score(example_quantile)
+pairwise <- pairwise_comparison(scores, by = "target_type")
+plot(pairwise, type = "mean_scores_ratio") +
+  facet_wrap(~target_type)
+}
+\seealso{
+Other plotting functions: 
+\code{\link{plot.scoringutils_available_forecasts}()},
+\code{\link{plot.scoringutils_correlation}()},
+\code{\link{plot_heatmap}()},
+\code{\link{plot_interval_coverage}()},
+\code{\link{plot_pit}()},
+\code{\link{plot_predictions}()},
+\code{\link{plot_quantile_coverage}()},
+\code{\link{plot_ranges}()},
+\code{\link{plot_score_table}()},
+\code{\link{plot_wis}()}
+}
+\concept{plotting functions}
diff --git a/man/plot_correlation.Rd b/man/plot_correlation.Rd
index 390c90f20..4fd75faf2 100644
--- a/man/plot_correlation.Rd
+++ b/man/plot_correlation.Rd
@@ -2,25 +2,14 @@
 % Please edit documentation in R/plot.R
 \name{plot_correlation}
 \alias{plot_correlation}
-\title{Plot Correlation Between Metrics}
+\title{\ifelse{html}{\href{https://lifecycle.r-lib.org/articles/stages.html#deprecated}{\figure{lifecycle-deprecated.svg}{options: alt='[Deprecated]'}}}{\strong{[Deprecated]}} Plot Correlation Between Metrics}
 \usage{
 plot_correlation(correlations)
 }
 \arguments{
-\item{correlations}{A data.table of correlations between scores as produced
-by \code{\link[=correlation]{correlation()}}.}
-}
-\value{
-A ggplot2 object showing a coloured matrix of correlations
-between metrics
+\item{correlations}{A data.table with correalations as produced by
+\code{\link[=correlation]{correlation()}}}
 }
 \description{
-Plots a heatmap of correlations between different metrics
-}
-\examples{
-scores <- score(example_quantile)
-correlations <- correlation(
- summarise_scores(scores)
-)
-plot_correlation(correlations)
+Deprecated version of \code{\link[=plot.scoringutils_correlation]{plot.scoringutils_correlation()}} for compatibility.
 }
diff --git a/man/plot_heatmap.Rd b/man/plot_heatmap.Rd
index 8b1aac549..4c623e5f0 100644
--- a/man/plot_heatmap.Rd
+++ b/man/plot_heatmap.Rd
@@ -33,3 +33,17 @@ scores <- summarise_scores(scores, by = c("model", "target_type", "range"))
 
 plot_heatmap(scores, x = "target_type", metric = "bias")
 }
+\seealso{
+Other plotting functions: 
+\code{\link{plot.scoringutils_available_forecasts}()},
+\code{\link{plot.scoringutils_correlation}()},
+\code{\link{plot.scoringutils_pairwise}()},
+\code{\link{plot_interval_coverage}()},
+\code{\link{plot_pit}()},
+\code{\link{plot_predictions}()},
+\code{\link{plot_quantile_coverage}()},
+\code{\link{plot_ranges}()},
+\code{\link{plot_score_table}()},
+\code{\link{plot_wis}()}
+}
+\concept{plotting functions}
diff --git a/man/plot_interval_coverage.Rd b/man/plot_interval_coverage.Rd
index 9c7da16fe..9aaa7b679 100644
--- a/man/plot_interval_coverage.Rd
+++ b/man/plot_interval_coverage.Rd
@@ -26,3 +26,17 @@ scores <- score(example_quantile)
 scores <- summarise_scores(scores, by = c("model", "range"))
 plot_interval_coverage(scores)
 }
+\seealso{
+Other plotting functions: 
+\code{\link{plot.scoringutils_available_forecasts}()},
+\code{\link{plot.scoringutils_correlation}()},
+\code{\link{plot.scoringutils_pairwise}()},
+\code{\link{plot_heatmap}()},
+\code{\link{plot_pit}()},
+\code{\link{plot_predictions}()},
+\code{\link{plot_quantile_coverage}()},
+\code{\link{plot_ranges}()},
+\code{\link{plot_score_table}()},
+\code{\link{plot_wis}()}
+}
+\concept{plotting functions}
diff --git a/man/plot_pairwise_comparison.Rd b/man/plot_pairwise_comparison.Rd
index 71b5e77e8..41b5b9579 100644
--- a/man/plot_pairwise_comparison.Rd
+++ b/man/plot_pairwise_comparison.Rd
@@ -2,7 +2,7 @@
 % Please edit documentation in R/plot.R
 \name{plot_pairwise_comparison}
 \alias{plot_pairwise_comparison}
-\title{Plot Heatmap of Pairwise Comparisons}
+\title{\ifelse{html}{\href{https://lifecycle.r-lib.org/articles/stages.html#deprecated}{\figure{lifecycle-deprecated.svg}{options: alt='[Deprecated]'}}}{\strong{[Deprecated]}} Plot Heatmap of Pairwise Comparisons}
 \usage{
 plot_pairwise_comparison(
   comparison_result,
@@ -18,14 +18,30 @@ plot_pairwise_comparison(
 visualise the ratio or the p-value of the pairwise comparison.
 Default is "mean_scores_ratio".}
 }
+\value{
+A data.table with pairwise comparisons. In addition, the output is
+of class \code{scoringutils_pairwise} and can be visualised using \code{\link[=plot]{plot()}}.
+}
 \description{
-Creates a heatmap of the ratios or pvalues from a pairwise comparison
-between models
+Deprecated version of \code{\link[=plot.scoringutils_pairwise]{plot.scoringutils_pairwise()}}
 }
 \examples{
 library(ggplot2)
 scores <- score(example_quantile)
 pairwise <- pairwise_comparison(scores, by = "target_type")
-plot_pairwise_comparison(pairwise, type = "mean_scores_ratio") +
+plot(pairwise, type = "mean_scores_ratio") +
   facet_wrap(~target_type)
 }
+\seealso{
+Other plotting functions: 
+\code{\link{plot.scoringutils_available_forecasts}()},
+\code{\link{plot.scoringutils_correlation}()},
+\code{\link{plot_heatmap}()},
+\code{\link{plot_interval_coverage}()},
+\code{\link{plot_pit}()},
+\code{\link{plot_predictions}()},
+\code{\link{plot_quantile_coverage}()},
+\code{\link{plot_ranges}()},
+\code{\link{plot_score_table}()},
+\code{\link{plot_wis}()}
+}
diff --git a/man/plot_pit.Rd b/man/plot_pit.Rd
index d0eceabd3..df4bec54c 100644
--- a/man/plot_pit.Rd
+++ b/man/plot_pit.Rd
@@ -48,3 +48,17 @@ plot_pit(pit, breaks = seq(0.1, 1, 0.1))
 pit <- pit(example_integer,by = "model")
 plot_pit(pit)
 }
+\seealso{
+Other plotting functions: 
+\code{\link{plot.scoringutils_available_forecasts}()},
+\code{\link{plot.scoringutils_correlation}()},
+\code{\link{plot.scoringutils_pairwise}()},
+\code{\link{plot_heatmap}()},
+\code{\link{plot_interval_coverage}()},
+\code{\link{plot_predictions}()},
+\code{\link{plot_quantile_coverage}()},
+\code{\link{plot_ranges}()},
+\code{\link{plot_score_table}()},
+\code{\link{plot_wis}()}
+}
+\concept{plotting functions}
diff --git a/man/plot_predictions.Rd b/man/plot_predictions.Rd
index 6464262fa..c079a687f 100644
--- a/man/plot_predictions.Rd
+++ b/man/plot_predictions.Rd
@@ -68,3 +68,17 @@ example_continuous \%>\%
   facet_wrap(~ location + target_type, scales = "free_y") +
   aes(fill = model, color = model)
 }
+\seealso{
+Other plotting functions: 
+\code{\link{plot.scoringutils_available_forecasts}()},
+\code{\link{plot.scoringutils_correlation}()},
+\code{\link{plot.scoringutils_pairwise}()},
+\code{\link{plot_heatmap}()},
+\code{\link{plot_interval_coverage}()},
+\code{\link{plot_pit}()},
+\code{\link{plot_quantile_coverage}()},
+\code{\link{plot_ranges}()},
+\code{\link{plot_score_table}()},
+\code{\link{plot_wis}()}
+}
+\concept{plotting functions}
diff --git a/man/plot_quantile_coverage.Rd b/man/plot_quantile_coverage.Rd
index 2e6ef489e..e553ca9a2 100644
--- a/man/plot_quantile_coverage.Rd
+++ b/man/plot_quantile_coverage.Rd
@@ -25,3 +25,17 @@ scores <- score(example_quantile)
 scores <- summarise_scores(scores, by = c("model", "quantile"))
 plot_quantile_coverage(scores)
 }
+\seealso{
+Other plotting functions: 
+\code{\link{plot.scoringutils_available_forecasts}()},
+\code{\link{plot.scoringutils_correlation}()},
+\code{\link{plot.scoringutils_pairwise}()},
+\code{\link{plot_heatmap}()},
+\code{\link{plot_interval_coverage}()},
+\code{\link{plot_pit}()},
+\code{\link{plot_predictions}()},
+\code{\link{plot_ranges}()},
+\code{\link{plot_score_table}()},
+\code{\link{plot_wis}()}
+}
+\concept{plotting functions}
diff --git a/man/plot_ranges.Rd b/man/plot_ranges.Rd
index a4a999ff2..e0acd3739 100644
--- a/man/plot_ranges.Rd
+++ b/man/plot_ranges.Rd
@@ -41,3 +41,17 @@ plot_ranges(scores, x = "model") +
 plot_ranges(scores, y = "dispersion", x = "model") +
   facet_wrap(~target_type)
 }
+\seealso{
+Other plotting functions: 
+\code{\link{plot.scoringutils_available_forecasts}()},
+\code{\link{plot.scoringutils_correlation}()},
+\code{\link{plot.scoringutils_pairwise}()},
+\code{\link{plot_heatmap}()},
+\code{\link{plot_interval_coverage}()},
+\code{\link{plot_pit}()},
+\code{\link{plot_predictions}()},
+\code{\link{plot_quantile_coverage}()},
+\code{\link{plot_score_table}()},
+\code{\link{plot_wis}()}
+}
+\concept{plotting functions}
diff --git a/man/plot_score_table.Rd b/man/plot_score_table.Rd
index 9984e8000..492f5fd10 100644
--- a/man/plot_score_table.Rd
+++ b/man/plot_score_table.Rd
@@ -45,3 +45,17 @@ plot_score_table(scores,
                  y = c("model", "target_type"),
                  by = "target_type")
 }
+\seealso{
+Other plotting functions: 
+\code{\link{plot.scoringutils_available_forecasts}()},
+\code{\link{plot.scoringutils_correlation}()},
+\code{\link{plot.scoringutils_pairwise}()},
+\code{\link{plot_heatmap}()},
+\code{\link{plot_interval_coverage}()},
+\code{\link{plot_pit}()},
+\code{\link{plot_predictions}()},
+\code{\link{plot_quantile_coverage}()},
+\code{\link{plot_ranges}()},
+\code{\link{plot_wis}()}
+}
+\concept{plotting functions}
diff --git a/man/plot_wis.Rd b/man/plot_wis.Rd
index 982ff1406..984c02126 100644
--- a/man/plot_wis.Rd
+++ b/man/plot_wis.Rd
@@ -46,3 +46,17 @@ plot_wis(scores,
 Bracher J, Ray E, Gneiting T, Reich, N (2020) Evaluating epidemic forecasts
 in an interval format. \url{https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008618}
 }
+\seealso{
+Other plotting functions: 
+\code{\link{plot.scoringutils_available_forecasts}()},
+\code{\link{plot.scoringutils_correlation}()},
+\code{\link{plot.scoringutils_pairwise}()},
+\code{\link{plot_heatmap}()},
+\code{\link{plot_interval_coverage}()},
+\code{\link{plot_pit}()},
+\code{\link{plot_predictions}()},
+\code{\link{plot_quantile_coverage}()},
+\code{\link{plot_ranges}()},
+\code{\link{plot_score_table}()}
+}
+\concept{plotting functions}
diff --git a/tests/testthat/test-plot_correlation.R b/tests/testthat/test-plot_correlation.R
index 9d6280d6e..8a0e7e67b 100644
--- a/tests/testthat/test-plot_correlation.R
+++ b/tests/testthat/test-plot_correlation.R
@@ -1,6 +1,6 @@
-test_that("plot_correlation() works as expected", {
+test_that("plot works as expected for correlation", {
   correlations <- correlation(summarise_scores(scores))
-  p <- plot_correlation(correlations)
+  p <- plot(correlations)
   expect_s3_class(p, "ggplot")
   skip_on_cran()
   vdiffr::expect_doppelganger("plot__correlation", p)
diff --git a/tests/testthat/test-plot_pairwise_comparison.R b/tests/testthat/test-plot_pairwise_comparison.R
index 4cd477e90..c4839a264 100644
--- a/tests/testthat/test-plot_pairwise_comparison.R
+++ b/tests/testthat/test-plot_pairwise_comparison.R
@@ -2,16 +2,16 @@ pairwise <- suppressMessages(
   pairwise_comparison(scores, by = "target_type")
 )
 
-test_that("plot_pairwise_comparison() works as expected", {
-  p <- plot_pairwise_comparison(pairwise) +
+test_that("plot() for pairwise comparisons works as expected", {
+  p <- plot(pairwise) +
     ggplot2::facet_wrap(~target_type)
   expect_s3_class(p, "ggplot")
   skip_on_cran()
   vdiffr::expect_doppelganger("plot_pairwise_comparison", p)
 })
 
-test_that("plot_pairwise_comparison() works when showing p values", {
-  p <- plot_pairwise_comparison(pairwise, type = "pval") +
+test_that("plot() for pairwise comparisons works when showing p values", {
+  p <- plot(pairwise, type = "pval") +
     ggplot2::facet_wrap(~target_type)
   expect_s3_class(p, "ggplot")
   skip_on_cran()
diff --git a/vignettes/scoringutils.Rmd b/vignettes/scoringutils.Rmd
index b4dd0bf16..80c2d1321 100644
--- a/vignettes/scoringutils.Rmd
+++ b/vignettes/scoringutils.Rmd
@@ -351,7 +351,7 @@ If using the `pairwise_comparison()` function, we can also visualise pairwise co
 example_quantile %>%
   score() %>%
   pairwise_comparison(by = c("model", "target_type")) %>%
-  plot_pairwise_comparison() +
+  plot() +
   facet_wrap(~ target_type)
 ```
 
@@ -376,7 +376,7 @@ example_quantile %>%
   score() %>%
   summarise_scores() %>%
   correlation() %>%
-  plot_correlation()
+  plot()
 ```
 
 ### Scores by interval ranges