diff --git a/NAMESPACE b/NAMESPACE
index 24866726..364bc652 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -1,6 +1,10 @@
 # Generated by roxygen2: do not edit by hand
 
 S3method(`[`,scores)
+S3method(as_forecast_point,default)
+S3method(as_forecast_point,forecast_quantile)
+S3method(as_forecast_quantile,default)
+S3method(as_forecast_quantile,forecast_sample)
 S3method(assert_forecast,default)
 S3method(assert_forecast,forecast_binary)
 S3method(assert_forecast,forecast_point)
@@ -64,7 +68,6 @@ export(plot_pit)
 export(plot_quantile_coverage)
 export(plot_wis)
 export(quantile_score)
-export(sample_to_quantile)
 export(score)
 export(se_mean_sample)
 export(select_metrics)
diff --git a/NEWS.md b/NEWS.md
index 73f5169f..8905b5bc 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -22,7 +22,8 @@ of our [original](https://doi.org/10.48550/arXiv.2205.07090) `scoringutils` pape
   - the CRPS is now reported as decomposition into dispersion, overprediction and underprediction.
 
 ### Creating a forecast object  
-- The `as_forecast_<type>()` functions create a forecast object and validates it. They also allow users to rename/specify required columns and specify the forecast unit in a single step, taking over the functionality of `set_forecast_unit()` in most cases. 
+- The `as_forecast_<type>()` functions create a forecast object and validates it. They also allow users to rename/specify required columns and specify the forecast unit in a single step, taking over the functionality of `set_forecast_unit()` in most cases. See `?as_forecast()` for more information.
+- Some `as_forecast_<type>()` functions like e.g. `as_forecast_point()` and `as_forecast_quantile()` have S3 methods for converting from another forecast type to the respective forecast type. For example, `as_forecast_quantile()` has a method for converting from a `forecast_sample` object to a `forecast_quantile` object by estimating quantiles from the samples.
 
 ### Updated workflows
 - An example workflow for scoring a forecast now looks like this: 
@@ -281,7 +282,7 @@ the mean before returning an output.
 ### Bug fixes
 
 - Testing was expanded
-- Minor bugs were fixed, for example a bug in the `sample_to_quantile()` function
+- Minor bugs were fixed, for example a bug in the `as_forecast_quantile()` function
 (https://github.com/epiforecasts/scoringutils/pull/223)
 
 ### Package data updated
diff --git a/R/forecast.R b/R/forecast.R
index df6c2a79..7ce02f38 100644
--- a/R/forecast.R
+++ b/R/forecast.R
@@ -5,18 +5,24 @@
 #' and observations. If the input passes all input checks, it will be converted
 #' to a `forecast` object. A forecast object is a `data.table` with a
 #' class `forecast` and an additional class that depends on the forecast type.
-#' Every forecast type has its own `as_forecast_()` function.
+#' Every forecast type has its own `as_forecast_<type>()` function.
 #' See the details section below for more information
 #' on the expected input formats.
 #'
-#' The `as_forecast_()` functions give users some control over how their data
-#' is parsed.
+#' The `as_forecast_<type>()` functions give users some control over how their
+#' data is parsed.
 #' Using the arguments `observed`, `predicted`, `model`, etc. users can rename
 #' existing columns of their input data to match the required columns for a
 #' forecast object. Using the argument `forecast_unit`, users can specify the
 #' the columns that uniquely identify a single forecast (and remove the others,
 #' see [set_forecast_unit()] for details).
 #'
+#' The following functions are available:
+#' - [as_forecast_point()]
+#' - [as_forecast_binary()]
+#' - [as_forecast_sample()]
+#' - [as_forecast_quantile()]
+#'
 #' @param data A data.frame (or similar) with predicted and observed values.
 #'   See the details section of [as_forecast()] for additional information
 #'   on required input formats.
@@ -43,6 +49,8 @@
 #' - `forecast_sample` for sample-based forecasts
 #' - `forecast_quantile` for quantile-based forecasts
 #' @keywords check-forecasts
+#' @seealso [as_forecast_point()], [as_forecast_binary()],
+#'   [as_forecast_sample()], [as_forecast_quantile()]
 #' @examples
 #' as_forecast_binary(example_binary)
 #' as_forecast_quantile(
@@ -99,48 +107,105 @@ as_forecast_generic <- function(data,
 }
 
 
-#' @rdname as_forecast
+#' @title Create a `forecast` object for binary forecasts
+#' @description
+#' Create a `forecast` object for binary forecasts. See more information on
+#' forecast types and expected input formats by calling `?`[as_forecast()].
 #' @export
+#' @inheritParams as_forecast
+#' @seealso [as_forecast()], [as_forecast_point()], [as_forecast_binary()],
+#'   [as_forecast_sample()], [as_forecast_quantile()]
 #' @importFrom cli cli_warn
-as_forecast_point <- function(data,
-                              forecast_unit = NULL,
-                              observed = NULL,
-                              predicted = NULL,
-                              model = NULL) {
+#' @keywords check-forecasts
+as_forecast_binary <- function(data,
+                               forecast_unit = NULL,
+                               observed = NULL,
+                               predicted = NULL,
+                               model = NULL) {
   data <- as_forecast_generic(data, forecast_unit, observed, predicted, model)
-  data <- new_forecast(data, "forecast_point")
+  data <- new_forecast(data, "forecast_binary")
   assert_forecast(data)
   return(data)
 }
 
 
-#' @rdname as_forecast
+#' @title Create a `forecast` object for point forecasts
+#' @description
+#' Create a `forecast` object for point forecasts. See more information on
+#' forecast types and expected input formats by calling `?`[as_forecast()].
+#' @inherit as_forecast params
+#' @param ... Unused
+#' @seealso [as_forecast()], [as_forecast_point()], [as_forecast_binary()],
+#'   [as_forecast_sample()], [as_forecast_quantile()]
+#' @export
+#' @keywords check-forecasts
+as_forecast_point <- function(data, ...) {
+  UseMethod("as_forecast_point")
+}
+
+
+#' @rdname as_forecast_point
 #' @export
 #' @importFrom cli cli_warn
-as_forecast_binary <- function(data,
-                               forecast_unit = NULL,
-                               observed = NULL,
-                               predicted = NULL,
-                               model = NULL) {
+as_forecast_point.default <- function(data,
+                                      forecast_unit = NULL,
+                                      observed = NULL,
+                                      predicted = NULL,
+                                      model = NULL,
+                                      ...) {
   data <- as_forecast_generic(data, forecast_unit, observed, predicted, model)
-  data <- new_forecast(data, "forecast_binary")
+  data <- new_forecast(data, "forecast_point")
   assert_forecast(data)
   return(data)
 }
 
 
-#' @rdname as_forecast
+#' @rdname as_forecast_point
+#' @description
+#' When converting a `forecast_quantile` object into a `forecast_point` object,
+#' the 0.5 quantile is extracted and returned as the point forecast.
+#' @export
+#' @keywords check-forecasts
+as_forecast_point.forecast_quantile <- function(data, ...) {
+  assert_forecast(data, verbose = FALSE)
+  assert_subset(0.5, unique(data$quantile_level))
+
+  forecast <- data[quantile_level == 0.5]
+  forecast[, "quantile_level" := NULL]
+
+  point_forecast <- new_forecast(forecast, "forecast_point")
+  return(point_forecast)
+}
+
+
+#' @title Create a `forecast` object for quantile-based forecasts
+#' @description
+#' Create a `forecast` object for quantile-based forecasts. See more information
+#' on forecast types and expected input formats by calling `?`[as_forecast()].
+#' @param ... Unused
+#' @seealso [as_forecast()], [as_forecast_point()], [as_forecast_binary()],
+#'   [as_forecast_sample()], [as_forecast_quantile()]
+#' @inheritParams as_forecast
+#' @export
+#' @keywords check-forecasts
+as_forecast_quantile <- function(data, ...) {
+  UseMethod("as_forecast_quantile")
+}
+
+
+#' @rdname as_forecast_quantile
 #' @param quantile_level (optional) Name of the column in `data` that contains
 #'   the quantile level of the predicted values. This column will be renamed to
 #'   "quantile_level". Only applicable to quantile-based forecasts.
 #' @export
 #' @importFrom cli cli_warn
-as_forecast_quantile <- function(data,
-                                 forecast_unit = NULL,
-                                 observed = NULL,
-                                 predicted = NULL,
-                                 model = NULL,
-                                 quantile_level = NULL) {
+as_forecast_quantile.default <- function(data,
+                                         forecast_unit = NULL,
+                                         observed = NULL,
+                                         predicted = NULL,
+                                         model = NULL,
+                                         quantile_level = NULL,
+                                         ...) {
   assert_character(quantile_level, len = 1, null.ok = TRUE)
   assert_subset(quantile_level, names(data), empty.ok = TRUE)
   if (!is.null(quantile_level)) {
@@ -154,12 +219,61 @@ as_forecast_quantile <- function(data,
 }
 
 
-#' @rdname as_forecast
+#' @rdname as_forecast_quantile
+#' @description
+#' When creating a `forecast_quantile` object from a `forecast_sample` object,
+#' the quantiles are estimated by computing empircal quantiles from the samples
+#' via [quantile()]. Note that empirical quantiles are a biased estimator for
+#' the true quantiles in particular in the tails of the distribution and
+#' when the number of available samples is low.
+#' @param probs A numeric vector of quantile levels for which
+#'   quantiles will be computed. Corresponds to the `probs` argument in
+#'   [quantile()].
+#' @param type Type argument passed down to the quantile function. For more
+#'   information, see [quantile()].
+#' @importFrom stats quantile
+#' @importFrom methods hasArg
+#' @importFrom checkmate assert_numeric
+#' @export
+as_forecast_quantile.forecast_sample <- function(
+  data,
+  probs = c(0.05, 0.25, 0.5, 0.75, 0.95),
+  type = 7,
+  ...
+) {
+  forecast <- copy(data)
+  assert_forecast(forecast, verbose = FALSE)
+  assert_numeric(probs, min.len = 1)
+  reserved_columns <- c("predicted", "sample_id")
+  by <- setdiff(colnames(forecast), reserved_columns)
+
+  quantile_level <- unique(
+    round(c(probs, 1 - probs), digits = 10)
+  )
+
+  forecast <-
+    forecast[, .(quantile_level = quantile_level,
+                 predicted = quantile(x = predicted, probs = ..probs,
+                                      type = ..type, na.rm = TRUE)),
+             by = by]
+
+  quantile_forecast <- new_forecast(forecast, "forecast_quantile")
+  assert_forecast(quantile_forecast)
+
+  return(quantile_forecast)
+}
+
+
+#' @title Create a `forecast` object for sample-based forecasts
 #' @param sample_id (optional) Name of the column in `data` that contains the
 #'   sample id. This column will be renamed to "sample_id". Only applicable to
 #'   sample-based forecasts.
+#' @inheritParams as_forecast
 #' @export
+#' @seealso [as_forecast()], [as_forecast_point()], [as_forecast_binary()],
+#'   [as_forecast_sample()], [as_forecast_quantile()]
 #' @importFrom cli cli_warn
+#' @keywords check-forecasts
 as_forecast_sample <- function(data,
                                forecast_unit = NULL,
                                observed = NULL,
diff --git a/R/utils_data_handling.R b/R/utils_data_handling.R
index ff5d92c0..1b8ea27d 100644
--- a/R/utils_data_handling.R
+++ b/R/utils_data_handling.R
@@ -1,51 +1,3 @@
-#' @title Change data from a sample based format to a quantile format
-#'
-#' @description
-#' Transform data from a format that is based on predictive samples to a format
-#' based on plain quantiles.
-#'
-#' @param forecast A `forecast` object of class `forecast_sample` (a validated
-#'   data.table with predicted and observed values, see [as_forecast()]).
-#'
-#' @param quantile_level A numeric vector of quantile levels for which
-#'   quantiles will be computed.
-#' @param type Type argument passed down to the quantile function. For more
-#'   information, see [quantile()].
-#' @return a data.table in a long interval range format
-#' @importFrom data.table as.data.table
-#' @importFrom stats quantile
-#' @importFrom methods hasArg
-#' @importFrom checkmate assert_numeric
-#' @keywords data-handling
-#' @export
-#' @examples
-#' library(magrittr) # pipe operator
-#' example_sample_discrete %>%
-#'   as_forecast_sample() %>%
-#'   sample_to_quantile()
-sample_to_quantile <- function(forecast,
-                               quantile_level = c(0.05, 0.25, 0.5, 0.75, 0.95),
-                               type = 7) {
-  forecast <- copy(forecast)
-  assert_forecast(forecast, forecast_type = "sample", verbose = FALSE)
-  assert_numeric(quantile_level, min.len = 1)
-  reserved_columns <- c("predicted", "sample_id")
-  by <- setdiff(colnames(forecast), reserved_columns)
-
-  quantile_level <- unique(
-    round(c(quantile_level, 1 - quantile_level), digits = 10)
-  )
-
-  forecast <-
-    forecast[, .(quantile_level = quantile_level,
-                 predicted = quantile(x = predicted, probs = ..quantile_level,
-                                      type = ..type, na.rm = TRUE)),
-             by = by]
-
-  return(as_forecast_quantile(forecast))
-}
-
-
 # ==================== Functions internally used for scoring ===================
 # These functions would ideally be replaced in the future
 
@@ -186,7 +138,7 @@ quantile_to_interval_numeric <- function(observed,
 #' Transform data from a format that is based on predictive samples to a format
 #' based on interval ranges.
 #'
-#' @inheritParams sample_to_quantile
+#' @inheritParams as_forecast_quantile
 #' @param keep_quantile_col keep quantile_level column, default is TRUE
 #' @return A data.table in a long interval interval range format
 #' @importFrom data.table as.data.table
@@ -203,9 +155,9 @@ sample_to_interval_long <- function(data,
   upper_quantiles <- 1 - lower_quantiles
   quantile_levels <- sort(unique(c(lower_quantiles, upper_quantiles)))
 
-  data <- sample_to_quantile(
+  data <- as_forecast_quantile(
     data,
-    quantile_level = quantile_levels,
+    probs = quantile_levels,
     type = type
   )
 
diff --git a/R/z_globalVariables.R b/R/z_globalVariables.R
index d2037617..37c463cc 100644
--- a/R/z_globalVariables.R
+++ b/R/z_globalVariables.R
@@ -1,6 +1,6 @@
 globalVariables(c(
   "..index",
-  "..quantile_level",
+  "..probs",
   "..type",
   ".",
   ".SD",
diff --git a/inst/manuscript/R/00-standalone-Figure-replication.R b/inst/manuscript/R/00-standalone-Figure-replication.R
index 7276e833..c4ffdc78 100644
--- a/inst/manuscript/R/00-standalone-Figure-replication.R
+++ b/inst/manuscript/R/00-standalone-Figure-replication.R
@@ -143,7 +143,7 @@ pit_plots <- plot_pit(stored$pit) +
 # create interval and quantile coverage plots ----------------------------------
 # create coverage plots by transforming to quantile format first
 quantiles <- c(0.01, 0.025, seq(0.05, 0.95, 0.05), 0.975, 0.99)
-df_quantile <- sample_to_quantile(df,
+df_quantile <- as_forecast_quantile(df,
                                   quantiles = quantiles)
 
 res_quantile <- score(df_quantile)
diff --git a/inst/manuscript/R/toy-example-calibration.R b/inst/manuscript/R/toy-example-calibration.R
index 0f1db5df..0e3b6d22 100644
--- a/inst/manuscript/R/toy-example-calibration.R
+++ b/inst/manuscript/R/toy-example-calibration.R
@@ -72,7 +72,7 @@ pit_plots <- plot_pit(stored$pit) +
 # create interval and quantile coverage plots ----------------------------------
 # create coverage plots by transforming to quantile format first
 quantiles <- c(0.01, 0.025, seq(0.05, 0.95, 0.05), 0.975, 0.99)
-df_quantile <- sample_to_quantile(df,
+df_quantile <- as_forecast_quantile(df,
                                   quantile_level = quantiles)
 
 res_quantile <- score(df_quantile)
diff --git a/man/as_forecast.Rd b/man/as_forecast.Rd
index 8fb2e061..3fda18ef 100644
--- a/man/as_forecast.Rd
+++ b/man/as_forecast.Rd
@@ -2,46 +2,7 @@
 % Please edit documentation in R/forecast.R
 \name{as_forecast}
 \alias{as_forecast}
-\alias{as_forecast_point}
-\alias{as_forecast_binary}
-\alias{as_forecast_quantile}
-\alias{as_forecast_sample}
 \title{Create a \code{forecast} object}
-\usage{
-as_forecast_point(
-  data,
-  forecast_unit = NULL,
-  observed = NULL,
-  predicted = NULL,
-  model = NULL
-)
-
-as_forecast_binary(
-  data,
-  forecast_unit = NULL,
-  observed = NULL,
-  predicted = NULL,
-  model = NULL
-)
-
-as_forecast_quantile(
-  data,
-  forecast_unit = NULL,
-  observed = NULL,
-  predicted = NULL,
-  model = NULL,
-  quantile_level = NULL
-)
-
-as_forecast_sample(
-  data,
-  forecast_unit = NULL,
-  observed = NULL,
-  predicted = NULL,
-  model = NULL,
-  sample_id = NULL
-)
-}
 \arguments{
 \item{data}{A data.frame (or similar) with predicted and observed values.
 See the details section of \code{\link[=as_forecast]{as_forecast()}} for additional information
@@ -63,14 +24,6 @@ predicted values. This column will be renamed to "predicted".}
 \item{model}{(optional) Name of the column in \code{data} that contains the names
 of the models/forecasters that generated the predicted values.
 This column will be renamed to "model".}
-
-\item{quantile_level}{(optional) Name of the column in \code{data} that contains
-the quantile level of the predicted values. This column will be renamed to
-"quantile_level". Only applicable to quantile-based forecasts.}
-
-\item{sample_id}{(optional) Name of the column in \code{data} that contains the
-sample id. This column will be renamed to "sample_id". Only applicable to
-sample-based forecasts.}
 }
 \value{
 Depending on the forecast type, an object of the following class will be
@@ -87,17 +40,25 @@ Process and validate a data.frame (or similar) or similar with forecasts
 and observations. If the input passes all input checks, it will be converted
 to a \code{forecast} object. A forecast object is a \code{data.table} with a
 class \code{forecast} and an additional class that depends on the forecast type.
-Every forecast type has its own \code{as_forecast_()} function.
+Every forecast type has its own \verb{as_forecast_<type>()} function.
 See the details section below for more information
 on the expected input formats.
 
-The \code{as_forecast_()} functions give users some control over how their data
-is parsed.
+The \verb{as_forecast_<type>()} functions give users some control over how their
+data is parsed.
 Using the arguments \code{observed}, \code{predicted}, \code{model}, etc. users can rename
 existing columns of their input data to match the required columns for a
 forecast object. Using the argument \code{forecast_unit}, users can specify the
 the columns that uniquely identify a single forecast (and remove the others,
 see \code{\link[=set_forecast_unit]{set_forecast_unit()}} for details).
+
+The following functions are available:
+\itemize{
+\item \code{\link[=as_forecast_point]{as_forecast_point()}}
+\item \code{\link[=as_forecast_binary]{as_forecast_binary()}}
+\item \code{\link[=as_forecast_sample]{as_forecast_sample()}}
+\item \code{\link[=as_forecast_quantile]{as_forecast_quantile()}}
+}
 }
 \section{Forecast types and input formats}{
 Various different forecast types / forecast formats are supported. At the
@@ -180,4 +141,8 @@ as_forecast_quantile(
                     "horizon", "location")
 )
 }
+\seealso{
+\code{\link[=as_forecast_point]{as_forecast_point()}}, \code{\link[=as_forecast_binary]{as_forecast_binary()}},
+\code{\link[=as_forecast_sample]{as_forecast_sample()}}, \code{\link[=as_forecast_quantile]{as_forecast_quantile()}}
+}
 \keyword{check-forecasts}
diff --git a/man/as_forecast_binary.Rd b/man/as_forecast_binary.Rd
new file mode 100644
index 00000000..c965d9a1
--- /dev/null
+++ b/man/as_forecast_binary.Rd
@@ -0,0 +1,45 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/forecast.R
+\name{as_forecast_binary}
+\alias{as_forecast_binary}
+\title{Create a \code{forecast} object for binary forecasts}
+\usage{
+as_forecast_binary(
+  data,
+  forecast_unit = NULL,
+  observed = NULL,
+  predicted = NULL,
+  model = NULL
+)
+}
+\arguments{
+\item{data}{A data.frame (or similar) with predicted and observed values.
+See the details section of \code{\link[=as_forecast]{as_forecast()}} for additional information
+on required input formats.}
+
+\item{forecast_unit}{(optional) Name of the columns in \code{data} (after
+any renaming of columns) that denote the unit of a
+single forecast. See \code{\link[=get_forecast_unit]{get_forecast_unit()}} for details.
+If \code{NULL} (the default), all columns that are not required columns are
+assumed to form the unit of a single forecast. If specified, all columns
+that are not part of the forecast unit (or required columns) will be removed.}
+
+\item{observed}{(optional) Name of the column in \code{data} that contains the
+observed values. This column will be renamed to "observed".}
+
+\item{predicted}{(optional) Name of the column in \code{data} that contains the
+predicted values. This column will be renamed to "predicted".}
+
+\item{model}{(optional) Name of the column in \code{data} that contains the names
+of the models/forecasters that generated the predicted values.
+This column will be renamed to "model".}
+}
+\description{
+Create a \code{forecast} object for binary forecasts. See more information on
+forecast types and expected input formats by calling \verb{?}\code{\link[=as_forecast]{as_forecast()}}.
+}
+\seealso{
+\code{\link[=as_forecast]{as_forecast()}}, \code{\link[=as_forecast_point]{as_forecast_point()}}, \code{\link[=as_forecast_binary]{as_forecast_binary()}},
+\code{\link[=as_forecast_sample]{as_forecast_sample()}}, \code{\link[=as_forecast_quantile]{as_forecast_quantile()}}
+}
+\keyword{check-forecasts}
diff --git a/man/as_forecast_point.Rd b/man/as_forecast_point.Rd
new file mode 100644
index 00000000..25da6844
--- /dev/null
+++ b/man/as_forecast_point.Rd
@@ -0,0 +1,57 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/forecast.R
+\name{as_forecast_point}
+\alias{as_forecast_point}
+\alias{as_forecast_point.default}
+\alias{as_forecast_point.forecast_quantile}
+\title{Create a \code{forecast} object for point forecasts}
+\usage{
+as_forecast_point(data, ...)
+
+\method{as_forecast_point}{default}(
+  data,
+  forecast_unit = NULL,
+  observed = NULL,
+  predicted = NULL,
+  model = NULL,
+  ...
+)
+
+\method{as_forecast_point}{forecast_quantile}(data, ...)
+}
+\arguments{
+\item{data}{A data.frame (or similar) with predicted and observed values.
+See the details section of \code{\link[=as_forecast]{as_forecast()}} for additional information
+on required input formats.}
+
+\item{...}{Unused}
+
+\item{forecast_unit}{(optional) Name of the columns in \code{data} (after
+any renaming of columns) that denote the unit of a
+single forecast. See \code{\link[=get_forecast_unit]{get_forecast_unit()}} for details.
+If \code{NULL} (the default), all columns that are not required columns are
+assumed to form the unit of a single forecast. If specified, all columns
+that are not part of the forecast unit (or required columns) will be removed.}
+
+\item{observed}{(optional) Name of the column in \code{data} that contains the
+observed values. This column will be renamed to "observed".}
+
+\item{predicted}{(optional) Name of the column in \code{data} that contains the
+predicted values. This column will be renamed to "predicted".}
+
+\item{model}{(optional) Name of the column in \code{data} that contains the names
+of the models/forecasters that generated the predicted values.
+This column will be renamed to "model".}
+}
+\description{
+Create a \code{forecast} object for point forecasts. See more information on
+forecast types and expected input formats by calling \verb{?}\code{\link[=as_forecast]{as_forecast()}}.
+
+When converting a \code{forecast_quantile} object into a \code{forecast_point} object,
+the 0.5 quantile is extracted and returned as the point forecast.
+}
+\seealso{
+\code{\link[=as_forecast]{as_forecast()}}, \code{\link[=as_forecast_point]{as_forecast_point()}}, \code{\link[=as_forecast_binary]{as_forecast_binary()}},
+\code{\link[=as_forecast_sample]{as_forecast_sample()}}, \code{\link[=as_forecast_quantile]{as_forecast_quantile()}}
+}
+\keyword{check-forecasts}
diff --git a/man/as_forecast_quantile.Rd b/man/as_forecast_quantile.Rd
new file mode 100644
index 00000000..edb5d5e5
--- /dev/null
+++ b/man/as_forecast_quantile.Rd
@@ -0,0 +1,77 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/forecast.R
+\name{as_forecast_quantile}
+\alias{as_forecast_quantile}
+\alias{as_forecast_quantile.default}
+\alias{as_forecast_quantile.forecast_sample}
+\title{Create a \code{forecast} object for quantile-based forecasts}
+\usage{
+as_forecast_quantile(data, ...)
+
+\method{as_forecast_quantile}{default}(
+  data,
+  forecast_unit = NULL,
+  observed = NULL,
+  predicted = NULL,
+  model = NULL,
+  quantile_level = NULL,
+  ...
+)
+
+\method{as_forecast_quantile}{forecast_sample}(
+  data,
+  probs = c(0.05, 0.25, 0.5, 0.75, 0.95),
+  type = 7,
+  ...
+)
+}
+\arguments{
+\item{data}{A data.frame (or similar) with predicted and observed values.
+See the details section of \code{\link[=as_forecast]{as_forecast()}} for additional information
+on required input formats.}
+
+\item{...}{Unused}
+
+\item{forecast_unit}{(optional) Name of the columns in \code{data} (after
+any renaming of columns) that denote the unit of a
+single forecast. See \code{\link[=get_forecast_unit]{get_forecast_unit()}} for details.
+If \code{NULL} (the default), all columns that are not required columns are
+assumed to form the unit of a single forecast. If specified, all columns
+that are not part of the forecast unit (or required columns) will be removed.}
+
+\item{observed}{(optional) Name of the column in \code{data} that contains the
+observed values. This column will be renamed to "observed".}
+
+\item{predicted}{(optional) Name of the column in \code{data} that contains the
+predicted values. This column will be renamed to "predicted".}
+
+\item{model}{(optional) Name of the column in \code{data} that contains the names
+of the models/forecasters that generated the predicted values.
+This column will be renamed to "model".}
+
+\item{quantile_level}{(optional) Name of the column in \code{data} that contains
+the quantile level of the predicted values. This column will be renamed to
+"quantile_level". Only applicable to quantile-based forecasts.}
+
+\item{probs}{A numeric vector of quantile levels for which
+quantiles will be computed. Corresponds to the \code{probs} argument in
+\code{\link[=quantile]{quantile()}}.}
+
+\item{type}{Type argument passed down to the quantile function. For more
+information, see \code{\link[=quantile]{quantile()}}.}
+}
+\description{
+Create a \code{forecast} object for quantile-based forecasts. See more information
+on forecast types and expected input formats by calling \verb{?}\code{\link[=as_forecast]{as_forecast()}}.
+
+When creating a \code{forecast_quantile} object from a \code{forecast_sample} object,
+the quantiles are estimated by computing empircal quantiles from the samples
+via \code{\link[=quantile]{quantile()}}. Note that empirical quantiles are a biased estimator for
+the true quantiles in particular in the tails of the distribution and
+when the number of available samples is low.
+}
+\seealso{
+\code{\link[=as_forecast]{as_forecast()}}, \code{\link[=as_forecast_point]{as_forecast_point()}}, \code{\link[=as_forecast_binary]{as_forecast_binary()}},
+\code{\link[=as_forecast_sample]{as_forecast_sample()}}, \code{\link[=as_forecast_quantile]{as_forecast_quantile()}}
+}
+\keyword{check-forecasts}
diff --git a/man/as_forecast_sample.Rd b/man/as_forecast_sample.Rd
new file mode 100644
index 00000000..45e1ae98
--- /dev/null
+++ b/man/as_forecast_sample.Rd
@@ -0,0 +1,49 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/forecast.R
+\name{as_forecast_sample}
+\alias{as_forecast_sample}
+\title{Create a \code{forecast} object for sample-based forecasts}
+\usage{
+as_forecast_sample(
+  data,
+  forecast_unit = NULL,
+  observed = NULL,
+  predicted = NULL,
+  model = NULL,
+  sample_id = NULL
+)
+}
+\arguments{
+\item{data}{A data.frame (or similar) with predicted and observed values.
+See the details section of \code{\link[=as_forecast]{as_forecast()}} for additional information
+on required input formats.}
+
+\item{forecast_unit}{(optional) Name of the columns in \code{data} (after
+any renaming of columns) that denote the unit of a
+single forecast. See \code{\link[=get_forecast_unit]{get_forecast_unit()}} for details.
+If \code{NULL} (the default), all columns that are not required columns are
+assumed to form the unit of a single forecast. If specified, all columns
+that are not part of the forecast unit (or required columns) will be removed.}
+
+\item{observed}{(optional) Name of the column in \code{data} that contains the
+observed values. This column will be renamed to "observed".}
+
+\item{predicted}{(optional) Name of the column in \code{data} that contains the
+predicted values. This column will be renamed to "predicted".}
+
+\item{model}{(optional) Name of the column in \code{data} that contains the names
+of the models/forecasters that generated the predicted values.
+This column will be renamed to "model".}
+
+\item{sample_id}{(optional) Name of the column in \code{data} that contains the
+sample id. This column will be renamed to "sample_id". Only applicable to
+sample-based forecasts.}
+}
+\description{
+Create a \code{forecast} object for sample-based forecasts
+}
+\seealso{
+\code{\link[=as_forecast]{as_forecast()}}, \code{\link[=as_forecast_point]{as_forecast_point()}}, \code{\link[=as_forecast_binary]{as_forecast_binary()}},
+\code{\link[=as_forecast_sample]{as_forecast_sample()}}, \code{\link[=as_forecast_quantile]{as_forecast_quantile()}}
+}
+\keyword{check-forecasts}
diff --git a/man/sample_to_interval_long.Rd b/man/sample_to_interval_long.Rd
index 959d1a1b..c16254a0 100644
--- a/man/sample_to_interval_long.Rd
+++ b/man/sample_to_interval_long.Rd
@@ -12,6 +12,10 @@ sample_to_interval_long(
 )
 }
 \arguments{
+\item{data}{A data.frame (or similar) with predicted and observed values.
+See the details section of \code{\link[=as_forecast]{as_forecast()}} for additional information
+on required input formats.}
+
 \item{type}{Type argument passed down to the quantile function. For more
 information, see \code{\link[=quantile]{quantile()}}.}
 
diff --git a/man/sample_to_quantile.Rd b/man/sample_to_quantile.Rd
deleted file mode 100644
index db885c66..00000000
--- a/man/sample_to_quantile.Rd
+++ /dev/null
@@ -1,36 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/utils_data_handling.R
-\name{sample_to_quantile}
-\alias{sample_to_quantile}
-\title{Change data from a sample based format to a quantile format}
-\usage{
-sample_to_quantile(
-  forecast,
-  quantile_level = c(0.05, 0.25, 0.5, 0.75, 0.95),
-  type = 7
-)
-}
-\arguments{
-\item{forecast}{A \code{forecast} object of class \code{forecast_sample} (a validated
-data.table with predicted and observed values, see \code{\link[=as_forecast]{as_forecast()}}).}
-
-\item{quantile_level}{A numeric vector of quantile levels for which
-quantiles will be computed.}
-
-\item{type}{Type argument passed down to the quantile function. For more
-information, see \code{\link[=quantile]{quantile()}}.}
-}
-\value{
-a data.table in a long interval range format
-}
-\description{
-Transform data from a format that is based on predictive samples to a format
-based on plain quantiles.
-}
-\examples{
-library(magrittr) # pipe operator
-example_sample_discrete \%>\%
-  as_forecast_sample() \%>\%
-  sample_to_quantile()
-}
-\keyword{data-handling}
diff --git a/tests/testthat/test-forecast.R b/tests/testthat/test-forecast.R
index c6359cd5..7d31baf8 100644
--- a/tests/testthat/test-forecast.R
+++ b/tests/testthat/test-forecast.R
@@ -90,7 +90,11 @@ test_that("as_forecast() warns if there are different numbers of quantiles", {
   expect_no_condition(w)
 })
 
-
+test_that("as_forecast_point() works", {
+  expect_no_condition(
+    as_forecast_point(as_forecast_quantile(na.omit(example_quantile)))
+  )
+})
 
 test_that("check_columns_present() works", {
   expect_equal(
diff --git a/tests/testthat/test-utils_data_handling.R b/tests/testthat/test-utils_data_handling.R
index fd5e9355..cbca2f9e 100644
--- a/tests/testthat/test-utils_data_handling.R
+++ b/tests/testthat/test-utils_data_handling.R
@@ -59,14 +59,15 @@ test_that("quantile_to_interval_dataframe() works", {
 })
 
 
-test_that("sample_to_quantiles works", {
+test_that("as_forecast_quantiles works", {
   samples <- data.frame(
     date = as.Date("2020-01-01") + 1:10,
     model = "model1",
     observed = 1:10,
     predicted = c(rep(0, 10), 2:11, 3:12, 4:13, rep(100, 10)),
     sample_id = rep(1:5, each = 10)
-  )
+  ) %>%
+    as_forecast_sample()
 
   quantile <- data.frame(
     date = rep(as.Date("2020-01-01") + 1:10, each = 2),
@@ -76,34 +77,33 @@ test_that("sample_to_quantiles works", {
     predicted = rep(2:11, each = 2) + c(0, 2)
   )
 
-  expect_error(
-    sample_to_quantile(samples, quantile_level = c(0.25, 0.75)),
-    "The input needs to be a valid forecast object."
+  expect_no_condition(
+    as_forecast_quantile(samples, probs = c(0.25, 0.75))
   )
 
   wrongclass <- as_forecast_sample(samples)
   class(wrongclass) <- c("forecast_point", "data.table", "data.frame")
   expect_error(
-    sample_to_quantile(wrongclass, quantile_level = c(0.25, 0.75)),
-    'Desired forecast type: "sample"'
+    as_forecast_quantile(wrongclass, quantile_level = c(0.25, 0.75)),
+    "Assertion on 'quantile_level' failed: Must be of type"
   )
 
 
-  quantile2 <- sample_to_quantile(
+  quantile2 <- as_forecast_quantile(
     as_forecast_sample(samples),
-    quantile_level = c(0.25, 0.75)
+    probs = c(0.25, 0.75)
   )
 
   expect_equal(quantile, as.data.frame(quantile2))
 
-  # Verify that `type` is correctly scoped in sample_to_quantile(), as it is
+  # Verify that `type` is correctly scoped in as_forecast_quantile(), as it is
   # also an argument.
-  # If it's not scoped well, the call to `sample_to_quantile()` will fail.
+  # If it's not scoped well, the call to `as_forecast_quantile()` will fail.
   samples$type <- "test"
 
-  quantile3 <- sample_to_quantile(
+  quantile3 <- as_forecast_quantile(
     as_forecast_sample(samples),
-    quantile_level = c(0.25, 0.75)
+    probs = c(0.25, 0.75)
   )
   quantile3$type <- NULL
 
@@ -113,12 +113,12 @@ test_that("sample_to_quantiles works", {
   )
 })
 
-test_that("sample_to_quantiles issue 557 fix", {
+test_that("as_forecast_quantiles issue 557 fix", {
 
   out <- example_sample_discrete %>%
     as_forecast_sample() %>%
-    sample_to_quantile(
-      quantile_level = c(0.01, 0.025, seq(0.05, 0.95, 0.05), 0.975, 0.99)
+    as_forecast_quantile(
+      probs = c(0.01, 0.025, seq(0.05, 0.95, 0.05), 0.975, 0.99)
     ) %>%
     score()
 
@@ -269,7 +269,13 @@ test_that("quantile_to_interval works - data.frame case", {
     colnames(ex_interval),
     c(colnames(ex), "boundary", "interval_range")
   )
+
+  expect_error(
+    quantile_to_interval(x = "not working"),
+    "Input must be either a data.frame or a numeric vector."
+  )
 })
 
 
 
+