diff --git a/DESCRIPTION b/DESCRIPTION
index 85b588f2b..d5a9adad9 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: scoringutils
 Title: Utilities for Scoring and Assessing Predictions
-Version: 1.1.1
+Version: 1.1.2
 Language: en-GB
 Authors@R: c(
     person(given = "Nikos",
diff --git a/NAMESPACE b/NAMESPACE
index 334ea3856..68eb44f95 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -17,6 +17,7 @@ export(crps_sample)
 export(dss_sample)
 export(find_duplicates)
 export(interval_score)
+export(log_shift)
 export(logs_binary)
 export(logs_sample)
 export(mad_sample)
@@ -45,6 +46,7 @@ export(squared_error)
 export(summarise_scores)
 export(summarize_scores)
 export(theme_scoringutils)
+export(transform_forecasts)
 importFrom(data.table,"%like%")
 importFrom(data.table,':=')
 importFrom(data.table,.I)
diff --git a/NEWS.md b/NEWS.md
index 79ab29fd9..1635621ca 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,3 +1,11 @@
+# scoringutils 1.1.2
+
+## Feature updates
+
+- added a new function, `transform_forecasts()` to make it easy to transform forecasts before scoring them, as suggested in Bosse et al. (2023), https://www.medrxiv.org/content/10.1101/2023.01.23.23284722v1. 
+- added another function, `log_shift()` that implements the default transformation function. The function allows to truncate negative values and add an offset before applying the logarithm. 
+
+
 # scoringutils 1.1.1
 
 - added a small change to `interval_score()` which explicitly converts the logical vector to a numeric one. This should happen implicitly anyway, but is now done explicitly in order to avoid issues that may come up if the input vector has a type that doesn't allow the implict conversion.  
diff --git a/R/convenience-functions.R b/R/convenience-functions.R
new file mode 100644
index 000000000..e660511d3
--- /dev/null
+++ b/R/convenience-functions.R
@@ -0,0 +1,209 @@
+#' @title Transform forecasts and observed values
+#'
+#' @description Function to transform forecasts and true values before scoring.
+#'
+#' @details There are a few reasons, depending on the circumstances, for
+#' why this might be desirable (check out the linked reference for more info).
+#' In epidemiology, for example, it may be useful to log-transform incidence
+#' counts before evaluating forecasts using scores such as the weighted interval
+#' score (WIS) or the continuous ranked probability score (CRPS).
+#' Log-transforming forecasts and observations changes the interpretation of
+#' the score from a measure of absolute distance between forecast and
+#' observation to a score that evaluates a forecast of the exponential growth
+#' rate. Another motivation can be to apply a variance-stabilising
+#' transformation or to standardise incidence counts by population.
+#'
+#' Note that if you want to apply a transformation, it is important to transform
+#' the forecasts and observations and then apply the score. Applying a
+#' transformation after the score risks losing propriety of the proper scoring
+#' rule.
+#'
+#' @inheritParams score
+#' @param fun A function used to transform both true values and predictions.
+#' The default function is [log_shift()], a custom function that is essentially
+#' the same as [log()], but has two additional arguments (`offset` and
+#' `negative_to_zero`) that allow you to first truncate negative values to zero
+#' and then add an offset before applying the logarithm.
+#' @param append whether or not to append a transformed version of the data to
+#' the currently existing data (default is TRUE). If selected, the data gets
+#' transformed and appended to the existing data frame, making it possible to
+#' use the outcome directly in [score()]. An additional column, 'scale', gets
+#' created that denotes which rows or untransformed ('scale' has the value
+#' "natural") and which have been transformed ('scale' has the value passed to
+#' the argument `label`).
+#' @param label A string for the newly created 'scale' column to denote the
+#' newly transformed values. Only relevant if `append = TRUE`.
+#' @param ... Additional parameters to pass to the function you supplied.
+#' @return A data.table with either a transformed version of the data, or one
+#' with both the untransformed and the transformed data. includes the original data as well as a
+#' transformation of the original data. There will be one additional column,
+#' 'scale', present which will be set to "natural" for the untransformed
+#' forecasts.
+#'
+#' @importFrom data.table ':=' is.data.table copy
+#' @author Nikos Bosse \email{nikosbosse@@gmail.com}
+#' @export
+#' @references Transformation of forecasts for evaluating predictive
+#' performance in an epidemiological context
+#' Nikos I. Bosse, Sam Abbott, Anne Cori, Edwin van Leeuwen, Johannes Bracher,
+#' Sebastian Funk
+#' medRxiv 2023.01.23.23284722
+#' \doi{https://doi.org/10.1101/2023.01.23.23284722}
+#' <https://www.medrxiv.org/content/10.1101/2023.01.23.23284722v1> # nolint
+
+#' @keywords check-forecasts
+#' @examples
+#'
+#' library(magrittr) # pipe operator
+#'
+#' # add log transformed forecasts (produces a warning as some values are zero)
+#' # negative values need to be handled (here by replacing them with 0)
+#' example_quantile %>%
+#'   .[, true_value := ifelse(true_value < 0, 0, true_value)] %>%
+#'   transform_forecasts(append = FALSE)
+#'
+#' # alternatively:
+#' transform_forecasts(example_quantile, negative_to_zero = TRUE, append = FALSE)
+#'
+#' # specifying an offset manually for the log transformation removes the warning
+#' example_quantile %>%
+#'   .[, true_value := ifelse(true_value < 0, 0, true_value)] %>%
+#'   transform_forecasts(offset = 1, append = FALSE)
+#'
+#' # truncating forecasts manually before sqrt
+#' example_quantile %>%
+#'   .[, true_value := ifelse(true_value < 0, 0, true_value)] %>%
+#'   transform_forecasts(fun = sqrt, label = "sqrt")
+#'
+#' # alternatively, this achieves the same
+#' example_quantile %>%
+#'   transform_forecasts(fun = function(x) pmax(0, x), append = FALSE) %>%
+#'   transform_forecasts(fun = sqrt, label = "sqrt")
+#'
+#' # adding multiple transformations
+#' library(magrittr) # pipe operator
+#' example_quantile %>%
+#'   .[, true_value := ifelse(true_value < 0, 0, true_value)] %>%
+#'   transform_forecasts(offset = 1) %>%
+#'   transform_forecasts(fun = sqrt, label = "sqrt") %>%
+#'   score() %>%
+#'   summarise_scores(by = c("model", "scale"))
+
+transform_forecasts <- function(data,
+                                fun = log_shift,
+                                append = TRUE,
+                                label = "log",
+                                ...) {
+  original_data <- as.data.table(data)
+  scale_col_present <- ("scale" %in% colnames(original_data))
+
+  # Error handling
+  if (scale_col_present) {
+    if (!("natural" %in% original_data$scale)) {
+      stop(
+        "If a column 'scale' is present, entries with scale =='natural' are required for the transformation"
+      )
+    }
+    if (append && (label %in% original_data$scale)) {
+      w <- paste0(
+        "Appending new transformations with label '",
+        label,
+        "', even though that entry is already present in column 'scale'."
+      )
+      warning(w)
+    }
+  }
+
+  if (append) {
+    if (scale_col_present) {
+      transformed_data <-
+        data.table::copy(original_data)[scale == "natural"]
+    } else {
+      transformed_data <- data.table::copy(original_data)
+      original_data[, scale := "natural"]
+    }
+    transformed_data[, prediction := fun(prediction, ...)]
+    transformed_data[, true_value := fun(true_value, ...)]
+    transformed_data[, scale := label]
+    out <- rbind(original_data, transformed_data)
+    return(out[])
+  }
+
+  # check if a column called "scale" is already present and if so, only
+  # restrict to transformations of the original data
+  if (scale_col_present) {
+    original_data[scale == "natural", prediction := fun(prediction, ...)]
+    original_data[scale == "natural", true_value := fun(true_value, ...)]
+    original_data[scale == "natural", scale := label]
+  } else {
+    original_data[, prediction := fun(prediction, ...)]
+    original_data[, true_value := fun(true_value, ...)]
+  }
+  return(original_data[])
+}
+
+
+
+
+
+
+#' @title Log transformation with an additive shift
+#'
+#' @description Function that shifts a value by some offset and then applies the
+#' natural logarithm to it.
+#'
+#' @details The output is computed as log(x + offset) (or
+#' log(pmax(0, x) + offset)) if `negative_to_zero = TRUE`.
+#'
+#' @param x vector of input values to be transformed
+#' @param offset number to add to the input value before taking the natural
+#' logarithm
+#' @param negative_to_zero whether or not to replace all negative values with
+#' zero before applying the log transformation. Default is FALSE.
+#' @param base a positive or complex number: the base with respect to which
+#' logarithms are computed. Defaults to e = exp(1).
+#' @return A numeric vector with transformed values
+#' @export
+#' @references Transformation of forecasts for evaluating predictive
+#' performance in an epidemiological context
+#' Nikos I. Bosse, Sam Abbott, Anne Cori, Edwin van Leeuwen, Johannes Bracher,
+#' Sebastian Funk
+#' medRxiv 2023.01.23.23284722
+#' \doi{https://doi.org/10.1101/2023.01.23.23284722}
+#' <https://www.medrxiv.org/content/10.1101/2023.01.23.23284722v1> # nolint
+
+#' @keywords check-forecasts
+#' @examples
+#'
+#' log_shift(1:10)
+#' log_shift(0:9, offset = 1)
+#' log_shift(-1:9, offset = 1, negative_to_zero = TRUE)
+#'
+#' transform_forecasts(
+#'   example_quantile,
+#'   fun = log_shift,
+#'   offset = 1,
+#'   negative_to_zero = TRUE
+#'  )
+
+log_shift <- function(x,
+                      offset = 0,
+                      negative_to_zero = FALSE,
+                      base = exp(1)) {
+  if (negative_to_zero) {
+    x <- pmax(0, x)
+  }
+
+  if (any (x < 0, na.rm = TRUE)) {
+    w <- paste("Detected input values < 0.",
+               "Try truncating negative values (use negative_to_zero = TRUE)")
+    warning(w)
+  }
+
+  if (any(x == 0, na.rm = TRUE) && offset == 0) {
+    w <- paste0("Detected zeros in input values.",
+                "Try specifying offset = 1 (or any other offset).")
+    warning(w)
+  }
+  log(x + offset, base = base)
+}
diff --git a/man/log_shift.Rd b/man/log_shift.Rd
new file mode 100644
index 000000000..78ea80567
--- /dev/null
+++ b/man/log_shift.Rd
@@ -0,0 +1,54 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/convenience-functions.R
+\name{log_shift}
+\alias{log_shift}
+\title{Log transformation with an additive shift}
+\usage{
+log_shift(x, offset = 0, negative_to_zero = FALSE, base = exp(1))
+}
+\arguments{
+\item{x}{vector of input values to be transformed}
+
+\item{offset}{number to add to the input value before taking the natural
+logarithm}
+
+\item{negative_to_zero}{whether or not to replace all negative values with
+zero before applying the log transformation. Default is FALSE.}
+
+\item{base}{a positive or complex number: the base with respect to which
+logarithms are computed. Defaults to e = exp(1).}
+}
+\value{
+A numeric vector with transformed values
+}
+\description{
+Function that shifts a value by some offset and then applies the
+natural logarithm to it.
+}
+\details{
+The output is computed as log(x + offset) (or
+log(pmax(0, x) + offset)) if \code{negative_to_zero = TRUE}.
+}
+\examples{
+
+log_shift(1:10)
+log_shift(0:9, offset = 1)
+log_shift(-1:9, offset = 1, negative_to_zero = TRUE)
+
+transform_forecasts(
+  example_quantile,
+  fun = log_shift,
+  offset = 1,
+  negative_to_zero = TRUE
+ )
+}
+\references{
+Transformation of forecasts for evaluating predictive
+performance in an epidemiological context
+Nikos I. Bosse, Sam Abbott, Anne Cori, Edwin van Leeuwen, Johannes Bracher,
+Sebastian Funk
+medRxiv 2023.01.23.23284722
+\doi{https://doi.org/10.1101/2023.01.23.23284722}
+\url{https://www.medrxiv.org/content/10.1101/2023.01.23.23284722v1} # nolint
+}
+\keyword{check-forecasts}
diff --git a/man/transform_forecasts.Rd b/man/transform_forecasts.Rd
new file mode 100644
index 000000000..afc6c09e1
--- /dev/null
+++ b/man/transform_forecasts.Rd
@@ -0,0 +1,131 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/convenience-functions.R
+\name{transform_forecasts}
+\alias{transform_forecasts}
+\title{Transform forecasts and observed values}
+\usage{
+transform_forecasts(data, fun = log_shift, append = TRUE, label = "log", ...)
+}
+\arguments{
+\item{data}{A data.frame or data.table with the predictions and observations.
+For scoring using \code{\link[=score]{score()}}, the following columns need to be present:
+\itemize{
+\item \code{true_value} - the true observed values
+\item \code{prediction} - predictions or predictive samples for one
+true value. (You only don't need to provide a prediction column if
+you want to score quantile forecasts in a wide range format.)}
+For scoring integer and continuous forecasts a \code{sample} column is needed:
+\itemize{
+\item \code{sample} - an index to identify the predictive samples in the
+prediction column generated by one model for one true value. Only
+necessary for continuous and integer forecasts, not for
+binary predictions.}
+For scoring predictions in a quantile-format forecast you should provide
+a column called \code{quantile}:
+\itemize{
+\item \code{quantile}: quantile to which the prediction corresponds
+}
+
+In addition a \code{model} column is suggested and if not present this will be
+flagged and added to the input data with all forecasts assigned as an
+"unspecified model").
+
+You can check the format of your data using \code{\link[=check_forecasts]{check_forecasts()}} and there
+are examples for each format (\link{example_quantile}, \link{example_continuous},
+\link{example_integer}, and \link{example_binary}).}
+
+\item{fun}{A function used to transform both true values and predictions.
+The default function is \code{\link[=log_shift]{log_shift()}}, a custom function that is essentially
+the same as \code{\link[=log]{log()}}, but has two additional arguments (\code{offset} and
+\code{negative_to_zero}) that allow you to first truncate negative values to zero
+and then add an offset before applying the logarithm.}
+
+\item{append}{whether or not to append a transformed version of the data to
+the currently existing data (default is TRUE). If selected, the data gets
+transformed and appended to the existing data frame, making it possible to
+use the outcome directly in \code{\link[=score]{score()}}. An additional column, 'scale', gets
+created that denotes which rows or untransformed ('scale' has the value
+"natural") and which have been transformed ('scale' has the value passed to
+the argument \code{label}).}
+
+\item{label}{A string for the newly created 'scale' column to denote the
+newly transformed values. Only relevant if \code{append = TRUE}.}
+
+\item{...}{Additional parameters to pass to the function you supplied.}
+}
+\value{
+A data.table with either a transformed version of the data, or one
+with both the untransformed and the transformed data. includes the original data as well as a
+transformation of the original data. There will be one additional column,
+'scale', present which will be set to "natural" for the untransformed
+forecasts.
+}
+\description{
+Function to transform forecasts and true values before scoring.
+}
+\details{
+There are a few reasons, depending on the circumstances, for
+why this might be desirable (check out the linked reference for more info).
+In epidemiology, for example, it may be useful to log-transform incidence
+counts before evaluating forecasts using scores such as the weighted interval
+score (WIS) or the continuous ranked probability score (CRPS).
+Log-transforming forecasts and observations changes the interpretation of
+the score from a measure of absolute distance between forecast and
+observation to a score that evaluates a forecast of the exponential growth
+rate. Another motivation can be to apply a variance-stabilising
+transformation or to standardise incidence counts by population.
+
+Note that if you want to apply a transformation, it is important to transform
+the forecasts and observations and then apply the score. Applying a
+transformation after the score risks losing propriety of the proper scoring
+rule.
+}
+\examples{
+
+library(magrittr) # pipe operator
+
+# add log transformed forecasts (produces a warning as some values are zero)
+example_quantile \%>\%
+  .[, true_value := ifelse(true_value < 0, 0, true_value)] \%>\%
+  transform_forecasts(append = FALSE)
+
+# alternatively:
+transform_forecasts(example_quantile, negative_to_zero = TRUE, append = FALSE)
+
+# specifying an offset manually for the log transformation removes the warning
+example_quantile \%>\%
+  .[, true_value := ifelse(true_value < 0, 0, true_value)] \%>\%
+  transform_forecasts(offset = 1, append = FALSE)
+
+# truncating forecasts manually before sqrt
+example_quantile \%>\%
+  .[, true_value := ifelse(true_value < 0, 0, true_value)] \%>\%
+  transform_forecasts(fun = sqrt, label = "sqrt")
+
+# alternatively, this achieves the same
+example_quantile \%>\%
+  transform_forecasts(fun = function(x) pmax(0, x), append = FALSE) \%>\%
+  transform_forecasts(fun = sqrt, label = "sqrt")
+
+# adding multiple transformations
+library(magrittr) # pipe operator
+example_quantile \%>\%
+  .[, true_value := ifelse(true_value < 0, 0, true_value)] \%>\%
+  transform_forecasts(offset = 1) \%>\%
+  transform_forecasts(fun = sqrt, label = "sqrt") \%>\%
+  score() \%>\%
+  summarise_scores(by = c("model", "scale"))
+}
+\references{
+Transformation of forecasts for evaluating predictive
+performance in an epidemiological context
+Nikos I. Bosse, Sam Abbott, Anne Cori, Edwin van Leeuwen, Johannes Bracher,
+Sebastian Funk
+medRxiv 2023.01.23.23284722
+\doi{https://doi.org/10.1101/2023.01.23.23284722}
+\url{https://www.medrxiv.org/content/10.1101/2023.01.23.23284722v1} # nolint
+}
+\author{
+Nikos Bosse \email{nikosbosse@gmail.com}
+}
+\keyword{check-forecasts}
diff --git a/tests/testthat/test-convenience-functions.R b/tests/testthat/test-convenience-functions.R
new file mode 100644
index 000000000..b4bdecc58
--- /dev/null
+++ b/tests/testthat/test-convenience-functions.R
@@ -0,0 +1,38 @@
+test_that("function transform_forecasts works", {
+
+  predictions_original <- example_quantile$prediction
+  predictions <- transform_forecasts(
+    example_quantile,
+    fun = function(x) pmax(0, x),
+    append = FALSE
+  )
+
+  expect_equal(predictions$prediction, pmax(0, predictions_original))
+
+  one <- transform_forecasts(predictions, offset = 1)
+  expect_equal(one$prediction,
+               c(predictions$prediction, log(predictions$prediction + 1)))
+
+  two <- transform_forecasts(predictions, fun = sqrt, label = "sqrt")
+  expect_equal(two$prediction,
+               c(predictions$prediction, sqrt(predictions$prediction)))
+
+
+  # expect a warning if existing transformation is overwritten
+  expect_warning(
+    transform_forecasts(one, fun = sqrt)
+  )
+
+  # multiple transformations
+  three <- transform_forecasts(one, fun = sqrt, label = "sqrt")
+  expect_equal(unique(three$scale), c("natural", "log", "sqrt"))
+
+  # multiple transformations without append
+  four <- transform_forecasts(two, fun = log_shift, offset = 1, append = FALSE)
+  compare <- c(
+    one$prediction[one$scale == "log"],
+    three$prediction[three$scale == "sqrt"]
+  )
+
+  expect_equal(four$prediction, compare)
+})