From 26026263286dae4b60d144e1a39f02e7a5826fbc Mon Sep 17 00:00:00 2001
From: nikosbosse <37978797+nikosbosse@users.noreply.github.com>
Date: Thu, 31 Oct 2024 14:06:40 +0000
Subject: [PATCH] Automatic README update

---
 README.md | 28 +++++++++++++++-------------
 1 file changed, 15 insertions(+), 13 deletions(-)

diff --git a/README.md b/README.md
index 038bd7ec..61d498d3 100644
--- a/README.md
+++ b/README.md
@@ -12,11 +12,10 @@ version](https://img.shields.io/github/r-package/v/epiforecasts/scoringutils)
 downloads](http://cranlogs.r-pkg.org/badges/grand-total/scoringutils)](https://cran.r-project.org/package=scoringutils)
 <!-- badges: end -->
 
-**Note**: [This
-documentation](https://epiforecasts.io/scoringutils/dev/) refers to the
-development version of `scoringutils`. You can also view the
-[documentation of the stable
-version](https://epiforecasts.io/scoringutils/).
+**Note**: [This documentation](https://epiforecasts.io/scoringutils/)
+refers to the stable version of `scoringutils`. You can also view the
+[documentation of the development
+version](https://epiforecasts.io/scoringutils/dev/).
 
 The `scoringutils` package facilitates the process of evaluating
 forecasts in R, using a convenient and flexible `data.table`-based
@@ -77,21 +76,24 @@ probabilistic forecast for a continuous or discrete outcome variable,
 with the forecast distribution represented by a set of predictive
 quantiles. - `sample`: a probabilistic forecast for a continuous or
 discrete outcome variable, with the forecast represented by a finite set
-of samples drawn from the predictive distribution.
+of samples drawn from the predictive distribution. - `nominal`
+categorical forecast with unordered outcome possibilities
+(generalisation of binary forecasts to multiple outcomes)
 
 ### Input formats and input validation
 
 The expected input format is generally a `data.frame` (or similar) with
-required columns `observed`, `predicted`, and `model` that holds the
-forecasts and observed values. Exact requirements depend on the forecast
-type. For more information, have a look at the
+required columns `observed`, and `predicted` that holds the forecasts
+and observed values. Exact requirements depend on the forecast type. For
+more information, have a look at the
 [paper](https://drive.google.com/file/d/1URaMsXmHJ1twpLpMl1sl2HW4lPuUycoj/view?usp=drive_link),
-call `?as_forecast()`, or have a look at the example data provided in
-the package (`example_binary`, `example_point`, `example_quantile`,
-`example_sample_continuous`, `example_sample_discrete`).
+call `?as_forecast_binary`, `?as_forecast_quantile` etc., or have a look
+at the example data provided in the package (`example_binary`,
+`example_point`, `example_quantile`, `example_sample_continuous`,
+`example_sample_discrete`, `example_nominal`).
 
 Before scoring, input data needs to be validated and transformed into a
-forecast object using the function `as_forecast()`.
+forecast object using one of the `as_forecast_<type>()` functions.
 
 ``` r
 forecast_quantile <- example_quantile |>