update news file

NEWS.md

@@ -11,7 +11,7 @@ of our [original](https://doi.org/10.48550/arXiv.2205.07090) `scoringutils` pape
 
 ### `score()`
 - The main function of the package is still the function `score()`. However, we reworked the function and updated and clarified its input requirements. 
-  - The previous columns "true_value" and "prediction" were renamed. `score()` now requires columns called "observed" and "predicted" and "model". The column `quantile` was renamed to `quantile_level` and `sample` was renamed to `sample_id`
+  - The previous columns "true_value" and "prediction" were renamed. `score()` now requires columns called "observed" and "predicted" (some functions still assume the existence of a `model` column as default but this is not a strict requirement). The column `quantile` was renamed to `quantile_level` and `sample` was renamed to `sample_id`
   - `score()` is now a generic. It has S3 methods for the classes `forecast_point`, `forecast_binary`, `forecast_quantile`,  `forecast_sample`, and `forecast_nominal`, which correspond to the different forecast types that can be scored with `scoringutils`. 
   - `score()` now calls `na.omit()` on the data, instead of only removing rows with missing values in the columns `observed` and `predicted`. This is because `NA` values in other columns can also mess up e.g. grouping of forecasts according to the unit of a single forecast.
   - `score()` and many other functions now require a validated `forecast` object. `forecast` objects can be created using the functions `as_forecast_point()`, `as_forecast_binary()`, `as_forecast_quantile()`, and `as_forecast_sample()` (which replace the previous `check_forecast()`). A forecast object is a data.table with class `forecast` and an additional class corresponding to the forecast type (e.g. `forecast_quantile`).