# scoringutils 2.2.0

- `get_pairwise_comparisons()` now works with only two models when a baseline is specified, instead of requiring at least three (#1022).
- `score()` now warns when column names in the input data clash with metric names, as these columns are overwritten during scoring (#382).
- The print method for multivariate forecasts now displays the `joint_across` columns, making it easier to see which variables are forecast jointly (#1043).
- Renamed internal functions `assert_dims_ok_point()` and `check_dims_ok_point()` to `assert_dims_ok_scalar()` and `check_dims_ok_scalar()` to reflect that they are used by both point and binary forecasts, not just point (#938).
- Added `forecast_multivariate_point` class for scoring multivariate point forecasts with the variogram score.
The new `as_forecast_multivariate_point()` constructor takes a `joint_across` argument, matching the pattern of `as_forecast_multivariate_sample()`.
The `transform_forecasts()` function now works with both multivariate forecast types (#1112).
- Added the variogram score (`variogram_score_multivariate()`) as a default metric for multivariate sample forecasts.
The variogram score (Scheuerer and Hamill, 2015) evaluates the dependence structure of multivariate forecasts and is more sensitive to correlation misspecification than the energy score (#1111).
- Fixed `score()` dropping the `scale` column for multivariate forecasts when using `transform_forecasts(append = TRUE)`. The issue was caused by stale `.mv_group_id` values not being recomputed after appending transformed data (#1108).
- Fixed a naming inconsistency where the class `forecast_sample_multivariate` did not match the constructor `as_forecast_multivariate_sample()`. The class has been renamed to `forecast_multivariate_sample`. The old class name is retained in the class vector during the deprecation period so `inherits(x, "forecast_sample_multivariate")` still works. `is_forecast_sample_multivariate()` is deprecated in favour of `is_forecast_multivariate_sample()`. This also fixes `transform_forecasts(append = TRUE)` which previously failed on multivariate sample forecasts (#1071).
- Added support for scoring multivariate forecasts (#288, big thank you to Sam Abbott and Sebastian Funk). You can find detailed information in the Vignette "Scoring multivariate forecasts". There is a new forecast type, `forecast_multivariate_sample` and a corresponding `as_forecast_multivariate_sample()` function. To score a multivariate forecast, users are expected to provide a `joint_across` argument which specifies the variables which are forecast jointly.
- Fixed a small bug with `bias_sample()` when continuous predictions were equal to observations. These ties could lead to incorrect bias scores. New calculations now use mid-ranks to deal with ties.
- Removed workaround for `{distributional}` native pipe issue which has now been fixed upstream (#947).

# scoringutils 2.1.2

- fixed an issue that could arise with small rounding errors in quantile-based forecasts. This happened when there were quantile_levels like 0.5, and 0.5 + 1e-16 present at the same time. `scoringutils` now warns the user of the issue and automatically rounds all quantile levels to 10 digits.
- updated a few example plots to comply with an updated ggplot2 requirement to name labels in `labs()` explicitly.

# scoringutils 2.1.1

- The package now depends on R version at least 4.1.0 as a result of downstream dependencies.
- Added tolerance for numeric errors when checking that probabilities sum to one in ordinal forecasts (#997)
- Made computation of p-values optional in pairwise comparisons by allowing `test_type = NULL` in `compare_forecasts()`. When `test_type = NULL`, p-values will be `NA` (#978).
- Added a dependency on a scoringRules version >= 1.1.3 which is required for ordinal forecasts and the `rps_probs()` function (#1006,#1011).
- A bug was fixed in PIT histograms from continuous values (#1010). The bug caused all PIT histograms to use the discrete version, even with continuous data. In the calculations, values were also rounded inappropriately.

# scoringutils 2.1.0

Minor spelling / mathematical updates to Scoring rule vignette. (#969)

## Package updates

- A bug was fixed where `crps_sample()` could fail in edge cases.
- All `as_forecast_<type>()` functions now have S3 methods for converting from another forecast type to the respective forecast type.
- Implemented a new forecast class, `forecast_ordinal` with appropriate metrics. Ordinal forecasts are a form of categorical forecasts. The main difference between ordinal and nominal forecasts is that the outcome is ordered, rather than unordered.
- Refactored the way that columns get internally renamed in `as_forecast_<type>()` functions (#980)
- The package now correctly specifies a dependency on `data.table` >= 1.16.0.

# scoringutils 2.0.0

This update represents a major rewrite of the package and introduces breaking changes. If you want to keep using the older version, you can download it using `remotes::install_github("epiforecasts/scoringutils@v1.2")`.

The update aims to make the package more modular and customisable and overall cleaner and easier to work with. In particular, we aimed to make the suggested workflows for evaluating forecasts more explicit and easier to follow (see visualisation below). To do that, we clarified input formats and made them consistent across all functions. We refactord many functions to S3-methods and introduced `forecast` objects with separate classes for different types of forecasts. A new set of `as_forecast_<type>()` functions was introduced to validate the data and convert inputs into a `forecast` object (a `data.table` with a `forecast` class and an additional class corresponding to the forecast type (see below)). Another major update is the possibility for users to pass in their own scoring functions into `score()`. We updated and improved all function documentation and added new vignettes to guide users through the package. Internally, we refactored the code, improved input checks, updated notifications (which now use the `cli` package) and increased test coverage.

The most comprehensive documentation for the new package after the rewrite is the [revised version](https://drive.google.com/file/d/1URaMsXmHJ1twpLpMl1sl2HW4lPuUycoj/view?usp=drive_link)
of our [original](https://doi.org/10.48550/arXiv.2205.07090) `scoringutils` paper.

## Package updates

### `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" (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`).
  `score()` now returns objects of class `scores` with a stored attribute `metrics` that holds the names of the scoring rules that were used. Users can call `get_metrics()` to access the names of those scoring rules.
  - `score()` now returns one score per forecast, instead of one score per sample or quantile. For binary and point forecasts, the columns "observed" and "predicted" are now removed for consistency with the other forecast types.
  - Users can now also use their own scoring rules (making use of the `metrics` argument, which takes in a named list of functions). Default scoring rules can be accessed using the function `get_metrics()`, which is a a generic with S3 methods for each forecast type. It returns a named list of scoring rules suitable for the respective forecast object. For example, you could call `get_metrics(example_quantile)`. Column names of scores in the output of `score()` correspond to the names of the scoring rules (i.e. the names of the functions in the list of metrics).
  - Instead of supplying arguments to `score()` to manipulate individual scoring rules users should now manipulate the metric list being supplied using `purrr::partial()` and `select_metric()`. See `?score()` for more information.
  - the CRPS is now reported as decomposition into dispersion, overprediction and underprediction.
  - functionality to calculate the Probability Integral Transform (PIT) has been deprecated and replaced by functionality to calculate PIT histograms, using the `get_pit_histogram()` function; as part of this change, nonrandomised PITs can now be calculated for count data, and this is is done by default

### 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. 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:
  ```
  forecast_quantile <- as_forecast_quantile(
    example_quantile,
    observed = "observed",
    predicted = "predicted",
    model = "model",
    quantile_level = "quantile_level",
    forecast_unit = c("model", "location", "target_end_date", "forecast_date", "target_type")
  )
  scores <- score(forecast_quantile)
  ```
- Overall, we updated the suggested workflows for how users should work with the package. The following gives an overview (see the  [updated paper](https://drive.google.com/file/d/1URaMsXmHJ1twpLpMl1sl2HW4lPuUycoj/view?usp=drive_link) for more details).
  ![package workflows](./man/figures/workflow.png)

### Input formats
- We standardised input formats both for `score()` as well as for the scoring rules exported by `scoreingutils`. The following plot gives a overview of the expected input formats for the different forecast types in `score()`.
![input formats](./man/figures/required-inputs.png)

- Support for the interval format was mostly dropped (see PR #525 by @nikosbosse and reviewed by @seabbs). The co-existence of the quantile and interval format let to a confusing user experience with many duplicated functions providing the same functionality. We decided to simplify the interface by focusing exclusively on the quantile format.
    - The function `bias_range()` was removed (users should now use `bias_quantile()` instead)
    - The function `interval_score()` was made an internal function rather than being exported to users. We recommend using `wis()` instead.

### (Re-)Validating forecast objects
- To create and validate a new `forecast` object, users can use `as_forecast_<type>()`. To revalidate an existing `forecast` object users can call `assert_forecast()` (which validates the input and returns `invisible(NULL)`. `assert_forecast()` is a generic with methods for the different forecast types. Alternatively, users can call ``as_forecast_<type>()` again to re-validate a forecast object. Simply printing the object will also provide some additional information.
- Users can test whether an object is of class `forecast_*()` using the function `is_forecast()`. Users can also test for a specific `forecast_*` class using the appropriate `is_forecast.forecast_*` method. For example, to check whether an object is of class `forecast_quantile`, you would use you would use `scoringutils:::is_forecast.forecast_quantile()`.

### Pairwise comparisons and relative skill
- The functionality for computing pairwise comparisons was now split from `summarise_scores()`. Instead of doing pairwise comparisons as part of summarising scores, a new function, `add_relative_skill()`, was introduced that takes summarised scores as an input and adds columns with relative skill scores and scaled relative skill scores.
- The function `pairwise_comparison()` was renamed to `get_pairwise_comparisons()`, in line with other `get_`-functions. Analogously, `plot_pairwise_comparison()` was renamed to `plot_pairwise_comparisons()`.
- Output columns for pairwise comparisons have been renamed to contain the name of the metric used for comparing.
- Replaced warnings with errors in `get_pairwise_comparison` to avoid returning `NULL`

### Computing coverage values
- `add_coverage()` was replaced by a new function, `get_coverage()`. This function comes with an updated workflow where coverage values are computed directly based on the original data and can then be visualised using `plot_interval_coverage()` or `plot_quantile_coverage()`. An example workflow would be `example_quantile |> as_forecast_quantile() |> get_coverage(by = "model") |> plot_interval_coverage()`.

### Obtaining and plotting forecast counts
- The function `avail_forecasts()` was renamed to `get_forecast_counts()`. This represents a change in the naming convention where we aim to name functions that provide the user with additional useful information about the data with a prefix "get_". Sees Issue #403 and #521 and PR #511 by @nikosbosse and reviewed by @seabbs for details.
  - For clarity, the output column in `get_forecast_counts()` was renamed from "Number forecasts" to "count".
  - `get_forecast_counts()` now also displays combinations where there are 0 forecasts, instead of silently dropping corresponding rows.
  - `plot_avail_forecasts()` was renamed `plot_forecast_counts()` in line with the change in the function name. The `x` argument no longer has a default value, as the value will depend on the data provided by the user.

### Renamed functions
- The function `find_duplicates()` was renamed to `get_duplicate_forecasts()`.
- Renamed `interval_coverage_quantile()` to `interval_coverage()`.
- "range" was consistently renamed to "interval_range" in the code. The "range"-format (which was mostly used internally) was renamed to "interval"-format
- Renamed `correlation()` to `get_correlations()` and `plot_correlation()` to `plot_correlations()`

### Deleted functions
- Removed abs_error and squared_error from the package in favour of `Metrics::ae` and `Metrics::se`.`get_duplicate_forecasts()` now sorts outputs according to the forecast unit, making it easier to spot duplicates. In addition, there is a `counts` option that allows the user to display the number of duplicates for each forecast unit, rather than the raw duplicated rows.
- Deleted the function `plot_ranges()`. If you want to continue using the functionality, you can find the function code [here](https://github.com/epiforecasts/scoringutils/issues/462) or in the Deprecated-visualisations Vignette.
- Removed the function `plot_predictions()`, as well as its helper function `make_NA()`, in favour of a dedicated Vignette that shows different ways of visualising predictions. For future reference, the function code can be found [here](https://github.com/epiforecasts/scoringutils/issues/659) (Issue #659) or in the Deprecated-visualisations Vignette.
- Removed the function `plot_score_table()`. You can find the code in the Deprecated-visualisations Vignette.
- Removed the function `merge_pred_and_obs()` that was used to merge two separate data frames with forecasts and observations. We moved its contents to a new "Deprecated functions"-vignette.
- Removed `interval_coverage_sample()` as users are now expected to convert to a quantile format first before scoring.
- Function `set_forecast_unit()` was deleted. Instead there is now a `forecast_unit` argument in `as_forecast_<type>()` as well as in `get_duplicate_forecasts()`.
- Removed `interval_coverage_dev_quantile()`. Users can still access the difference between nominal and actual interval coverage using `get_coverage()`.
- `pit()`, `pit_sample()` and `plot_pit()` have been removed and replaced by functionality to create PIT histograms (`pit_histogram_sampel()` and `get_pit_histogram()`)

### Function changes
- `bias_quantile()` changed the way it handles forecasts where the median is missing: The median is now imputed by linear interpolation between the innermost quantiles. Previously, we imputed the median by simply taking the mean of the innermost quantiles.
- In contrast to the previous `correlation` function, `get_correlations` doesn't round correlations by default. Instead, `plot_correlations` now has a `digits` argument that allows users to round correlations before plotting them. Alternatively, using `dplyr`, you could call something like `mutate(correlations, across(where(is.numeric), \(x) signif(x, digits = 2)))` on the output of `get_correlations`.
- `wis()` now errors by default if not all quantile levels form valid prediction intervals and returns `NA` if there are missing values. Previously, `na.rm` was set to `TRUE` by default, which could lead to unexpected results, if users are not aware of this.

### Internal package updates
- 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()`.
- Added a subsetting `[` operator for scores, so that the score name attribute gets preserved when subsetting.
- Switched to using `cli` for condition handling and signalling, and added tests for all the `check_*()` and `test_*()` functions. See #583 by @jamesmbaazam and reviewed by @nikosbosse and @seabbs.
- `scoringutils` now requires R >= 4.0


### Documentation and testing
- Updates documentation for most functions and made sure all functions have documented return statements
- Documentation pkgdown pages are now created both for the stable and dev versions.
- Added unit tests for many functions

# scoringutils 1.2.2

## Package updates
- `scoringutils` now depends on R 3.6. The change was made since packages `testthat` and `lifecycle`, which are used in `scoringutils` now require R 3.6. We also updated the Github action CI check to work with R 3.6 now.
- Added a new PR template with a checklist of things to be included in PRs to facilitate the development and review process

## Bug fixes
- Fixes a bug with `set_forecast_unit()` where the function only worked with a data.table, but not a data.frame as an input.
- The metrics table in the vignette [Details on the metrics implemented in `scoringutils`](https://epiforecasts.io/scoringutils/articles/metric-details.html) had duplicated entries. This was fixed by removing the duplicated rows.

# scoringutils 1.2.1

## Package updates
- This minor update fixes a few issues related to gh actions and the vignettes displayed at epiforecasts.io/scoringutils. It
  - Gets rid of the preferably package in _pkgdown.yml. The theme had a toggle between light and dark theme that didn't work properly
  - Updates the gh pages deploy action to v4 and also cleans up files when triggered
  - Introduces a gh action to automatically render the Readme from Readme.Rmd
  - Removes links to vignettes that have been renamed

# scoringutils 1.2.0

This major release contains a range of new features and bug fixes that have been introduced in minor releases since `1.1.0`. The most important changes are:

- Documentation updated to reflect changes since version 1.1.0, including new transform and workflow functions.
- New `set_forecast_unit()` function allows manual setting of forecast unit.
- `summarise_scores()` gains new `across` argument for summarizing across variables. EDIT: This has since been removed again in [PR #831](https://github.com/epiforecasts/scoringutils/pull/831).
- New `transform_forecasts()` and `log_shift()` functions allow forecast transformations. See the documentation for `transform_forecasts()` for more details and an example use case.
- Input checks and test coverage improved for bias functions.
- Bug fix in `get_prediction_type()` for integer matrix input.
- Links to scoringutils paper and citation updates.
- Warning added in `interval_score()` for small interval ranges.
- Linting updates and improvements.

Thanks to @nikosbosse, @seabbs, and @sbfnk for code and review contributions. Thanks to @bisaloo for the suggestion to use a linting GitHub Action that only triggers on changes, and @adrian-lison for the suggestion to add a warning to `interval_score()` if the interval range is between 0 and 1.

## Package updates

- The documentation was updated to reflect the recent changes since `scoringutils 1.1.0`. In particular, usage of the functions `set_forecast_unit()`, `check_forecasts()` and `transform_forecasts()` are now documented in the Vignettes. The introduction of these functions enhances the overall workflow and help to make the code more readable. All functions are designed to be used together with the pipe operator. For example, one can now use something like the following:

```r
example_quantile |>
  set_forecast_unit(c("model", "location", "forecast_date", "horizon", "target_type")) |>
  check_forecasts() |>
  score()
```

Documentation for the `transform_forecasts()` has also been extended. This functions allows the user to easily add transformations of forecasts, as suggested in the paper ["Scoring epidemiological forecasts on transformed scales"](https://www.medrxiv.org/content/10.1101/2023.01.23.23284722v1). In an epidemiological context, for example, it may make sense to apply the natural logarithm first before scoring forecasts, in order to obtain scores that reflect how well models are able to predict exponential growth rates, rather than absolute values. Users can now do something like the following to score a transformed version of the data in addition to the original one:

```r
data <- example_quantile[true_value > 0, ]
data |>
  transform_forecasts(fun = log_shift, offset = 1) |>
  score() |>
  summarise_scores(by = c("model", "scale"))
```

Here we use the `log_shift()` function to apply a logarithmic transformation to the forecasts. This function was introduced in `scoringutils 1.1.2` as a helper function that acts just like `log()`, but has an additional argument `offset` that can add a number to every prediction and observed value before applying the log transformation.

## Feature updates

- Made `check_forecasts()` and `score()` pipeable (see issue #290). This means that
users can now directly use the output of `check_forecasts()` as input for
`score()`. As `score()` otherwise runs `check_forecasts()` internally anyway
this simply makes the step explicit and helps writing clearer code.

# scoringutils 1.1.7

Release by @seabbs in #305. Reviewed by @nikosbosse and @sbfnk.

## Breaking changes

- The `prediction_type` argument of `get_forecast_unit()` has been changed dropped. Instead a new internal function `prediction_is_quantile()` is used to detect if a quantile variable is present. Whilst this is an internal function it may impact some users as it is accessible via `find_duplicates().

## Package updates

- Made imputation of the median in `bias_range()` and `bias_quantile()` more obvious to the user as this may cause unexpected behaviour.
- Simplified `bias_range()` so that it uses `bias_quantile()` internally.
- Added additional input checks to `bias_range()`, `bias_quantile()`, and `check_predictions()` to make sure that the input is valid.
- Improve the coverage of unit tests for `bias_range()`, `bias_quantile()`, and `bias_sample()`.
- Updated pairwise comparison unit tests to use more realistic data.

## Bug fixes

- Fixed a bug in `get_prediction_type()` which led to it being unable to correctly detect integer (instead categorising them as continuous) forecasts when the input was a matrix. This issue impacted `bias_sample()` and also `score()` when used with integer forecasts resulting in lower bias scores than expected.

# scoringutils 1.1.6

## Feature updates

- Added a new argument, `across`, to `summarise_scores()`. This argument allows the user to summarise scores across different forecast units as an alternative to specifying `by`. See the documentation for `summarise_scores()` for more details and an example use case.

# scoringutils 1.1.5

## Feature updates

- Added a new function, `set_forecast_unit()` that allows the user to set the forecast unit manually. The function removes all columns that are not relevant for uniquely identifying a single forecast. If not done manually, `scoringutils` attempts to determine the unit of a single automatically by simply assuming that all column names are relevant to determine the forecast unit. This can lead to unexpected behaviour, so setting the forecast unit explicitly can help make the code easier to debug and easier to read (see issue #268). When used as part of a workflow, `set_forecast_unit()` can be directly piped into `check_forecasts()` to check everything is in order.

# scoringutils 1.1.4

## Package updates

- Added links to the scoringutils paper [Evaluating Forecasts with scoringutils in R](https://arxiv.org/abs/2205.07090) to the package.
- Updated citation formatting to comply with newer standards.

# scoringutils 1.1.3

## Package updates

- Added a warning to `interval_score()` if the interval range is between 0 and 1. Thanks to @adrian-lison (see #277) for the suggestion.

## Package updates

- Switched to a linting GitHub Action that only triggers on changes. Inspired by @bisaloo recent contribution to the [`epinowcast` package](https://github.com/epinowcast/epinowcast/pull/220).
- Updated package linters to be more extensive. Inspired by @bisaloo recent contribution to the [`epinowcast` package](https://github.com/epinowcast/epinowcast/pull/220).
- Resolved all flagged linting issues across the package.

# 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 a function, `log_shift()` that implements the default transformation function. The function allows to 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 implicit conversion.

# scoringutils 1.1.0

A minor update to the package with some bug fixes and minor changes.

## Feature updates

## Package updates

- Removed the on attach message which warned of breaking changes in `1.0.0`.
- Renamed the `metric` argument of `summarise_scores()` to `relative_skill_metric`. This argument is now deprecated and will be removed in a future version of the package. Please use the new argument instead.
- Updated the documentation for `score()` and related functions to make the soft requirement for a `model` column in the input data more explicit.
- Updated the documentation for `score()`, `pairwise_comparison()` and `summarise_scores()` to make it clearer what the unit of a single forecast is that is required for computations
- Simplified the function `plot_pairwise_comparison()` which now only supports plotting mean score ratios or p-values and removed the hybrid option to print both at the same time.

## Bug fixes

- Missing baseline forecasts in `pairwise_comparison()` now trigger an explicit and informative error message.
- The requirements table in the getting started vignette is now correct.
- Added support for an optional `sample` column when using a quantile forecast format. Previously this resulted in an error.

# scoringutils 1.0.0

Major update to the package and most package functions with lots of breaking changes.

## Feature updates

- New and updated Readme and vignette.
- The proposed scoring workflow was reworked. Functions were changed so they
can easily be piped and have simplified arguments and outputs.

### New functions and function changes

- The function `eval_forecasts()` was replaced by a function `score()` with a much reduced set of function arguments.
- Functionality to summarise scores and to add relative skill scores was moved
to a function `summarise_scores()`
- New function `check_forecasts()` to analyse input data before scoring
- New function `correlation()` to compute correlations between different metrics
- New function `add_coverage()` to add coverage for specific central prediction intervals.
- New function `avail_forecasts()` allows to visualise the number of available forecasts.
- New function `find_duplicates()` to find duplicate forecasts which cause an error.
- All plotting functions were renamed to begin with `plot_`. Arguments were
simplified.
- The function `pit()` now works based on data.frames. The old `pit` function
was renamed to `pit_sample()`. PIT p-values were removed entirely.
- The function `plot_pit()` now works directly with input as produced by `pit()`
- Many data-handling functions were removed and input types for `score()` were
restricted to sample-based, quantile-based or binary forecasts.
- The function `brier_score()` now returns all brier scores, rather than taking
the mean before returning an output.
- `crps()`, `dss()` and `logs()` were renamed to `crps_sample()`, `dss_sample()`, and
`logs_sample()`

### Bug fixes

- Testing was expanded
- Minor bugs were fixed, for example a bug in the `as_forecast_quantile()` function
(https://github.com/epiforecasts/scoringutils/pull/223)

### Package data updated

- Package data is now based on forecasts submitted to the European Forecast Hub
(https://covid19forecasthub.eu/).
- All example data files were renamed to begin with `example_`.
- A new data set, `summary_metrics` was included that contains a summary of the metrics implemented in `scoringutils`.

## Other breaking changes

- The 'sharpness' component of the weighted interval score was renamed to dispersion. This was done to make it more clear what the component represents and to maintain consistency with what is used in other places.

# scoringutils 0.1.8

## Feature updates

- Added a function `check_forecasts()` that runs some basic checks on the
input data and provides feedback.

# scoringutils 0.1.7.2

## Package updates

- Minor bug fixes (previously, 'interval_score' needed to be among the selected metrics).
- All data.tables are now returned as `table[]` rather than as `table`, such that they don't have to be called twice to display the contents.

# scoringutils 0.1.7

## Feature updates

- Added a function, `pairwise_comparison()` that runs pairwise comparisons between models on the output of `eval_forecasts()`
- Added functionality to compute relative skill within `eval_forecasts()`.
- Added a function to visualise pairwise comparisons.

## Package updates

- The WIS definition change introduced in version 0.1.5 was partly corrected such that the difference in weighting is only introduced when summarising over scores from different interval ranges.
- "sharpness" was renamed to 'mad' in the output of [score()] for sample-based
forecasts.

# scoringutils 0.1.

## Feature updates

- `eval_forecasts()` can now handle a separate forecast and truth data set as
as input.
- `eval_forecasts()` now supports scoring point forecasts along side quantiles
in a quantile-based format. Currently the only metric used is the absolute error.

## Package updates

- Many functions, especially `eval_forecasts()` got a major rewrite. While
functionality should be unchanged, the code should now be easier to maintain
- Some of the data-handling functions got renamed, but old names are supported
as well for now.

# scoringutils 0.1.5

## Package updates

- Changed the default definition of the weighted interval score. Previously,
the median prediction was counted twice, but is no only counted once. If you
want to go back to the old behaviour, you can call the interval_score function
with the argument `count_median_twice = FALSE`.

# scoringutils 0.1.4

## Feature updates

- Added basic plotting functionality to visualise scores. You can now
easily obtain diagnostic plots based on scores as produced by `score`.
- `correlation_plot()` shows correlation between metrics.
- `plot_ranges()` shows contribution of different prediction intervals to some chosen metric.
- `plot_heatmap()` visualises scores as heatmap.
- `plot_score_table()` shows a coloured summary table of scores.

## package updates

- Renamed "calibration" to "coverage".
- Renamed "true_values" to "true_value" in data.frames.
- Renamed "predictions" to "prediction" in data.frames.
- Renamed "is_overprediction" to "overprediction".
- Renamed "is_underprediction" to "underprediction".

# scoringutils 0.1.3

## (Potentially) Breaking changes

- The by argument in `score` now has a slightly changed meaning. It now denotes the lowest possible grouping unit, i.e. the unit of one observation and needs to be specified explicitly. The default is now `NULL`. The reason for
this change is that most metrics need scoring on the observation level and this the most consistent implementation of this principle. The pit function receives
its grouping now from `summarise_by`. In a similar spirit, `summarise_by` has to
be specified explicitly and e.g. doesn't assume anymore that you want 'range'
to be included.
- For the interval score, `weigh = TRUE` is now the default option.
- Renamed true_values to true_value and predictions to prediction.

## Feature updates

- Updated quantile evaluation metrics in `score`. Bias as well as calibration now take all quantiles into account.
- Included option to summarise scores according to a `summarise_by` argument in `score()` The summary can return the mean, the standard deviation as well
as an arbitrary set of quantiles.
- `score()` can now return pit histograms.
- Switched to `ggplot2` for plotting.

# scoringutils 0.1.2

## (Potentially) Breaking changes

- All scores in score were consistently renamed to lower case. `Interval_score` is now `interval_score`, `CRPS` is now `crps` etc.

## Feature updates

- Included support for grouping scores according to a vector of column names
in `score()`.
- Included support for passing down arguments to lower-level functions in `score()`
- Included support for three new metrics to score quantiles with `score()`: bias, sharpness and calibration

## Package updates

- Example data now has a horizon column to illustrate the use of grouping.
- Documentation updated to explain the above listed changes.

# scoringutils 0.1.1

## Feature updates

- Included support for a long as well as wide input formats for quantile forecasts that are scored with `score()`.

## Package updates

- Updated documentation for the `score()`.
- Added badges to the `README`.
