Issue #604 - Add support for nominal forecasts

Description

This PR closes #604.

Nominal forecasts are forecasts for outcomes that can fall in one of several unordered categories. This PR implements support for nominal forecasts (see #604, #607, and #608).

Specifically, the PR

creates a new nominal_forecast class with
- an assert_input_nominal function that checks the inputs passed to a scoring function
- a check_input_nominal, doing the same thing without producing an error
- an assert_forecast.forecast_nominal function, checking that a data.table is complying with the required input format
- a default list of metrics, provided via metrics_nominal
- a new method score.forecast_nominal
adds new example data
updates as_forecast() to accept a new predicted_label argument.
updates get_forecast_type() and adds a check function to make sure that the forecast type is nominal
implements the log score for nominal forecasts
adds tests

Note: Throughout the process, I noticed that sadly, scoringutils is currently not "easily extensible"... To make this go smoothly, there are quite a few hoops. Some of this will be simplified in the future when we implement a separate as_forecast_nominal() function instead of a single as_forecast() function that has to do all the guesswork.

Still missing (likely for a future PR)

Updating the manuscript to include nominal forecasts
other kinds of docs
- Creating a vignette that walks through a hubVerse example
A helper function that completes the forecast such that users don't have to specify every single option (see #608)

One current code example:

# remotes::install_github("epiforecasts/scoringutils@multiclass")
library(dplyr)
library(hubExamples)
library(scoringutils)

pred <- hubExamples::forecast_outputs |> filter(output_type == "pmf")
obs <- hubExamples::forecast_target_observations |> 
  dplyr::filter(output_type == "pmf")
hubex <- dplyr::full_join(pred, obs)

hubex |> 
  dplyr::group_by(model_id, location, reference_date, horizon, target_end_date, target, output_type) |>
  dplyr::mutate(
    observation = output_type_id[observation == 1], 
    observation = factor(observation, levels = c("low", "moderate", "high", "very high")), 
    output_type_id = factor(output_type_id, levels =  c("low", "moderate", "high", "very high"))) |>
  as_forecast(
    model = "model_id", observed = "observation", 
    predicted = "value", predicted_label = "output_type_id"
  ) |> 
  score()

Checklist

[x] My PR is based on a package issue and I have explicitly linked it.
[x] I have included the target issue or issues in the PR title as follows: issue-number: PR title
[x] I have tested my changes locally.
[x] I have added or updated unit tests where necessary.
[x] I have updated the documentation if required.
[x] I have built the package locally and run rebuilt docs using roxygen2.
[x] My code follows the established coding standards and I have run lintr::lint_package() to check for style issues introduced by my changes.
[x] I have added a news item linked to this PR.
[x] I have reviewed CI checks for this PR and addressed them as far as I am able.

Codecov Report

Attention: Patch coverage is 73.62637% with 24 lines in your changes missing coverage. Please review.

Project coverage is 94.25%. Comparing base (b58434e) to head (3ba8a46). Report is 19 commits behind head on main.

Files	Patch %	Lines
R/score.R	0.00%	16 Missing :warning:
R/default-scoring-rules.R	0.00%	4 Missing :warning:
R/check-inputs-scoring-functions.R	92.00%	2 Missing :warning:
R/forecast.R	96.15%	1 Missing :warning:
R/metrics-nominal.R	87.50%	1 Missing :warning:

Additional details and impacted files

```diff @@ Coverage Diff @@ ## main #837 +/- ## ========================================== - Coverage 95.53% 94.25% -1.28% ========================================== Files 21 22 +1 Lines 1569 1655 +86 ========================================== + Hits 1499 1560 +61 - Misses 70 95 +25 ```

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epiforecasts / scoringutils