Issue #310 - Improve imputation of median in `bias_quantile()`

[nikosbosse]

Description

This PR closes #310.

As suggested by @sbfnk in #310, this PR improves the way we impute the median in bias_quantile() when it's missing. Previously, we took a simple mean of the two innermost quantiles. Now, we do a linear interpolation between the two, handling cases in which the two innermost quantiles are not symmetric around the median.

This PR

• make the change from simple mean to linear interpolation
• refactors the code such that the interpolation sits in its own function to make testing easier
• adds tests
• updates the news file

I think I did everything correctly. But also this is a late night PR, so I'd appreciate an extra pair of eyes on the interpolation logic.

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.

[nikosbosse]

Latest update:

• applied suggestions from code review (thank you!)
• resolved merge conflict in News file by adding a new section:

### 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.