scoringutils
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Utilities for Scoring and Assessing Predictions
We have this visualisation, which among others makes the point that you need quite a lot of samples for the sample-based log score to be reliable.  It was previously...
We have this nice illustration:  It's not used in the manuscript anymore. But it would be nice to use it in a vignette explaining differences between scores. Related: #933,...
We have this illustration:  Generated by this code: ``` library(ggplot2) library(patchwork) library(scoringutils) p1
`quantile_to_interval_numeric()`, for example, makes use of an internal `forecast_id` column. This might cause issues if there is already a `forecast_id` column in the input. We should likely prefix all internal...
(but happy to leave as is and we should maybe just open an issue to get rid of `check_columns_present` altogether) _Originally posted by @nikosbosse in https://github.com/epiforecasts/scoringutils/pull/915#discussion_r1768078954_ Seems like functionality can...
As mentioned in https://github.com/hubverse-org/hubEvals/pull/46 do we want to have any kind of special metric function factories for generating things like multiple interval coverages? I think the answer is potentially with...
Maybe something like the following would be nice. Normal output ``` ex