hubEvals 0.3.0

• score_model_out() now handles single-model input gracefully when relative metrics are requested. Previously this errored via scoringutils ("not enough comparators"); now the relative-skill columns are filled with 1, matching the trivial fact that a model has skill 1 relative to itself. If a baseline is supplied that does not match the lone model, score_model_out() errors with a clear message (#75).

• New "Getting started with hubEvals" vignette walking through the main scoring workflows for each supported output type (quantile, mean, median, pmf nominal/ordinal, sample marginal/compound), the relative_metrics and baseline arguments for relative-skill scoring, and the transform and transform_append arguments for scoring on transformed scales (#38).

• Fix score_model_out() so that requesting transform_append = TRUE with default summarize = TRUE now correctly returns one row per scale (natural and transformed) per model, instead of silently averaging across scales (#122).

• score_model_out() now errors with a clear hubEvals message when "bias" is requested as a relative metric, instead of letting scoringutils fail downstream with a cryptic "all values must have the same sign" error. Bias is a signed quantity, so a geometric-mean pairwise ratio has no clean interpretation (#119).

• score_model_out() now returns a tibble (inheriting from scoringutils' scores class) instead of a data.table. This gives more predictable user-facing behaviour (e.g. with $ access, printing, and dplyr) while keeping the scores class so downstream scoringutils helpers like get_metrics() continue to work (#70).

• score_model_out() now errors when no requested metric produces a score.

• Fix transform_quantile_model_out(), transform_point_model_out(), and transform_sample_model_out() to handle oracle outputs that carry an output_type_id column without an output_type column. Previously, this combination caused as_forecast_*() to error on a stray output_type_id (#73).