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).
Add support for scoring sample output types via
transform_sample_model_out() and the "sample"
case in score_model_out(). Marginal scoring produces
metrics such as CRPS, bias, and DSS; compound scoring (via the new
compound_taskid_set argument) produces multivariate scores
such as energy score and variogram score for joint forecasts
(#94).
score_model_out() now errors with a clear message
when a scale transformation produces non-finite values (NaN or Inf),
instead of silently returning invalid scores (#99).
transform, transform_append, and
transform_label arguments to score_model_out()
for computing scores on transformed scales (e.g., log, sqrt). Supported
for quantile, mean, and median output types (#48, #91).transform_pmf_model_out(),
transform_point_model_out(), and
transform_quantile_model_out() used to transform hubverse
model outputs into a scoringutils forecast objectscore_model_out() function for evaluating model
outputs