scoringutils 1.1.0
A minor update to the package with some bug fixes and minor
changes.
Feature updates
Package updates
- Removed the on attach message which warned of breaking changes in
1.0.0.
- Renamed the
metric argument of
summarise_scores() to relative_skill_metric.
This argument is now deprecated and will be removed in a future version
of the package. Please use the new argument instead.
- Updated the documentation for
score() and related
functions to make the soft requirement for a model column
in the input data more explicit.
- Updated the documentation for
score(),
pairwise_comparison() and summaris_scores() to
make it clearer what the unit of a single forecast is that is required
for computations
- Simplified the function
plot_pairwise_comparison()
which now only supports plotting mean score ratios or p-values and
removed the hybrid option to print both at the same time.
Bug fixes
- Missing baseline forecasts in
pairwise_comparison() now
trigger an explicit and informative error message.
- The requirements table in the getting started vignette is now
correct.
- Added support for an optional
sample column when using
a quantile forecast format. Previously this resulted in an error.
scoringutils 1.0.0
Major update to the package and most package functions with lots of
breaking changes.
Feature updates
- new and updated Readme and vignette
- the proposed scoring workflow was reworked. Functions were changed
so they can easily be piped and have simplified arguments and
outputs.
new functions and function
changes
- the function
eval_forecasts() was replaced by a
function score() with a much reduced set of function
arguments.
- Functionality to summarise scores and to add relative skill scores
was moved to a function
summarise_scores()
- new function
check_forecasts() to analyse input data
before scoring
- new function
correlation() to compute correlations
between different metrics
- new function
add_coverage() to add coverage for
specific central prediction intervals
- new function
avail_forecasts() allows to visualise the
number of available forecasts
- new function
find_duplicates() to find duplicate
forecasts which cause an error
- all plotting functions were renamed to begin with
plot_. Arguments were simplified
- the function
pit() now works based on data.frames. The
old pit function was renamed to pit_sample().
PIT p-values were removed entirely.
- the function
plot_pit() now works directly with input
as produced by pit()
- many data-handling functions were removed and input types for
score() were restricted to sample-based, quantile-based or
binary forecasts.
- the function
brier_score() now returns all brier
scores, rather than taking the mean before returning an output.
crps, dss and logs were
renamed to crps_sample(), dss_sample(), and
logs_sample()
Bug fixes
- Testing was expanded
- minor bugs were fixed, for example a bug in the sample_to_quantile
function (https://github.com/epiforecasts/scoringutils/pull/223)
package data updated
- package data is now based on forecasts submitted to the European
Forecast Hub (https://covid19forecasthub.eu/).
- all example data files were renamed to begin with
example_
- a new data set,
summary_metrics was included that
contains a summary of the metrics implemented in
scoringutils
Other breaking changes
- The ‘sharpness’ component of the weighted interval score was renamed
to dispersion. This was done to make it more clear what the component
represents and to maintain consistency with what is used in other
places.
scoringutils 0.1.8
Feature updates
- now added a function
check_forecasts() that runs some
basic checks on the input data and provides feedback
scoringutils 0.1.7.2
Package updates
- minor bug fixes (previously, ‘interval_score’ needed to be among the
selected metrics)
- all data.tables are now returned as
table[] rather than
as table, such that they don’t have to be called twice to
display the contents.
scoringutils 0.1.7
Feature updates
- added a function,
pairwise_comparison() that runs
pairwise comparisons between models on the output of
eval_forecasts()
- added functionality to compute relative skill within
eval_forecasts()
- added a function to visualise pairwise comparisons
Package updates
- The WIS definition change introduced in version 0.1.5 was partly
corrected such that the difference in weighting is only introduced when
summarising over scores from different interval ranges
- “sharpness” was renamed to ‘mad’ in the output of [score()] for
sample-based forecasts.
scoringutils 0.1.
Feature updates
eval_forecasts() can now handle a separate forecast and
truth data set as as inputeval_forecasts() now supports scoring point forecasts
along side quantiles in a quantile-based format. Currently the only
metric used is the absolute error
Package updates
- Many functions, especially
eval_forecasts() got a major
rewrite. While functionality should be unchanged, the code should now be
easier to maintain
- Some of the data-handling functions got renamed, but old names are
supported as well for now.
scoringutils 0.1.5
Package updates
- changed the default definition of the weighted interval score.
Previously, the median prediction was counted twice, but is no only
counted once. If you want to go back to the old behaviour, you can call
the interval_score function with the argument
count_median_twice = FALSE.
scoringutils 0.1.4
Feature updates
- we added basic plotting functionality to visualise scores. You can
now easily obtain diagnostic plots based on scores as produced by
score.
correlation_plot shows correlation between metricsplot_ranges shows contribution of different prediction
intervals to some chosen metricplot_heatmap visualises scores as heatmapplot_score_table shows a coloured summary table of
scores
package updates
- renamed “calibration” to “coverage”
- renamed “true_values” to “true_value” in data.frames
- renamed “predictions” to “prediction” in data.frames
- renamed “is_overprediction” to “overprediction”
- renamed “is_underprediction” to “underprediction”
scoringutils 0.1.3
(Potentially) Breaking
changes
- the by argument in
score now has a slightly changed
meaning. It now denotes the lowest possible grouping unit, i.e. the unit
of one observation and needs to be specified explicitly. The default is
now NULL. The reason for this change is that most metrics
need scoring on the observation level and this the most consistent
implementation of this principle. The pit function receives its grouping
now from summarise_by. In a similar spirit,
summarise_by has to be specified explicitly and
e.g. doesn’t assume anymore that you want ‘range’ to be included.
- for the interval score,
weigh = TRUE is now the default
option.
- (potentially planned) rename true_values to true_value and
predictions to prediction.
Feature updates
- updated quantile evaluation metrics in
score. Bias as
well as calibration now take all quantiles into account
- Included option to summarise scores according to a
summarise_by argument in score The summary can
return the mean, the standard deviation as well as an arbitrary set of
quantiles.
score can now return pit histograms.- switched to ggplot2 for plotting
scoringutils 0.1.2
(Potentially) Breaking
changes
- all scores in score were consistently renamed to lower case.
Interval_score is now interval_score, CRPS is now crps etc.
Feature updates
- included support for grouping scores according to a vector of column
names in
score
- included support for passing down arguments to lower-level functions
in
score
- included support for three new metrics to score quantiles with
score: bias, sharpness and calibration
Package updates
- example data now has a horizon column to illustrate the use of
grouping
- documentation updated to explain the above listed changes
scoringutils 0.1.1
Feature updates
- included support for a long as well as wide input formats for
quantile forecasts that are scored with
score
Package updates
- updated documentation for the
score
- added badges to the Readme