quantile_to_range

a data.frame following the specifications shown in the example
<code><a rd-options="" href="/link/range_example_data_long?package=scoringutils&version=0.1.7" data-mini-rdoc="scoringutils::range_example_data_long">range_example_data_long</a></code>)

data

keep the quantile column in the final
output after transformation (default is FALSE)

keep_quantile_col

Legacy function that will not be supported in future updates.

Combines a collection of metrics and proper scoring rules
(Tilmann Gneiting & Adrian E Raftery (2007)
<doi:10.1198/016214506000001437>) with an easy to
use wrapper that can be used to automatically evaluate predictions.
Apart from proper scoring rules functions are provided to assess bias,
sharpness and calibration (Sebastian Funk, Anton Camacho, Adam J. Kucharski,
Rachel Lowe, Rosalind M. Eggo, W. John Edmunds (2019)
<doi:10.1371/journal.pcbi.1006785>) of forecasts.
Several types of predictions can be evaluated:
probabilistic forecasts (generally
predictive samples generated by Markov Chain Monte Carlo procedures),
quantile forecasts or point forecasts. Observed values and predictions
can be either continuous, integer, or binary. Users can either choose
to apply these rules separately in a vector / matrix format that can
be flexibly used within other packages, or they can choose to do an
automatic evaluation of their forecasts. This is implemented with
'data.table' and provides a consistent and very efficient framework for
evaluating various types of predictions.

Nikos Bosse

scoringutils

Utilities for Scoring and Assessing Predictions

Sam Abbott 

Johannes Bracher 

Joel Hellewell

Sophie Meakins 

James Munday

Katharine Sherratt

Sebastian Funk

quantile_to_range function

a data.frame following the specifications shown in the example
<code><a rd-options='' href='range_example_data_long'>range_example_data_long</a></code>)

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quantile_to_range: Change Data from a Plain Quantile Format to a Long Range Format

Description

Usage

Arguments

Value