  When predicting future events, it is common to issue forecasts that are
probabilistic, in the form of probability distributions over the range of
possible outcomes. Such forecasts can be evaluated using proper scoring rules.
Proper scoring rules condense forecast performance into a single numerical
value, allowing competing forecasters to be ranked and compared. To facilitate
the use of scoring rules in practical applications, the scoringRules package in
R provides popular scoring rules for a wide range of forecast distributions.
This paper discusses an extension to the scoringRules package that additionally
permits the implementation of popular weighted scoring rules. Weighted scoring
rules allow particular outcomes to be targeted during forecast evaluation,
recognising that certain outcomes are often of more interest than others when
assessing forecast quality. This introduces the potential for very flexible,
user-oriented evaluation of probabilistic forecasts. We discuss the theory
underlying weighted scoring rules, and describe how they can readily be
implemented in practice using scoringRules. Functionality is available for
weighted versions of several popular scoring rules, including the logarithmic
score, the continuous ranked probability score (CRPS), and the energy score.
Two case studies are presented to demonstrate this, whereby weighted scoring
rules are applied to univariate and multivariate probabilistic forecasts in the
fields of meteorology and economics.
