  Prediction rule ensembles (PREs) are a relatively new statistical learning
method, which aim to strike a balance between predictive accuracy and
interpretability. Starting from a decision tree ensemble, like a boosted tree
ensemble or a random forest, PREs retain a small subset of tree nodes in the
final predictive model. These nodes can be written as simple rules of the form
if [condition] then [prediction]. As a result, PREs are often much less complex
than full decision tree ensembles, while they have been found to provide
similar predictive accuracy in many situations. The current paper introduces
the methodology and shows how PREs can be fitted using the R package pre
through several real-data examples from psychological research. The examples
also illustrate a number of features of package \textbf{pre} that may be
particularly useful for applications in psychology: support for categorical,
multivariate and count responses, application of (non-)negativity constraints,
inclusion of confirmatory rules and standardized variable importance measures.
