  Decision trees are flexible prediction models which are constructed to
quantify outcome-covariate relationships and characterize relevant population
subgroups. However, the standard graphical representation of fitted decision
trees highlights individual split points, and hence is suboptimal for
visualizing defined subgroups. In this paper, we present a novel visual
representation of decision trees which shifts the primary focus to
characterizing subgroups, both in terms of their defining covariates and their
outcome distribution. We implement our method in the \texttt{visTree} package,
which builds on the toolkit and infrastructure provided by the
\texttt{partykit} package and enables the visualization to be applied to varied
decision trees. Individual functions are demonstrated using data from the Box
Lunch study [French et al., 2014], a randomized trial to evaluate the effect of
exposure to different lunch sizes on energy intake and body weight among
working adults.
