  Growth curve models are popular tools for studying the development of a
response variable within subjects over time. Heterogeneity between subjects is
common in such models, and researchers are typically interested in explaining
or predicting this heterogeneity. We show how generalized linear mixed effects
model (GLMM) trees can be used to identify subgroups with differently shaped
trajectories in linear growth curve models. Originally developed for clustered
cross-sectional data, GLMM trees are extended here to longitudinal data. The
resulting extended GLMM trees are directly applicable to growth curve models as
an important special case. In simulated and real-world data, we assess the
performance of the extensions and compare against other partitioning methods
for growth curve models. Extended GLMM trees perform more accurately than the
original algorithm and LongCART, and similarly accurate as structural equation
model (SEM) trees. In addition, GLMM trees allow for modeling both discrete and
continuous time series, are less sensitive to (mis-)specification of the
random-effects structure and are much faster to compute.
