  We discuss a recently proposed family of statistical network models -
relational hyperevent models (RHEM) - for analyzing team selection and team
performance in scientific coauthor networks. The underlying rationale for using
RHEM in studies of coauthor networks is that scientific collaboration is
intrinsically polyadic, that is, it typically involves teams of any size.
Consequently, RHEM specify publication rates associated with hyperedges
representing groups of scientists of any size. Going beyond previous work on
RHEM for meeting data, we adapt this model family to settings in which
relational hyperevents have a dedicated outcome, such as a scientific paper
with a measurable impact (e.g., the received number of citations). Relational
outcome can on the one hand be used to specify additional explanatory variables
in RHEM since the probability of coauthoring may be influenced, for instance,
by prior (shared) success of scientists. On the other hand relational outcome
can also serve as a response variable in models seeking to explain the
performance of scientific teams. To tackle the latter we propose relational
hyperevent outcome models (RHOM) that are closely related with RHEM to the
point that both model families can specify the likelihood of scientific
collaboration - and the expected performance, respectively - with the same set
of explanatory variables allowing to assess, for instance, whether variables
leading to increased collaboration also tend to increase scientific impact. For
illustration, we apply RHEM to empirical coauthor networks comprising more than
350,000 published papers by scientists working in three scientific disciplines.
