  The R package merlin performs flexible joint modelling of hierarchical
multi-outcome data. Increasingly, multiple longitudinal biomarker measurements,
possibly censored time-to-event outcomes and baseline characteristics are
available. However, there is limited software that allows all of this
information to be incorporated into one model. In this paper, we present merlin
which allows for the estimation of models with unlimited numbers of continuous,
binary, count and time-to-event outcomes, with unlimited levels of nested
random effects. A wide variety of link functions, including the expected value,
the gradient and shared random effects, are available in order to link the
different outcomes in a biologically plausible way. The accompanying
predict.merlin function allows for individual and population level predictions
to be made from even the most complex models. There is the option to specify
user-defined families, making merlin ideal for methodological research. The
flexibility of merlin is illustrated using an example in patients followed up
after heart valve replacement, beginning with a linear model, and finishing
with a joint multiple longitudinal and competing risks survival model.

Computation;Applications;Methodology