  Multiplicative mixed models can be applied in a wide range of scientific
disciplines, since they are relevant in every situation where an interaction
between a fixed effect and a random effect is present. Until now, no R package
has been published, which can fit this type of models. The lack of
user-friendly open source tools to fit these models, is the main reason that
the models are not used as often as they could or should be. In this paper we
introduce the user-friendly R package $\mathsf{mumm}$ for fitting
multiplicative mixed models in a time-efficient manner. To illustrate the
interpretation of the multiplicative term, we provide four data analysis
examples, where the model is fitted to data sets that stem from studies in
sensometrics, agriculture and medicine. With these examples it is shown that
the statistical inference can be improved by using a multiplicative mixed
model, instead of a linear mixed model which is usually employed.
