  This paper is dedicated to the R package FMM which implements a novel
approach to describe rhythmic patterns in oscillatory signals. The frequency
modulated M\"obius (FMM) model is defined as a parametric signal plus a
gaussian noise, where the signal can be described as a single or a sum of
waves. The FMM approach is flexible enough to describe a great variety of
rhythmic patterns. The FMM package includes all required functions to fit and
explore single and multi-wave FMM models, as well as a restricted version that
allows equality constraints between parameters representing a priori knowledge
about the shape to be included. Moreover, the FMM package can generate
synthetic data and visualize the results of the fitting process. The potential
of this methodology is illustrated with examples of such biological
oscillations as the circadian rhythm in gene expression, the electrical
activity of the heartbeat and neuronal activity.
