  Multifractal detrended fluctuation analysis (MFDFA) has become a central
method to characterise the variability and uncertainty in empiric time series.
Extracting the fluctuations on different temporal scales allows quantifying the
strength and correlations in the underlying stochastic properties, their
scaling behaviour, as well as the level of fractality. Several extensions to
the fundamental method have been developed over the years, vastly enhancing the
applicability of MFDFA, e.g. empirical mode decomposition for the study of
long-range correlations and persistence. In this article we introduce an
efficient, easy-to-use python library for MFDFA, incorporating the most common
extensions and harnessing the most of multi-threaded processing for very fast
calculations.
