  In the nonlinear timeseries analysis literature, countless quantities have
been presented as new "entropy" or "complexity" measures, often with similar
roles. The ever-increasing pool of such measures makes creating a sustainable
and all-encompassing software for them difficult both conceptually and
pragmatically. Such a software however would be an important tool that can aid
researchers make an informed decision of which measure to use and for which
application, as well as accelerate novel research. Here we present
ComplexityMeasures.jl, an easily extendable and highly performant open-source
software that implements a vast selection of complexity measures. The software
provides 1530 measures with 3,834 lines of source code, averaging only 2.5
lines of code per exported quantity (version 3.5). This is made possible by its
mathematically rigorous composable design. In this paper we discuss the
software design and demonstrate how it can accelerate complexity-related
research in the future. We carefully compare it with alternative software and
conclude that ComplexityMeasures.jl outclasses the alternatives in several
objective aspects of comparison, such as computational performance, overall
amount of measures, reliability, and extendability. ComplexityMeasures.jl is
also a component of the DynamicalSystems.jl library for nonlinear dynamics and
nonlinear timeseries analysis and follows open source development practices for
creating a sustainable community of developers.
