This post is to announce the release of **EntropyHub.jl** - a package for estimating various information-theoretic entropy measures from time series and image data.

EntropyHub.jl (v0.1) features functions for computing:

- standard (base) entropy measures

e.g. sample entropy, fuzzy entropy, permutation entropy, slope entropy, and much more. - cross-entropy methods

e.g. cross-approximate entropy, cross-Kolmogorov entropy, and more. - multiscale entropy using any of the standard entropies

e.g. multiscale dispersion entropy, refined multiscale approximate entropy, and much more. - multiscale cross-entropy using any of the cross-entropies

e.g. multiscale cross-conditional entropy, composite multiscale cross-distribution entropy, and more. - bidimensional entropy measure for matrix (image) data

e.g. bidimensional fuzzy entropy, bidimensional dispersion entropy.

The EntropyHub project aims to bring together the many entropy measures in the scientific literature under one complete package that is available in Julia, Python and MatLab

EntropyHub is in the Julia Registry and can be installed in the Pkg REPL as follows:

```
pkg> add EntropyHub
```

Documentation for EntropyHub.jl, with descriptions of function syntax and examples of use can be found at:

MattWillFlood.github.io/EntropyHub.jl/stable and in the *EntropyHub Guide.pdf*.

EntropyHub continually seeks to integrate as many established entropy methods into one comprehensive package. We welcome suggestions and support from all in helping to achieve this

Thanks,

Matt

*info@EntropyHub.xyz*