Whooooooooooops sorry about that! Someone was pretty stupid, check out how I defined the “convenience function” permentropy:
function permentropy(x, m = 3; τ = 1, base = Base.MathConstants.e)
Entropies.genentropy(x, SymbolicPermutation(; τ = 1, m = 3))
end
yea…
Fixed now, thanks! Thankfully this has no impact on performance (assuming we measure with order 3). So the Entropies.jl version is a lot faster. We had an original version based on combinatorics that was really slow, then @kahaaga re-wrote the algorithm with different approach based on symbolizing. I was kinda hoping you guys would had something even faster so we could steal it from you 
But now please do feel free to steal our implementation, as the performance difference will only become bigger with higher m and larger timeseries, especially due to the difference in allocations.
What is a permutation entropy of order 1? At least according to the original definition of Bandt and Pompe, there is no such thing as permutation entropy of order 1.
We also have amplitude-aware and weighted versions for the entropy but not the rest, good to know!
I am being annoyingly persistent, but I want to challenge that. Who does this help? A python user that already uses the Python version doesn’t really have a reason to use the Julia version, and so doesn’t really benefit from using the same names. Similarly a Julia user would want to use the Julia version, and once again has no benefit from having the same names.
Absolutely! I hope you don’t take my remarks the wrong way, I’m playing devil’s advocate here to (hopefully) help you make it even better.