Julia packages that have no equivalents in other languages?

[lmiq]

I think this comment is more important than the packages available:

Comparing Numba and Julia for a complex matrix computation

I should add that this is almost certainly the wrong way to go about getting performance improvements. “Highly optimized” code in Python usually means code that is “vectorized” as much as possible — broken into a series of relatively simple array operations that can individually call fast library operations. (For example, calling atan on a bunch of numbers in your code above.) You can write the same sort of code in Julia, of course, but it won’t be magically faster than Numpy — there’s no s…

The audience is important. Will they be users of packages, or developers? It is like asking “What Julia has that Microsoft Word does not offer?” Well, if you are happy with MSWord, and what you want is an easy way to write letters, nothing.

That probably goes the same way if the user is satisfied with the set of packages of any other language he/she uses and there is no need to improve on those implementations, develop problem-specific codes, etc.

Julia is most appealing to developers. Maybe when the package ecosystem becomes as huge as that of python, “users” will find it as welcoming as python. But package users adapt easily to packages in different languages, if the packages are properly documented for the narrow uses they are designed to. In any field we read how to use package-specific inputs and syntax all the time.