To add to the very good answers so far, I'd mention that there is an issue which tracks scientific libraries here:
[https://github.com/nim-lang/needed-libraries/issues/77](https://github.com/nim-lang/needed-libraries/issues/77) And to answer your explicit question whether Nim is _suitable_ for statistics, I'd answer with a definitive YES. But of course being suitable does not mean most libraries you'd like to use exist, just that in my opinion it's a perfect language to write / port those libraries in / to. Aside from that I'm personally not a fan of e.g. jupyter notebooks anyways. And given the quick compile times I don't feel the need. I rather like to go the literate programming path, like e.g. here: [https://github.com/Vindaar/TimepixAnalysis/tree/refactorRawManipulation/Doc/other](https://github.com/Vindaar/TimepixAnalysis/tree/refactorRawManipulation/Doc/other)
