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Also for the record, Julia has all of 0, Missing (indicating data that is uknown), Nothing (indicating data non-existence), floating point NaN (as well as +Inf and -Inf of course), and exceptions for actual errors in Base and the Standard Libary. If you need more than that, user-defined types are just as performant and relatively trivial to implement.

Not sure if you were including Julia in "science or stats languages", but there it is anyway.

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Somewhat off topic, but my main problem with Julia is that my colleagues/correspondents won't understand it, it's not installed anywhere i need it, and my impression is they tried to make it MATLAB'y as though that was a positive rather than a negative.

If I wanted a performant, compiled, solution that allowed me to program up the answer myself that wasn't installed anywhere and everyone else couldn't understand, I'd just cut out the MATLAB syntax and install SBCL lisp :p

If it's a helpful perspective to you, here's an economics researcher who had been using common lisp for scientific computing and why he switched to Julia. I found it helpful for choosing between the two when first selecting a language for personal use:

https://tamaspapp.eu/post/common-lisp-to-julia/

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