They also infringed on the original lisp-stat https://homepage.stat.uiowa.edu/~luke/xls/xlsinfo/ without so much as an acknowledgement a previous time this was spammed.

And there don't seem to be much non-trivial code written under this project, it's just loosely putting together some existing work and adding some READMEs with the same format.

A bit disorienting for someone looking for statistical computing environment in CL, to say the least. Maybe I'm stupid but this is no where near what (a somewhat complete environment) it makes itself look like.

... which is not a bad reason in some cases.

I for instance find Python the most horrible language + ecosystem outside the js ecosystem (but I like js the language more and that's saying something), so I would always opt for lisp (or pen + paper) over Python. R / Rstudio are nice though.

I don't think it really tracks either; Lisp is quite ergonomic for this type of thing and, if you have been doing it for a while, you'll have your own tooling to work faster/more efficient in that lisp and of course, the comparison falls down then as the swiss knife now has a chainsaw option which is as good or better than other options to cut down trees.

>Opting for Lisp is like trying to use a swiss knife to cut a tree just because you love your swiss knife.

First thing I did when I got my Swiss Army pocket knife was go to the woods by my house and cut down a tree with its little saw. It was a small, aspen or poplar maybe 3" thick and it took some doing but it came down. That was my first pocket knife and the first tree I cut down, believe I was in third grade. Still remember the smell of the freshly cut wood and the damp humus, the feeling of the sap running over my hand; it was one of those shadowless overcast days, early fall before leaves started turning. I avoided washing my hands all day just to keep the smell of the sap with me. I did love my Swiss knife, took it with me everywhere I went for years. Thanks for the memories.

Both.

There are some optimizations that can be made a compile-time that can speed up the computations. It also makes it portable provided that the executables are provided for each desired platform.

In my view, the biggest advantages of ahead-of-time compilation is lower binary size, higher performance, and binary portability (in a sense of being able to copy the binary and run it on another system with same architecture and OS, not in the usual sense of being easy to run to a different system architecture or OS).

It is IMO not known widely enough that Python itself can be compiled, using Nuitka [0] compiler. It still runs Python code, so the performance increase is not as extreme as one would get from rewriting in a fully statically typed code, but the AOT compiled C code is still faster than the interpreter.

[0] https://nuitka.net/