LILisp-Stat·Jul 21, 2022
I think this depends on what part of the statistics universe you're working in.
For example, within Lisp-Stat the statistics routines [1] were written by an econometrician working for the Austrian government (Julia folks might know him - Tamas Papp). It would not be exaggerating to say his job depending on it. These are state of the art, high performance algorithms, equal to anything available in R or Python. So, if you're doing econometrics, or something related, everything you need is already there in the tin.
For machine learning, there's CLML [2], developed by NTT. This is the largest telco in Japan, equivalent to ATT in the USA. As well, there is MGL [3], used to win the Higgs Boson challenge a few years back. Both actively maintained.
For linear algebra, MagicCL was mention elsewhere in the thread. My favourite is MGL-MAT [4], al
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so by the author of MGL. This supports both BLAS and CUBLAS (CUDA for GPUs) for solutions.
Finally, there's the XLISP-STAT archive [5]. Prior to Luke Tierney, the author of XLISP-Stat joining the core R team, XLISP-STAT was the dominate statistical computing platform. There's heaps of stuff in the archive, most at least as good as what's in base R, that could be ported to Lisp-Stat.
Common Lisp is a viable platform for statistics and machine learning. It isn't (yet) quite as well organised as R or Python, but it's all there.
[1] https://github.com/Lisp-Stat/numerical-utilities/blob/master...
[2] https://github.com/mmaul/clml
[3] https://github.com/melisgl/mgl
[4] https://github.com/melisgl/mgl-mat
[5] https://lisp-stat.dev/docs/resources/xlisp/