What does HackerNews think of mgl?

If you are interested in machine learning, check out Gabor Melis's library: https://github.com/melisgl/mgl. It's not an area I'm super familiar with, so I can't speak to it's feature set, but I believe he used it to win a machine learning competition some years ago.

I don't think anyone's written a transformer or diffusion model with it, could be a fun challenge.

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], also 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/

If you're (very) motivated you could try MGL or CLML! https://github.com/melisgl/mgl - https://github.com/mmaul/clml - https://github.com/CodyReichert/awesome-cl#machine-learning

MGL's auhor [won](https://github.com/melisgl/higgsml) the Higgs Boson ML Challenge.