plot VS numerical-utilities

The examples from the first chapter of the Introduction to the Practice of Statistics, In Lisp-Stat, are complete and on github. This chapter is mostly about data visualisation, and anyone who uses PLOT might find the additional examples useful.

But, that's part of the reason for PLOT -- to hide that ugliness and make it easier to work with from Common Lisp. Have you found something specific that PLOT won't let you do? If so, open an issue and I'll take a look.

If you want to learn Lisp using a real-world problem, consider enhancing the stem-and-leaf plots. This is a good way to learn Common Lisp basics. It uses looping, printing and other basic programming constructs with text output. Specifically we need split stems and back-to-back stem plots.

Refresh the histogram code. Tamas Papp has a lot of good code that needs dusting off. The histogram code has a some bitrot that can be easily cleaned up and would make a nice addition. See the bottom of the statistics.lisp file.

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...