Yes, vega can do that. If you click on the round button with three ellipses (...) you'll see a menu and one of the options is a PNG or SVG download of the plot.
103 karma · joined April 1, 2021
- alexandria+
- data-frame
- dfio
- lisp-stat
- plot
- numerical-utilities
- select
- sqldf
It may be easier to get help from one of the community resources [1] (StackOverflow, github issue/discussion, mailing list) than HN, as those are always monitored.
That's a new file to help with the auto generated API documentation and it's possible one was missed at checkin.
Vindarel has correctly identified the main problem though, Quicklisp hasn't yet picked up the new files.
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
The Overview [1] and About [2] pages provide the best background to the project. Also see Tierney's paper, Back to the Future: Lisp as a Base for a Statistical Computing System [3]
Although inspired by Tierney's XLisp-Stat, this is a reboot in Common Lisp. XLisp-Stat code is unlikely to run except in trivial cases, but existing XLisp-Stat libraries can be ported with the assistance of the XLS-Compat [4] system. One of the goals was to make porting easier so as to have a ready-made eco-system of sorts. To that end I've also collected all the XLS code that is still readily available and put that on github. A summary of what's there is available on the website [5].
In developing the system, I wanted to avoid the 'lisp curse' [6] and picked existing libraries where possible, developed what didn't exist, and documented them all in an attempt to make the learning curve somewhat less steep. It's now to the point where I can use it in my own work and thought the broader CL community might find it useful.
[1] https://lisp-stat.dev/docs/overview/
[2] https://lisp-stat.dev/about
[3] https://www.stat.auckland.ac.nz/~ihaka/downloads/Compstat-20...
[4] https://github.com/Lisp-Stat/XLS-compat
[5] https://lisp-stat.dev/docs/contributing/xlisp/
[6] http://www.winestockwebdesign.com/Essays/Lisp_Curse.html