Lisp-Stat
Joined 5 years ago
Karma
103
Submissions
11
Member since
Apr 1
Recent submissions
Comment · 4 years ago
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.
Comment · 4 years ago
'plot/vglt' is from version 1 plotting. Version 2 uses 'plot/vega'. If you're seeing VGLT in the ASDF file it means that it's the old version, and you'll need to pull in the source for a manual install from github until Quicklisp gets updated. The systems you'll need are:
- 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.
Comment · 4 years ago
That requirement has been relaxed. I use Firefox. I'll update the docs.
Comment · 4 years ago
FIXED.
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.
Comment · 4 years ago
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
Comment · 4 years ago
No relation. The name was just used because we thought it would be cool to try and revive the spirit of the old company, and the name is long dead.
Comment · 4 years ago
Third major refactoring. Plotting significantly upgraded.
Comment · 5 years ago
+1 for MagicCL; it's very good. When I get around to adding BLAS-like functionality to data-frames, this is the lib we're going to use.
Comment · 5 years ago
Both great ideas. Code and plot examples added.
Comment · 5 years ago
Author here. Lisp-Stat is no April's fool's joke. The project is real and only a coincidence that it was mentioned here shortly before 1st of April.
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