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Lisp-stat: Lisp environment for statistical computing

https://lisp-stat.dev/about/
61•oumua_don17•1d ago

Comments

andsoitis•7h ago
> © 2025 Symbolics Pte Ltd

Seems to be this company in Singapore: https://opencorporates.com/companies/sg/201923570D

As opposed to the Symbolics company: https://en.wikipedia.org/wiki/Symbolics

nothisagain•6h ago
They also infringed on the original lisp-stat https://homepage.stat.uiowa.edu/~luke/xls/xlsinfo/ without so much as an acknowledgement a previous time this was spammed.
Joel_Mckay•5h ago
To be fair, Lisp has a tradition of concurrent unrelated variants.

https://www.youtube.com/watch?v=sV7C6Ezl35A

Yet, a failure to cite related parent projects certainly needs addressed. Maybe forgivable if it was a first year student. =3

dapperdrake•5h ago
How big is the confidence interval on this?
dleslie•5h ago
It appears to be derived from that:

https://github.com/Lisp-Stat/lisp-stat/blob/2514dc3004b09942...

And

https://lisp-stat.dev/blog/2021/05/09/statistical-analysis-w...

kscarlet•3h ago
And there don't seem to be much non-trivial code written under this project, it's just loosely putting together some existing work and adding some READMEs with the same format.

A bit disorienting for someone looking for statistical computing environment in CL, to say the least. Maybe I'm stupid but this is no where near what (a somewhat complete environment) it makes itself look like.

fud101•4h ago
I loved Xlisp-stat, the book was gorgeous and when I discovered Lisp-stat, i was using a Windows XP machine in a college Lab machine - it just worked and I used it as my first lisp. Such a good piece of software. Not sure about the new package - I'm long past my lisping days now.
awaymazdacx5•3h ago
the lispworks test package typically contains xlib-stat over tcp-udp transport protocls that should designate BMP-strings
submeta•2h ago
Chose the right tool for the right task. I‘ll go with R and RStudio or even Python for data analysis and statistics. Opting for Lisp is like trying to use a swiss knife to cut a tree just because you love your swiss knife.
anonzzzies•2h ago
... which is not a bad reason in some cases.

I for instance find Python the most horrible language + ecosystem outside the js ecosystem (but I like js the language more and that's saying something), so I would always opt for lisp (or pen + paper) over Python. R / Rstudio are nice though.

I don't think it really tracks either; Lisp is quite ergonomic for this type of thing and, if you have been doing it for a while, you'll have your own tooling to work faster/more efficient in that lisp and of course, the comparison falls down then as the swiss knife now has a chainsaw option which is as good or better than other options to cut down trees.

TurboHaskal•1h ago
Yeah I don't get it either. Lisp is perfectly fine for this task although probably makes less sense now that Julia is a thing.

Reminder that before Python was used for data science, people used things like BioPerl and PDL and that didn't stop people from working on pandas and the like.

Also let people have fun.

hatmatrix•1h ago
Lispers might not like that it's not a Lisp, but I remember Luke Tierney also making a statement to the effect that the statisticians have spoken and they don't prefer the Lisp syntax.

So Julia is a happy middle ground - MATLAB-like syntax with metaprogramming facilities (i.e., macros, access to ASTs). Its canonical implementation is JIT, but the community is working on allowing creation of medium-sized binaries (there has been much effort to reduce this footprint).

nomilk•1h ago
It cites inability to compile to machine code as a reason for preferring lisp to R and Python.

What are the benefits of an ability to compile to machine code? Does it mean you can make stand alone binaries (I.e. programs that can run without the language - lisp|R|python - installed), or is there some other advantage, eg performance?

hatmatrix•1h ago
Both.

There are some optimizations that can be made a compile-time that can speed up the computations. It also makes it portable provided that the executables are provided for each desired platform.

bheadmaster•1h ago
In my view, the biggest advantages of ahead-of-time compilation is lower binary size, higher performance, and binary portability (in a sense of being able to copy the binary and run it on another system with same architecture and OS, not in the usual sense of being easy to run to a different system architecture or OS).

It is IMO not known widely enough that Python itself can be compiled, using Nuitka [0] compiler. It still runs Python code, so the performance increase is not as extreme as one would get from rewriting in a fully statically typed code, but the AOT compiled C code is still faster than the interpreter.

[0] https://nuitka.net/

akashi9•7m ago
Is lower binary size or binary portability really a major concern for statistical computing? In my experience with statistical computing with R and using R I've never once had a situation where producing a binary was required? As for portability, I mean can just share the script and the data right?
akashi9•3m ago
Interesting and cool idea but by far the biggest strength of R for statistical computer is the wealth of libraries and documentation out there for the language, obviously Rome wasn't built in a day but does lisp-stat offer any of these things?

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Lisp-stat: Lisp environment for statistical computing

https://lisp-stat.dev/about/
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