incanter
numerical-utilities
| incanter | numerical-utilities | |
|---|---|---|
| 4 | 2 | |
| 2,233 | 13 | |
| 0.1% | - | |
| 3.1 | 5.5 | |
| 6 months ago | 4 months ago | |
| Clojure | Common Lisp | |
| - | Microsoft Public License |
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incanter
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New Lisp-Stat Release
Reminds me that there was an attempt to realize the "Back to the Future" vision in Clojure.
https://github.com/incanter/incanter
It never took off but looks like there was modifications made up until three years ago.
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What tutorial will teach you to do what they do over on /r/dataisbeautiful
Most languages have some sort of graphics library, so that can be used. When I did my project that lead to my single post on r/dataisbeautiful, I used the Clojure library Incater to do the heatmap. The matplotlib library for Python allows stuff like that as well.
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Replace Python @ Work w/ Scheme
Clojure has Incanter for the data-science stuff.
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SciCloj - How to build a Clojure Data Science Community
Is there a modern beginner guide for data sciences with Clojure? The best I found so far is the book "Clojure for Data Science" by Henry Garner which is fine but uses Incanter for visualization.
numerical-utilities
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Uncle Stats Wants You
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.
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New Lisp-Stat Release
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...
What are some alternatives?
hissp - It's Python with a Lissp.
clml - Common Lisp Machine Learning Library
chicken-pyffi - Chicken Scheme interface to Python
ultralisp - The software behind a Ultralisp.org Common Lisp repository
libpython-clj - Python bindings for Clojure
data-frame - Data frames for Common Lisp
hebigo - 蛇語(HEH-bee-go): An indentation-based skin for Hissp.
mgl - Common Lisp machine learning library.
py4cl - Call python from Common Lisp
plot - A vega-lite DSL for Common Lisp
cl-statistics - Updated (somewhat) version of Larry Hunter's CL-Statistics library