cephes.cl VS numerical-utilities

Compare cephes.cl vs numerical-utilities and see what are their differences.

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cephes.cl numerical-utilities
2 2
15 13
- -
3.9 5.5
about 2 months ago 4 months ago
C Common Lisp
Microsoft Public License Microsoft Public License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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cephes.cl

Posts with mentions or reviews of cephes.cl. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-07-23.

numerical-utilities

Posts with mentions or reviews of numerical-utilities. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-07-23.
  • Uncle Stats Wants You
    8 projects | /r/Common_Lisp | 23 Jul 2022
    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.
  • New Lisp-Stat Release
    11 projects | news.ycombinator.com | 21 Jul 2022
    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?

When comparing cephes.cl and numerical-utilities you can also consider the following projects:

gophernotes - The Go kernel for Jupyter notebooks and nteract.

clml - Common Lisp Machine Learning Library

plot - A vega-lite DSL for Common Lisp

ultralisp - The software behind a Ultralisp.org Common Lisp repository

cl-statistics - Updated (somewhat) version of Larry Hunter's CL-Statistics library

data-frame - Data frames for Common Lisp

mgl - Common Lisp machine learning library.

incanter - Clojure-based, R-like statistical computing and graphics environment for the JVM

mgl-mat - MAT is library for working with multi-dimensional arrays which supports efficient interfacing to foreign and CUDA code. BLAS and CUBLAS bindings are available.

Petalisp - Elegant High Performance Computing