Comparison_of_numerical_analysis_software

Comparison of numerical-analysis software

Comparison of numerical-analysis software

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The following tables provide a comparison of numerical analysis software.

Applications

General

More information Creator, Development started ...

Operating system support

The operating systems the software can run on natively (without emulation).

More information Windows, macOS ...

Language features

Colors indicate features available as

basic system abilities
official or officially supported extensions and libraries
third-party software components or not supported
More information Standalone executables creation support, Symbolic computation support ...

Libraries

General

More information Creator, Language ...

Operating-system support

The operating systems the software can run on natively (without emulation).

More information Windows, macOS ...

See also

Footnotes

  1. Julia allows direct calls of C functions (no wrappers needed). Designed for cloud parallel computing with LLVM just-in-time compilation (JIT) as a backend. Lightweight "green" threading (coroutines). Efficient support for Unicode. Shell-like abilities to manage other processes. Lisp-like macros and other metaprogramming facilities.
  2. Abilities of PSPP include analysis of sampled data, frequencies, cross-tabs comparison of means (t-tests and one-way ANOVA); linear regression, logistic regression, reliability (Cronbach's Alpha, not failure or Weibull), and re-ordering data, non-parametric tests, factor analysis, cluster analysis, principal components analysis, chi-square analysis and more.
  3. SequenceL delivers high performance on multicore hardware with ease of programming, and code clarity/readability. Designed to work with other languages, including C, C++, C#, Java, Fortran, Python, etc. Can be compiled to multithreaded C++ (and optionally OpenCL) code with no explicit indications from the programmer of how or what to parallelize. A platform-specific runtime manages the threads safely.
  4. Once was supported

References

  1. "Julia in a Nutshell", from the official Julia homepage. Accessed 2019-01-25.
  2. Sai K. Popuri and Matthias K. Gobbert. A Comparative Evaluation of Matlab, Octave, R, and Julia on Maya. Technical Report HPCF-2017-03, UMBC High Performance Computing Facility, University of Maryland, U.S.A., 2017. Accessed 2019-01-25.
  3. Jules Kouatchou; Basic Comparison of Python, Julia, Matlab, IDL and Java (2018 Edition) Version 74. NASA Modeling Guru, Technical Report DOC-2676. Created on: 5-Feb-2018. Last Modified: 14-Sep-2018. Accessed 2019-01-25.
  4. National Instruments. "Working with .m File Scripts in NI LabVIEW for Text Based Signal Processing, Analysis, and Math". Retrieved April 3, 2017.
  5. "Maplesoft Media Releases". www.maplesoft.com. Retrieved May 12, 2024.
  6. "PTC Mathcad Prime 4.0 | PTC". Retrieved August 12, 2018.
  7. Mathematica License Pricing Options Wolfram.com, February 2024
  8. "Octave-Forge". Retrieved May 18, 2011.
  9. "Octave Wiki: OctaveFortran". Archived from the original on July 17, 2012. Retrieved May 18, 2011.
  10. "Octave Wiki: OctavePerl". Archived from the original on December 22, 2005. Retrieved May 18, 2011.
  11. "Octave Wiki: OctaveTcl". Archived from the original on July 17, 2012. Retrieved May 18, 2011.
  12. "Octave Wiki: OctaveJava". Retrieved May 18, 2011.
  13. "Octave Wiki: CategoryExternal". Archived from the original on July 23, 2012. Retrieved May 18, 2011.
  14. National Instruments. "G# Framework". Archived from the original on July 9, 2017. Retrieved April 3, 2017.
  15. National Instruments (January 18, 2010). "Calling External Code From LabVIEW". Retrieved April 3, 2017.
  16. "Lua for LabVIEW". Retrieved April 3, 2017.
  17. "Maple: MATLAB Connectivity". Retrieved May 18, 2011.
  18. "Mathematica Link for LabVIEW 2.1". Archived from the original on August 8, 2011. Retrieved May 18, 2011.
  19. "Unisoftware plus". Archived from the original on July 17, 2011. Retrieved May 19, 2011.
  20. "Clojuratica". clojuratica.weebly.com. 2013. Retrieved June 14, 2013.
  21. Mathworks. "MATLAB Compiler". Retrieved May 18, 2011.
  22. Mathworks. "Symbolic Math Toolbox". Retrieved May 18, 2011.
  23. Mathworks. "Object-Oriented Programming in MATLAB". Archived from the original on July 19, 2017. Retrieved May 18, 2011.
  24. "MATLAB File Exchange". Retrieved May 18, 2011.
  25. Mathworks. "MEX-files Guide". Retrieved May 18, 2011.
  26. "Perlmonks". Retrieved January 24, 2013.
  27. "O'Reilly tutorial". Retrieved January 24, 2013.
  28. "PerlTK tutorial". Retrieved January 24, 2013.
  29. "CPAN". Retrieved January 24, 2013.
  30. "Calling Perl from C". Retrieved January 24, 2013.
  31. R Development Core Team (April 13, 2011). "Object-oriented programming". R Language Definition. ISBN 978-3-900051-13-6. Retrieved May 18, 2011.
  32. "CRAN: Contributed Packages". Retrieved May 18, 2011.
  33. Hornik, Kurt (2011). The R FAQ. ISBN 978-3-900051-08-2.
  34. Bengtsson, Henrik; Jason Riedy. "CRAN: R.matlab package". Retrieved May 18, 2011.
  35. Grothendieck, G.; Carlos J. Gil Bellosta. "rJython R package". Retrieved May 18, 2011.
  36. Neuwirth, Erich. "CRAN: RExcelInstaller package". Archived from the original on May 25, 2011. Retrieved May 18, 2011.
  37. "Additional Packages". Retrieved June 5, 2013.
  38. "Interpreter Interfaces". Retrieved June 6, 2013.
  39. "C/C++ Library Interfaces". Retrieved June 6, 2013.
  40. "Using Compiled Code Interactively". Archived from the original on April 4, 2013. Retrieved June 6, 2013.

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