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Comparison of numerical-analysis software

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The following tables provide a comparison of numerical analysis software.

Applications

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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 ...
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Libraries

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General

More information Creator, Language ...

Operating-system support

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

More information Windows, macOS ...
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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
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References

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