Software:List of numerical-analysis software

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Listed here are notable end-user computer applications intended for use with numerical or data analysis:

Numerical-software packages

General-purpose computer algebra systems

Interface-oriented

Language-oriented

Historically significant

  • Expensive Desk Calculator written for the TX-0 and PDP-1 in the late 1950s or early 1960s.
  • S is an (array-based) programming language with strong numerical support. R is an implementation of the S language.

See also

References

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  2. Gander, W., & Hrebicek, J. (Eds.). (2011). Solving problems in scientific computing using Maple and Matlab®. Springer Science & Business Media.
  3. Barnes, B., & Fulford, G. R. (2011). Mathematical modelling with case studies: a differential equations approach using Maple and MATLAB. Chapman and Hall/CRC.
  4. David Ramel (2018-05-08). "Open Source, Cross-Platform ML.NET Simplifies Machine Learning -- Visual Studio Magazine". Visual Studio Magazine. https://visualstudiomagazine.com/articles/2018/05/08/ml-net-framework.aspx. 
  5. Kareem Anderson (2017-05-09). "Microsoft debuts ML.NET cross-platform machine learning framework". On MSFT. https://www.onmsft.com/news/microsoft-debuts-ml-net-cross-platform-machine-learning-framework. 
  6. Bunks, C., Chancelier, J. P., Delebecque, F., Goursat, M., Nikoukhah, R., & Steer, S. (2012). Engineering and scientific computing with Scilab. Springer Science & Business Media.
  7. Thanki, R. M., & Kothari, A. M. (2019). Digital image processing using SCILAB. Springer International Publishing.
  8. Maeder, R. E. (1991). Programming in mathematica. Addison-Wesley Longman Publishing Co., Inc.
  9. Stephen Wolfram. (1999). The MATHEMATICA® book, version 4. Cambridge University Press .
  10. Shaw, W. T., & Tigg, J. (1993). Applied Mathematica: getting started, getting it done. Addison-Wesley Longman Publishing Co., Inc.
  11. Marasco, A., & Romano, A. (2001). Scientific Computing with Mathematica: Mathematical Problems for Ordinary Differential Equations; with a CD-ROM. Springer Science & Business Media.
  12. Zimmermann, P., Casamayou, A., Cohen, N., Connan, G., Dumont, T., Fousse, L., ... & Bray, E. (2018). Computational Mathematics with SageMath. SIAM.
  13. Wagner III, W. E. (2019). Using IBM® SPSS® statistics for research methods and social science statistics. Sage Publications.
  14. Pollock III, P. H., & Edwards, B. C. (2019). An IBM® SPSS® Companion to Political Analysis. Cq Press.
  15. Babbie, E., Wagner III, W. E., & Zaino, J. (2018). Adventures in social research: Data analysis using IBM SPSS statistics. Sage Publications.
  16. Aldrich, J. O. (2018). Using IBM® SPSS® Statistics: An interactive hands-on approach. Sage Publications.
  17. Stehlik-Barry, K., & Babinec, A. J. (2017). Data Analysis with IBM SPSS Statistics. Packt Publishing Ltd.
  18. Ch Scientific Numerical Computing
  19. Bezanson, J., Edelman, A., Karpinski, S., & Shah, V. B. (2017). Julia: A fresh approach to numerical computing. SIAM Review, 59(1), 65-98.
  20. Bezanson, J., Karpinski, S., Shah, V. B., & Edelman, A. (2012). Julia: A fast dynamic language for technical computing. arXiv preprint arXiv:1209.5145.
  21. Gumley, L. E. (2001). Practical IDL programming. Elsevier.
  22. Christiansen, T., Wall, L., & Orwant, J. (2012). Programming Perl: Unmatched power for text processing and scripting. " O'Reilly Media, Inc.".
  23. Srinivasan, S. (1997). Advanced perl programming. " O'Reilly Media, Inc.".
  24. Van Rossum, G. (2007, June). Python Programming Language. In USENIX annual technical conference (Vol. 41, p. 36).
  25. Sanner, M. F. (1999). Python: a programming language for software integration and development. J Mol Graph Model, 17(1), 57-61.
  26. Jones, E., Oliphant, T., & Peterson, P. (2001). SciPy: Open source scientific tools for Python.
  27. Bressert, E. (2012). SciPy and NumPy: an overview for developers. " O'Reilly Media, Inc.".
  28. Blanco-Silva, F. J. (2013). Learning SciPy for numerical and scientific computing. Packt Publishing Ltd.
  29. Ihaka, R., & Gentleman, R. (1996). R: a language for data analysis and graphics. Journal of computational and graphical statistics, 5(3), 299-314.
  30. Khattree, R., & Naik, D. N. (2018). Applied multivariate statistics with SAS software. SAS Institute Inc.
  31. SAS/IML