Mathematical and Statistical Software Applications

Research Computing offers a wide variety of Mathematical and Statistical software applications and tools for researchers. These software applications are available for use on Research Computing servers. Selecting any of the applications below will lead you to a listing of the information provided by the Research Computing group about that application, including documentation, FAQs, where to find manuals, links to other resources, and more.

The instructions given to run these programs assume a familiarity with the Research Computing infrastructure and the UNIX, Linux, Microsoft Windows, or MacOS operating systems, as appropriate. Please also read Mathematical and Statistical Software Application Notes to learn how to invoke these software applications on our servers.

Table 1. Available Mathematical and Statistical Software


Software Title Software Description
Maple Maple is a comprehensive computer system for advanced symbolic mathematics. It includes facilities for interactive algebra, calculus, discrete mathematics, graphics, numerical computation and many other areas of mathematics. It also provides a unique environment for rapid development of mathematical programs using its vast library of built-in functions and operations.
Mathematica Mathematica provides a fully integrated environment for technical computing. The usage of Mathematica is not only in the physical sciences, engineering and mathematics but also in the social science and commerce. It is also an important tool in computer science and software development.
Matlab Matlab is a high performance language for technical computing. Computation, visualization, and programming are integrated in an easy-to-use environment. Matlab can be used for mathematics, modeling, simulation, data analysis, visualization, scientific and engineering graphics.
R R is ‘GNU S’, a freely available language and environment for statistical computing and graphics which provides a wide variety of statistical and graphical techniques: linear and nonlinear modeling, statistical tests, time series analysis, classification, clustering, etc.
SAS The SAS System is a powerful programming language and a collection of ready-to-use programs called procedures. It has data access, data management, data presentation, and data analysis capabilities. Its roots are in academia, particularly statistical analysis, but SAS has broadened its applications to include dozens of tools from data entry products to a scalable performance data server product. The power of the SAS System comes from the integration of all of the SAS applications so they work together. Additionally, the SAS System is portable, you can easily move SAS applications across platforms. Finally, SAS has flexible user interfaces; this means if you would rather not learn the SAS programming language you can use the SAS user interface products for a menu-driven, task-oriented, and point-and-click environment.
SAVAS SAVAS is an application designed to translate data files from SAS datasets into Stata datasets or Stata datasets to SAS datasets.
Stata Stata is a complete, integrated statistical package for data analysis, data management, and graphics. It covers a wide range of statistical techniques and is programmable. Stata includes a variety of routines to analyze complex data and is a general purpose statistical package with good graphics capabilities and a graphic editor. Among the highlights of Stata are that it is relatively easy to learn for beginners. A fast and complete matrix programming language is an integral part of Stata.
SUDAAN Note. ITS Research Computing no longer licenses SUDAAN and so it is not available for use on the Research Computing server.

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