Free statistical software is a practical alternative to commercial packages. Many of the free to use programs aim to be similar in function to commercial packages, in that they are general statistical packages that perform a variety of statistical analyses. Many other free to use programs were designed specifically for particular functions, like factor analysis, power analysis in sample size calculations, classification and regression trees, or analysis of missing data.
Many of the free to use packages are fairly easy to learn, using menu systems. Many others are command-driven. Still others are meta-packages or statistical computing environments, which allow the user to code completely new statistical procedures. These packages come from a variety of sources, including governments, universities, and private individuals.
This article is primarily a review of the general statistical packages.
SAS (software) was among the first commercial statistical packages, released for mainframes in 1968. SAS has since then released versions free to use, the most recent of which is SAS Studio. Epi Info a free to use program from the Centers for Disease Control and Prevention was developed in the 1980s. One of the first completely free to use and open source statistical software was R, first released in 2000.
Some of the free software packages are from governments, for example Epi Info, which is from CDC (Centers for Disease Control and Prevention). Some other software packages are from smaller or independent organizations or universities. JASP is supported by the University of Amsterdam. Two other packages, R, and PSPP are being developed as part of the GNU Project by a large group of individuals, many of them volunteers, all over the world. These packages are notable in that it is not just open source but also free software in the same sense that material written on Wikipedia is free: others can edit, use, and redistribute at will.
OpenStat was developed as a teaching aid. Other packages were developed for specific purposes but can be more generally used.
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R is a programming language for statistical computing and graphics supported by the R Core Team and the R Foundation for Statistical Computing. Created by statisticians Ross Ihaka and Robert Gentleman, R is used among data miners, bioinformaticians and statisticians for data analysis and developing statistical software. The core R language is augmented by a large number of extension packages containing reusable code and documentation. According to user surveys and studies of scholarly literature databases, R is one of the most commonly used programming languages in data mining.
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