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R (General Statistical)

Functionality Background Licence availability, restrictions
and links to distributor.

R is at its heart a statistical programming and data manipulation language, capable of being used to perform just about any statistical analysis. It is also very strong on graphics. R is used extensively across academic statistics for developing new statistical methodology. Being an open project, there are an enormous number of add-ins and packages available to perform specific analyses. Some are very well tested and validated, but others are there purely for others to test and experiment with. So it is wise to check the pedigree of any add-in that you use for analysis. Some of the add-ins provide graphical interfaces making particular analyses or suites of analyses accessible to inexperienced users (e.g. R Commander and Rattle), but the R package itself is designed for professional statisticians and researchers.

The R Studio package provides a slightly more friendly interface for less-experienced users, and is certainly a good environment in which to teach the use of R.

‘S’ was developed as a statistical programming language by J.M. Chambers at Bell AT&T Labs. As a language it allows any statistical analysis to be written and run on virtually any data. ‘R’ is a free implementation of a dialect of S. (S-Plus is a commercial implementation: see entry on S-Plus).

As a matter of local history, in 2007 Prof David Firth and Dr Heather Turner of Warwick Statistics won the John M Chambers Statistical Software Award for their Generalized Nonlinear Models (gnm) R package.

Note that this is public domain software and, while there is reason to be confident in R itself, many add-ins are not tested and validated to commercial standards.
The R website with links for downloads, manuals, FAQs, book lists, etc. is at:
A library of contributed add-in packages is maintained (under the menu item packages) at:

R Studio can be downloaded from

Note on General Purpose Statistical Packages:

Broadly, these packages offer the user an easy way of applying standard estimation, confidence interval and hypothesis testing techniques to general data, usually input in the form of a spreadsheet.

Please share your experiences and examples of how you have used this software by leaving a comment below, to help others choose the right software for their research requirements.