Here is a small list of statistics packages/software/addons for people trying to move away from proprietary/expensive software.

R suite is a programming language that focuses on statistics and graphics. Not having much experience with it, I leave further explanations for the developers:

R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, (…) and graphical techniques, and is highly extensible.(…)

One of R’s strengths is the ease with which well-designed publication-quality plots can be produced, including mathematical symbols and formulae where needed. Great care has been taken over the defaults for the minor design choices in graphics, but the user retains full control.

R is available as Free Software under the terms of the Free Software Foundation‘s GNU General Public License in source code form. It compiles and runs on a wide variety of UNIX platforms and similar systems (including FreeBSD and Linux), Windows and MacOS.

R also has an open IDE, called RStudio. The people over RStudio provide not only this IDE, but courses and training for R. Also they are the developers of several open packages for R.

Last but not least, there are several packages available for further incrementing R’s capabilities. Some of them can be found here.

R project. Image from


PyMVPA is a python based software for Multivariate Pattern Analysis. Again a better description comes from the developers:

PyMVPA is a Python package intended to ease statistical learning analyses of large datasets. It offers an extensible framework with a high-level interface to a broad range of algorithms for classification, regression, feature selection, data import and export.


PSPP is a free open source alternative to the famous SPSS. It is a program for statistical analysis of sampled data. Contrary to SPSS, there is no time license or the necessity of buying add-ons to get «advanced» functionality.

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