03 07 14 - 16:39

R was born as a derivation of the commercial software S, or, to be more exact, being the most part of the source code of R identical to the S one , R can be defined as a different implementation of S. R was initially written by Robert Gentleman and Ross Ihaka of the Statistics Department of the University of Auckland. (who were part also of the S project ) , then a large team (called Cran) was born, which supports his continued devolepment.

R is available as Free Software under the terms of the Gnu license, and you can download it freely by Cran archive (but a lot of mirrors exist ), and it runs on a wide variety of Unix platforms and similar systems (including FreeBSD and Linux), Windows and MacOS.
The main features of R are:
1) An effective data handling and storage facility
2) A suite of operators for calculations on arrays, in particular matrices
3) A large, coherent, integrated collection of intermediate tools for data analysis
4) Graphical facilities for data analysis and display either on-screen or on hardcopy
5) A well-developed, simple and effective programming language which includes conditionals, loops, diuser-defined recursive functions and input and output facilities.
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03 07 14 - 16:38

Tanagra is defined by the author (Ricco Rakotomalala, professor at the University in Lione, France) as a free data mining software for research and education. Tanagra is available only for Windows and it is downloadable freely. Tanagra is really an open source project and the author allows to download also the whole source code so to give to everyone the possibility of developing this software.
The interface of Tanagra consists of three parts which identify the way of working in the program:

1) The stream diagram (built in a tree structure) which represents the various steps of analysis you want to carry out in Tanagra.
2) The single operators (or components) each of those (identified in the diagram by a node) represents an operation done on the data: for example there is a component to visualize the dataset, another one to perform a regression analysis and so on. The set of these operators is placed at the bottom of the interface and it is subdivided in categories. Some of them correspond to the various types of analysis (descriptive statistics, multiple linear regression, clustering , principal component analysis, multiple correspondance analysis and so on), other ones are related to dataset manipulation (importation, visualization, attribute definition).
3) The report in html format subdivived in two parts:the description of the analysis parameters and the results.
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03 07 14 - 16:36

ViSta (The Visual Statistics System) is a software written by Forrest W.Young, Professor of Psychometrics at the University of North Carolina at Chapel Hill.
As it is described on the website, this software helps you to see what your data seem to say. Indeed it has its strengths in visualizations which are highly dynamic and very interactive, showing you multiple views of your data simultaneously.
ViSta is an open source software (but to contribute to it you have to get a password from the author) and it is available for Windows, Macintosh and Unix.
ViSta is an extensible software, since it is open to new contributors (programs written in Fortran, in C and in XlispStat are accessible from within ViSta).
ViSta can perform various kind of analysis , also thanks to the implementation of some plugins you can download from its website. In particular ViSta can carry out univariate and various kind of multivariate analysis (for example principal component).
ViSta is mainly useful in teaching in univariate and multivariate statistics courses, but it can be also utilized in research.

Though it is expected a further enlargement of its functions in the future, ViSta has to be considered a software with some significant limits. At this moment also its interaction with software like Excel is difficult.
ViSta graphic interface has a top windows-style menu, but you can also give inputs from command line in the lower part of the screen. The workspace is subdivided between the workmap where the various steps of analysis are listed (and from here you can get either numerical results and graphich visualizations), and the datasheet, where you can enter data to process.