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- R: The R Project for Statistical Computing
The R Project for Statistical Computing Getting Started R is a free software environment for statistical computing and graphics It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS To download R, please choose your preferred CRAN mirror
- R: What is R? - The R Project for Statistical Computing
R is a language and environment for statistical computing and graphics It is a GNU project which is similar to the S language and environment which was developed at Bell Laboratories (formerly AT T, now Lucent Technologies) by John Chambers and colleagues
- R-4. 5. 1 for Windows - The Comprehensive R Archive Network
Download R-4 5 1 for Windows (86 megabytes, 64 bit) README on the Windows binary distribution; New features in this version
- The Comprehensive R Archive Network
The Comprehensive R Archive Network (CRAN) provides access to R software, packages, and documentation for statistical computing and graphics
- A Handbook of Statistical Analyses Using R - The Comprehensive R . . .
With the help of the R system for statistical computing, research really becomes reproducible when both the data and the results of all data analysis steps reported in a paper are available to the readers through an Rtranscript file Ris most widely used for teaching undergraduate and graduate statistics classes at universities all over
- An Introduction to R
There are now a number of books which describe how to use R for data analysis and statistics, and documentation for S S-Plus can typically be used with R, keeping the differences between the S implementations in mind
- The R Foundation - The R Project for Statistical Computing
Among the goals of the R Foundation are the support of continued development of R, the exploration of new methodology, teaching and training of statistical computing and the organization of meetings and conferences with a statistical computing orientation
- R: Software Development Life Cycle A Description of R’s Development . . .
R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classi cation, clustering, etc ) and graphical techniques, and is readily extensible
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