Statistical computing made easy with a language and environment designed specifically for this purpose. Enjoy user-friendly statistical analysis with this efficient software.
R is an exceptional programming language and environment that is specially designed for statistical computing and graphics. It is a GNU project that has similarities with the S language and environment that was originally developed at the Bell Laboratories by John Chambers and his colleagues. In essence, R can be regarded as an alternative implementation of the S programming language, with a lot of vital distinctions. However, a great deal of code written for S can run seamlessly on R.
A highlight of R is its remarkable capacity to offer a broad spectrum of statistical techniques and graphical tools, including linear and nonlinear modeling, classical statistical tests, clustering, time-series analysis, and much more. R is also highly extendable and widely used for research in statistical methodology. A key advantage of using R is how easy it is to develop publication-quality plots that can accommodate mathematical symbols and formulae. The graphics design choices are thoughtfully selected, but the user retains full control.
R is distributed under the GNU General Public License as Free Software in source code form. It runs on several UNIX platforms, including FreeBSD and Linux, Windows, and MacOS. R is a well-rounded software package that integrates data manipulation, calculation, and graphical display in one suite of software tools. It features an effective data handling and storage facility, operators for calculations on arrays (particularly matrices), a large collection of intermediate tools for data analysis, excellent graphical facilities, and a simple yet effective programming language that comes with conditionals, user-defined recursive functions, and input/output facilities.
What makes R an outstanding environment for data analysis is the way it is fully planned and unified. It is a coherent system, as opposed to being an accretion of inflexible and specific tools that are often found in other data analysis software. R is built around a genuine computer language that lets users extend its functionality with new functions. Besides, much of the system is written in the R dialect of the S programming language, which makes it easy for users to grasp the algorithmic choices made. For intensive computational tasks, R can link and call C, C++, and Fortran code at runtime. Experienced users can write C code for manipulating R objects directly.
While some might say that R is primarily a statistics system, we prefer to see it as an environment that allows a wide range of statistical techniques and methods to be implemented. R can easily be extended through packages, and the software distribution comes with eight such packages pre-installed. There are also many more packages available through the CRAN family of Internet sites, covering a vast array of modern statistical methods. R has its documentation system that is akin to LaTeX used for supplying comprehensive documentation online in several formats and can also be printed on paper.
A highlight of R is its remarkable capacity to offer a broad spectrum of statistical techniques and graphical tools, including linear and nonlinear modeling, classical statistical tests, clustering, time-series analysis, and much more. R is also highly extendable and widely used for research in statistical methodology. A key advantage of using R is how easy it is to develop publication-quality plots that can accommodate mathematical symbols and formulae. The graphics design choices are thoughtfully selected, but the user retains full control.
R is distributed under the GNU General Public License as Free Software in source code form. It runs on several UNIX platforms, including FreeBSD and Linux, Windows, and MacOS. R is a well-rounded software package that integrates data manipulation, calculation, and graphical display in one suite of software tools. It features an effective data handling and storage facility, operators for calculations on arrays (particularly matrices), a large collection of intermediate tools for data analysis, excellent graphical facilities, and a simple yet effective programming language that comes with conditionals, user-defined recursive functions, and input/output facilities.
What makes R an outstanding environment for data analysis is the way it is fully planned and unified. It is a coherent system, as opposed to being an accretion of inflexible and specific tools that are often found in other data analysis software. R is built around a genuine computer language that lets users extend its functionality with new functions. Besides, much of the system is written in the R dialect of the S programming language, which makes it easy for users to grasp the algorithmic choices made. For intensive computational tasks, R can link and call C, C++, and Fortran code at runtime. Experienced users can write C code for manipulating R objects directly.
While some might say that R is primarily a statistics system, we prefer to see it as an environment that allows a wide range of statistical techniques and methods to be implemented. R can easily be extended through packages, and the software distribution comes with eight such packages pre-installed. There are also many more packages available through the CRAN family of Internet sites, covering a vast array of modern statistical methods. R has its documentation system that is akin to LaTeX used for supplying comprehensive documentation online in several formats and can also be printed on paper.
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Version 2.8.1: N/A