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R

Home > Solution Platform > R
Open Source Scripting Platform for Data-Analysis, Statistics and Modeling
 
R is a scripting platform for statistical computation and graphics.  It consists of the R-scripting language and a run-time environment for graphics, debugging, access to system and network functions, and running script files. The Inference R Plug-In enables the R scripting language to be executed from within Inference Word by hosting the R-language runtime and collecting the output and graphics for sending to multiple destinations including Microsoft Word documents.
 
The R scripting platform is widely used for statistical method development and data analysis.  The R scripting platform is and open source project and is freely available under the GNU General Public License from the R-project. The syntax closely parallels the commercial S programming language developed by ATT labs, with semantics derived from the Scheme scripting platform.  The standard R distribution contains a broad range of functionality for a large number of statistical procedures.  Among these are: linear and generalized linear models, nonlinear regression models, time series analysis, classical parametric and nonparametric tests, clustering and smoothing. There is also a large set of functions which provide a flexible graphical environment for creating various kinds of data presentations.  Although R is mostly used for statistical computation and statistical method development, it can also be used as a general matrix calculation toolbox with comparable benchmark results to GNU Octave and its proprietary counterpart, MATLAB.
 
The R scripting platform is also highly extensible through the use of packages, which are user-submitted libraries and illustrative examples for specialized methods, specific areas of study, or novel graphical techniques. There are over 1,000 free packages available by download from the Comprehensive R Archive Network (CRAN).  Collections of packages in defined areas of application are available on the official R Task View pages.  Such collections include:
 

Cluster

Cluster Analysis & Finite Mixture Models

Econometrics

Computational Econometrics

Environmetrics

Analysis of ecological and environmental data

ExperimentalDesign

Design of Experiments (DoE) & Analysis of Experimental Data

Finance

Empirical Finance

Genetics

Statistical Genetics

Graphics

Graphic Displays & Dynamic Graphics & Graphic Devices & Visualization

gR

gRaphical models in R

MachineLearning

Machine Learning & Statistical Learning

Multivariate

Multivariate Statistics

Pharmacokinetics

Analysis of Pharmacokinetic Data

Psychometrics

Psychometric Models and Methods

SocialSciences

Statistics for the Social Sciences

Spatial

Analysis of Spatial Data

 

 
 
Learn More
Documents
Histograms and Density Plots.pdfHistograms and Density Plots
Dot and Bar Plots.pdfDot and Bar Plots
Line and Pie Charts.pdfLine and Pie Charts
Box and Scatter Plots.pdfBox and Scatter Plots
Combining Plots.pdfCombining Plots
Trellis Graphs.pdfTrellis Graphs
Probability and Mosaic Plots.pdfProbability and Mosaic Plots
Correlograms.pdfCorrelograms
Cluster Analysis.pdfCluster Analysis
Analysis of Michelson Data in R.pdfAnalysis of Michelson Data in R
Sample User Session.pdfSample User Session
Links
  R FAQ
  R for Windows FAQ
  R-Project Homepage
  CRAN Homepage
  R Graph Gallery
  R Wiki
Screenshots
Histograms and Density Plots
Analysis of Michelson Data
Sample User Session
 
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