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Related Concept Videos

Introduction to R01:11

Introduction to R

R is a powerful software environment for statistical computing and graphics. Originating as an implementation of the S language, developed at Bell Laboratories, R has evolved into a robust, open-source statistical software favored by statisticians and data scientists worldwide. Its comprehensive suite includes data manipulation, calculation, and graphical display capabilities, making it versatile for data analysis and visualization. Its programming language is at the core of R's functionality,...
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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
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Image analysis and statistical inference in neuroimaging with R.

K Tabelow1, J D Clayden, P Lafaye de Micheaux

  • 1Weierstrass Institute, Berlin, Germany. tabelow@wias-berlin.de

Neuroimage
|January 18, 2011
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Summary
This summary is machine-generated.

R, a statistical computing environment, offers specialized packages for analyzing neuroimaging data, including functional MRI and diffusion tensor imaging. This review explores R

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Area of Science:

  • Neuroimaging
  • Statistical Computing
  • Data Analysis

Background:

  • R is a powerful, open-source statistical computing and graphics environment, an implementation of the S language.
  • It runs on major operating systems and is part of the GNU project.
  • Specialized R packages are increasingly used for advanced data analysis.

Purpose of the Study:

  • To highlight R packages developed for neuroimaging data analysis.
  • To review the methodologies and capabilities of these R packages.
  • To summarize current trends in R-based neuroimaging software development.

Main Methods:

  • Identification and review of R packages for neuroimaging.
  • Analysis of package methodologies for functional MRI, diffusion tensor imaging, and dynamic contrast-enhanced MRI.
  • Overview of R package functionalities for neuroimaging data.

Main Results:

  • Several R packages are available for analyzing functional MRI, diffusion tensor imaging, and dynamic contrast-enhanced MRI data.
  • These packages offer diverse methodologies and capabilities for neuroimaging research.
  • Active development in R for neuroimaging software is ongoing.

Conclusions:

  • R provides a versatile environment for neuroimaging data analysis.
  • The R ecosystem offers valuable tools for researchers in functional MRI, diffusion tensor imaging, and DCE-MRI.
  • Continued development in R promises further advancements in neuroimaging software.