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R Programming Language

About: R Programming Language is a(n) research topic. Over the lifetime, 148 publication(s) have been published within this topic receiving 232170 citation(s).

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Open accessJournal Article
01 Jan 2014-MSOR connections
Abstract: Copyright (©) 1999–2012 R Foundation for Statistical Computing. Permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and this permission notice are preserved on all copies. Permission is granted to copy and distribute modified versions of this manual under the conditions for verbatim copying, provided that the entire resulting derived work is distributed under the terms of a permission notice identical to this one. Permission is granted to copy and distribute translations of this manual into another language, under the above conditions for modified versions, except that this permission notice may be stated in a translation approved by the R Core Team.

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Topics: R Programming Language (78%)

229,202 Citations


Open accessJournal ArticleDOI: 10.5964/EJOP.V7I4.163
John K. Kruschke1Institutions (1)
Abstract: There is an explosion of interest in Bayesian statistics, primarily because recently created computational methods have finally made Bayesian analysis obtainable to a wide audience. Doing Bayesian Data Analysis, A Tutorial Introduction with R and BUGS provides an accessible approach to Bayesian data analysis, as material is explained clearly with concrete examples. The book begins with the basics, including essential concepts of probability and random sampling, and gradually progresses to advanced hierarchical modeling methods for realistic data. The text delivers comprehensive coverage of all scenarios addressed by non-Bayesian textbooks--t-tests, analysis of variance (ANOVA) and comparisons in ANOVA, multiple regression, and chi-square (contingency table analysis). This book is intended for first year graduate students or advanced undergraduates. It provides a bridge between undergraduate training and modern Bayesian methods for data analysis, which is becoming the accepted research standard. Prerequisite is knowledge of algebra and basic calculus. Author website: http://www.indiana.edu/~kruschke/DoingBayesianDataAnalysis/ -Accessible, including the basics of essential concepts of probability and random sampling -Examples with R programming language and BUGS software -Comprehensive coverage of all scenarios addressed by non-bayesian textbooks- t-tests, analysis of variance (ANOVA) and comparisons in ANOVA, multiple regression, and chi-square (contingency table analysis). -Coverage of experiment planning -R and BUGS computer programming code on website -Exercises have explicit purposes and guidelines for accomplishment

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Topics: Bayesian statistics (57%), Bayesian probability (51%), R Programming Language (51%) ...read more

586 Citations


Open accessJournal ArticleDOI: 10.1093/BIOINFORMATICS/BTQ046
Gregoire Pau1, Florian Fuchs2, Oleg Sklyar, Michael Boutros  +1 moreInstitutions (2)
01 Apr 2010-Bioinformatics
Abstract: Summary: EBImage provides general purpose functionality for reading, writing, processing and analysis of images. Furthermore, in the context of microscopy-based cellular assays, EBImage offers tools to segment cells and extract quantitative cellular descriptors. This allows the automation of such tasks using the R programming language and use of existing tools in the R environment for signal processing, statistical modeling, machine learning and data visualization. Availability: EBImage is free and open source, released under the LGPL license and available from the Bioconductor project (http://www.bioconductor.org/packages/release/bioc/html/EBImage.html). Contact: gregoire.pau/at/ebi.ac.uk

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Topics: Bioconductor (58%), R Programming Language (53%)

483 Citations


Open accessJournal ArticleDOI: 10.18637/JSS.V046.I04
Abstract: Exploratory factor analysis is a widely used statistical technique in the social sciences. It attempts to identify underlying factors that explain the pattern of correlations within a set of observed variables. A statistical software package is needed to perform the calculations. However, there are some limitations with popular statistical software packages, like SPSS. The R programming language is a free software package for statistical and graphical computing. It oers many packages written by contributors from all over the world and programming resources that allow it to overcome the dialog limitations of SPSS. This paper oers an SPSS dialog written in the R programming language with the help of some packages, so that researchers with little or no knowledge in programming, or those who are accustomed to making their calculations based on statistical dialogs, have more options when applying factor analysis to their data and hence can adopt a better approach when dealing with ordinal, Likert-type data.

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188 Citations


Journal ArticleDOI: 10.1111/J.1755-0998.2012.03174.X
David J. Winter1Institutions (1)
Abstract: MMOD is a library for the R programming language that allows the calculation of the population differentiation measures D(est), G″(ST) and φ'(ST). R provides a powerful environment in which to conduct and record population genetic analyses but, at present, no R libraries provide functions for the calculation of these statistics from standard population genetic files. In addition to the calculation of differentiation measures, mmod can produce parametric bootstrap and jackknife samples of data sets for further analysis. By integrating with and complimenting the existing libraries adegenet and pegas, mmod extends the power of R as a population genetic platform.

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184 Citations


Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20221
202116
202017
201919
201816
201714

Top Attributes

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Topic's top 5 most impactful authors

Jan Vitek

4 papers, 10 citations

Jonathan M. Garibaldi

3 papers, 39 citations

Aviral Goel

3 papers, 10 citations

John K. Kruschke

2 papers, 590 citations

Jesse H. Krijthe

2 papers, 17 citations

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