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R packages are extensions to the R statistical programming language. R packages contain code, data, and documentation in a standardised collection format that can be installed by users of R, typically via a centralised software repository such as CRAN (the Comprehensive R Archive Network).
The tidyverse is a collection of open source packages for the R programming language introduced by Hadley Wickham [1] and his team that "share an underlying design philosophy, grammar, and data structures" of tidy data. [2] Characteristic features of tidyverse packages include extensive use of non-standard evaluation and encouraging piping. [3 ...
R is a programming language for statistical computing and data visualization. It has been adopted in the fields of data mining, bioinformatics and data analysis. [9] The core R language is augmented by a large number of extension packages, containing reusable code, documentation, and sample data. R software is open-source and free software.
With the release of version 0.3.0 in April 2016 [4] the use in production and research environments became more widespread. The package was reviewed several months later on the R blog The Beginner Programmer as "R provides a simple and very user friendly package named rnn for working with recurrent neural networks.", [5] which further increased usage.
OpenMx – A package for structural equation modeling running in R (programming language) OpenNN – A software library written in the programming language C++ which implements neural networks, a main area of deep learning research; Orange, a data mining, machine learning, and bioinformatics software
The following package management systems distribute the source code of their apps. Either the user must know how to compile the packages, or they come with a script that automates the compilation process. For example, in GoboLinux a recipe file contains information on how to download, unpack, compile and install a package using its Compile tool ...
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For the R programming language, the Comprehensive R Archive Network (CRAN) runs tests routinely. To understand how this is valuable, imagine a situation with two developers, Sally and John. Sally contributes a package A. Sally only runs the current version of the software under one version of Microsoft Windows, and has only tested it in that ...