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The group of packages strives to provide a cohesive collection of functions to deal with common data science tasks, including data import, cleaning, transformation and visualisation (notably with the ggplot2 package). The R Infrastructure packages [31] support coding and the development of R packages and as of 2021-05-04, Metacran [17] lists 16 ...
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.
Harwell Subroutine Library is a collection of Fortran 77 and 95 codes that address core problems in numerical analysis. LAPACK, [6] [7] the Linear Algebra PACKage, is a software library for numerical computing originally written in FORTRAN 77 and now written in Fortran 90.
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 ...
Characteristic features of tidyverse packages include extensive use of non-standard evaluation and encouraging piping. [3] [4] [5] As of November 2018, the tidyverse package and some of its individual packages comprise 5 out of the top 10 most downloaded R packages. [6] The tidyverse is the subject of multiple books and papers.
SuperCROSS – comprehensive statistics package with ad-hoc, cross tabulation analysis; Systat – general statistics package; The Unscrambler – free-to-try commercial multivariate analysis software for Windows; Unistat – general statistics package that can also work as Excel add-in; WarpPLS – statistics package used in structural ...
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.
A software package development process is a system for developing software packages.Such packages are used to reuse and share code, e.g., via a software repository.A package development process includes a formal system for package checking that usually exposes bugs, thereby potentially making it easier to produce trustworthy software (Chambers' prime directive). [1]