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Programming with Big Data in R (pbdR) [1] is a series of R packages and an environment for statistical computing with big data by using high-performance statistical computation. [2] [3] The pbdR uses the same programming language as R with S3/S4 classes and methods which is used among statisticians and data miners for developing statistical ...
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.
The terms 'computational statistics' and 'statistical computing' are often used interchangeably, although Carlo Lauro (a former president of the International Association for Statistical Computing) proposed making a distinction, defining 'statistical computing' as "the application of computer science to statistics", and 'computational ...
RStudio IDE (or RStudio) is an integrated development environment for R, a programming language for statistical computing and graphics. It is available in two formats: RStudio Desktop is a regular desktop application while RStudio Server runs on a remote server and allows accessing RStudio using a web browser.
Rmetrics is a free and open-source software project designed for teaching computational finance.Rmetrics is based primarily on the statistical R programming language, but does contain contributions in other programming languages, such as Fortran, C, and C++.
WarpPLS – statistics package used in structural equation modeling; Wolfram Language [6] – the computer language that evolved from the program Mathematica. It has similar statistical capabilities as Mathematica. World Programming System (WPS) – statistical package that supports the use of Python, R and SAS languages within a single user ...
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 goal is to extend R for distributed computing, while retaining the simplicity and look-and-feel of R. Distributed R consists of the following components: Distributed data structures: Distributed R extends R's common data structures such as array, data.frame, and list to store data across multiple nodes. The corresponding Distributed R data ...