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R logo. 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).
Bibliometrix is a package for the R statistical programming language for quantitative research in scientometrics and bibliometrics. [1]Bibliometrics is the application of quantitative analysis and statistics to publications such as journal articles and their accompanying citation counts.
The RStudio CRAN mirror download logs [11] show that the package is downloaded on average about 2,000 per month from those servers , [12] with a total of over 100,000 downloads since the first release, [13] according to RDocumentation.org, this puts the package in the 15th percentile of most popular R packages .
Ooms, Marius (2009). "Trends in Applied Econometrics Software Development 1985–2008: An Analysis of Journal of Applied Econometrics Research Articles, Software Reviews, Data and Code". Palgrave Handbook of Econometrics. Vol. 2: Applied Econometrics. Palgrave Macmillan. pp. 1321–1348. ISBN 978-1-4039-1800-0. Renfro, Charles G. (2004).
Data is entered into a spreadsheet interface [8] that can be imported into jamovi. If data are changed, all calculations and analyses affected by the change are automatically updated. [ 6 ] The software includes a multinomial test to determine whether observed data differs from researchers' predictions.
There are a few reviews of free statistical software. There were two reviews in journals (but not peer reviewed), one by Zhu and Kuljaca [26] and another article by Grant that included mainly a brief review of R. [27] Zhu and Kuljaca outlined some useful characteristics of software, such as ease of use, having a number of statistical procedures and ability to develop new procedures.
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Unlike analytics products offered by SAS Institute, R does not natively handle datasets larger than main memory.In 2010 Revolution Analytics introduced ScaleR, a package for Revolution R Enterprise designed to handle big data through a high-performance disk-based data store called XDF (not related to IBM's Extensible Data Format) and high performance computing across large clusters. [18]