Anyone can contribute to the development of the programming language and make a package. These contributions are publicly available to be accessed by anyone, and any package that submitted to CRAN will have significant traction in the R community. The main advantage of getting a package on CRAN is that it will be easier for users to install. To “submit” a package to CRAN, you have to check the CRAN Repository Policy to make sure that your submission meets these policies. For more information, you can contact the CRAN-submissions@R-project.org.

Unix came first as a first portable OS then where the GNU isn't actually an OS, but It's a set of rules that govern free software then later invented Linux as an alternative to the paid for operating systems. To have a better understanding of the Unix timeline check this chart (https://upload.wikimedia.org/wikipedia/commons/c/cd/Unix_timeline.en.svg). - Unix is a multi‐user computer OS created originally in 1969 by researchers of AT&T at Bell Labs, and the company charged for Unix while Universities got Unix for free. - GNU was developed by the GNU project in 1983 and was the original focus of the Free Software Foundation. - Linux refers to the family of Unix‐like computer operating systems, and originally was written in 1991 by Linus Torvalds.

It is a stand for Comprehensive R Archive Network. CRAN is a network of ftp and web servers around the world where you can find the R source code, R manuals and documentation, and contributed packages. In other words, CRAN isn’t a single website, but it’s a collection of web servers that store identical, up-to-date, versions of code and documentation for R. Thus, each web server is called a mirror. The idea is that you choose the mirror that is located nearest to where you are, which reduces international or long-distance Internet traffic. You can find a list of CRAN mirrors at CRAN mirrors.

R runs fine on Windows, Linux, and MAC, and it is difficult to give a definitive answer which one of these three OS's is better.

These are some books by the R Core Team members: John M. Chambers (2008), “Software for Data Analysis: Programming with R”. Springer, New York, ISBN 978-0-387-75935-7, http://statweb.stanford.edu/~jmc4/Rbook/. Peter Dalgaard (2008), “Introductory Statistics with R”, 2nd edition. Springer, ISBN 978-0-387-79053-4, http://publicifsv.sund.ku.dk/~pd/ISwR.html. Robert Gentleman (2008), “R Programming for Bioinformatics”. Chapman & Hall/CRC, Boca Raton, FL, ISBN 978-1-420-06367-7, https://www.bioconductor.org/pub/RBioinf/. Stefano M. Iacus (2008), “Simulation and Inference for Stochastic Differential Equations: With R Examples”. Springer, New York, ISBN 978-0-387-75838-1. Deepayan Sarkar (2007), “Lattice: Multivariate Data Visualization with R”. Springer, New York, ISBN 978-0-387-75968-5. W. John Braun and Duncan J. Murdoch (2007), “A First Course in Statistical Programming with R”. Cambridge University Press, Cambridge, ISBN 978-0521872652. P. Murrell (2005), “R Graphics”, Chapman & Hall/CRC, ISBN: 1-584-88486-X, https://www.stat.auckland.ac.nz/~paul/RGraphics/rgraphics.html. William N. Venables and Brian D. Ripley (2002), “Modern Applied Statistics with S” (4th edition). Springer, ISBN 0-387-95457-0, https://www.stats.ox.ac.uk/pub/MASS4/. Jose C. Pinheiro and Douglas M. Bates (2000), “Mixed-Effects Models in S and S-Plus”. Springer, ISBN 0-387-98957-0. Last, but not least, Ross’ and Robert’s experience in designing and implementing R is described in Ihaka & Gentleman (1996), “R: A Language for Data Analysis and Graphics”, Journal of Computational and Graphical Statistics, 5, 299–314.

R is a language and environment for a wide variety of statistical computing such as (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, …) and graphical techniques, and is highly extensible. R is a free software under the terms of the Free Software Foundation’s GNU General Public License in source code form. It compiles and runs on a wide variety of UNIX platforms and similar systems (including FreeBSD and Linux), Windows and MacOS.It can be considered as a different implementation of S with some important differences, but much code written for S runs unaltered under R. The S language is often the vehicle of choice for research in statistical methodology, and R provides an Open Source route to participation in that activity. for more information check the R project homepage: https://www.r-project.org/

Yes, there is online documentation for most the packages which explains the functions and variables. It is also existing in R, and it can be printed on-screen by typing help(name) (or ?name) at the R prompt, where name is the name of the topic help is sought for. This documentation can also be made available as one reference manual for on-line reading in HTML and PDF formats, and as hardcopy via LaTeX.