From BeSTGRID

[edit] R

R is a language for statistical computation initially written by Robert Gentleman and Ross Ihaka—also known as "R & R" of the Statistics Department of the University of Auckland. It is gaining traction as the standard language for statistical computation.

R is a GNU project which is similar to the S language and environment which was developed at Bell Laboratories (formerly AT&T, now Lucent Technologies) by John Chambers and colleagues. R can be considered as a different implementation of S. There are some important differences, but much code written for S runs unaltered under R.

R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, ...) and graphical techniques, and is highly extensible. 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.

One of R's strengths is the ease with which well-designed publication-quality plots can be produced, including mathematical symbols and formulae where needed. Great care has been taken over the defaults for the minor design choices in graphics, but the user retains full control.

R is available as 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.

R is supported by BeSTGRID and has been promoted for ubiquitous installation across BeSTGRID computation resources.

[edit] CRAN

R packages are distributed on the Comprehensive R Archive Network (CRAN), there is a local mirror available on the KAREN network at The University of Auckland, so there should be no need for BeSTGRID or KAREN members to set up local mirrors.

[edit] Submitting R jobs to BeSTGRID

R scripts can be run on BeSTGRID computation resources using Grisu the desktop application , or GriCli the command line application. More details can be found on the R Tutorial page.