R

R is a free open source programing language and statistical environment maintained by the GNU. R contains powerful libraries for parallel computing and is especially adept at computing on large data sets. The R website maintains and distributes the necessary files to install on all major operating systems. The R executable itself operates inside a command line environment; however, an optional and separate program Rstudio can provide a graphical development environment. Bayaan, R.H. Analyzing Linguistic Data: A Practical Introduction to Statistics . Cambridge: Cambridge University Press, 2008.

The R distribution contains functionality for a large number of statistical procedures. Among these are: linear and generalized linear models, nonlinear regression models, time series analysis, classical parametric and nonparametric tests, clustering and smoothing. There is also a large set of functions which provide a flexible graphical environment for creating various kinds of data presentations. A package specification allows the production of loadable modules for specific purposes, and several thousand contributed packages are made available through the CRAN sites (see for the current members). Since mid-1997 there has been a core group who can modify the R source code archive, listed in file doc/AUTHORS. This file has been minimally revised since the release of R 1.0.0. The present version implements most of the functionality in the 1988 book "The New S Language" (the "Blue Book") and many of the applications. In addition, we have implemented a large part of the functionality from the 1992 book "Statistical Models in S" (the "White Book") and the 1998 book "Programming with Data" (the "Green Book"). Our aim at the start of this project was to demonstrate that it was possible to produce an S-like environment which did not suffer from the memory-demands and performance problems which S has. Somewhat later, we started to turn R into a "real" system, but unfortunately we lost a large part of the efficiency advantage in the process, so have revised the memory management mechanism and implemented delayed loading of R objects. A lot of performance tuning has been done, including the ability to use tuned linear-algebra libraries. Longer-term goals include to explore new ideas: e.g. virtual objects and component-based programming, and expanding the scope of existing ones like formula-based interfaces. Further, we wish to get a handle on a general approach to graphical user interfaces (preferably with cross-platform portability), and to develop better 3-D and dynamic graphics.