• JGR: (pronounced Jaguar) is a unified graphical user interface for R.
• JRI: Java to R interface
• rJava: is the R to Java interface:
• J/QTL

  J/qtl is a Java GUI for the popular QTL data analysis software R/qtl.  It provides a flexible and powerful working environment for users to perform a variety of tasks.

  Software for mapping quantitative trait loci in experimental crosses. Pre-compiled binaries are available for Windows, Linux and Mac as well as unix source code.

• R/qtl
• R/mannova
  R/maanova is an extensible, interactive environment for Microarray analysis. It is implemented as an add-on package for the freely available and widely used statistical language/software R
• 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.

  R is mostly written in R and at the lower levels is written in C. This is from the README in the source:

  The core of R is an interpreted computer language with a syntax
  superficially similar to C, but which is actually a "functional
  programming language" with capabilities similar to Scheme. The
  language allows branching and looping as well as modular programming
  using functions. Most of the user-visible functions in R are written
  in R, calling upon a smaller set of internal primitives. It is
  possible for the user to interface to procedures written in C or
  Fortran languages for efficiency, and also to write additional
  primitives.

  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.

I thought this was an interesting description of R from a Ruby programmer: http://sciruby.codeforpeople.com/sr.cgi/BioProjects

R's strength comes mostly from its rich and well tested libraries. If you have to solve a problem it can often be done in a few lines of R code and that can be a big gain. On the other hand R's interpreter is slow. A loop iterating 1 million records took 36 hours in plain R. Rewriting the routine to use built in functions brought that down to a minute. But to use built in functions cleverly took a working day - it would have been faster to program in Ruby. Also Ruby has much better support for writing maintainable code and unit tests. Finally the OOP implementation of R is thin - it hardly deserves the name.