This page last changed on Apr 01, 2008 by stepheneb.
  • 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

    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

I thought this was an interesting description of R from a Ruby programmer:

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.

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