There has been dramatic growth in the development and application of
Bayesian inference in statistics. Berger (2000) documents the increase
in Bayesian activity by the number of published research articles, the
number of books,
andtheextensivenumberofapplicationsofBayesianarticlesinapplied
disciplines such as science and engineering. One reason for the dramatic
growth in Bayesian modeling is the availab- ity of computational
algorithms to compute the range of integrals that are necessary in a
Bayesian posterior analysis. Due to the speed of modern c- puters, it is
now possible to use the Bayesian paradigm to 't very complex models that
cannot be 't by alternative frequentist methods. To 't Bayesian models,
one needs a statistical computing environment. This environment should
be such that one can: write short scripts to de?ne a Bayesian model use
or write functions to summarize a posterior distribution use functions
to simulate from the posterior distribution construct graphs to
illustrate the posterior inference An environment that meets these
requirements is the R system. R provides a wide range of functions for
data manipulation, calculation, and graphical d- plays. Moreover, it
includes a well-developed, simple programming language that users can
extend by adding new functions. Many such extensions of the language in
the form of packages are easily downloadable from the Comp- hensive R
Archive Network (CRAN)
