THE MODELS DISCUSSED in this book are rather easy to program, and students are encouraged to do some or all of the exercises by writing their own programs. The writing of programs requires a complete understanding of the problem and is therefore the best way to ensure that the material has been mastered.
A number of excellent programs are suitable for programming the MCMC algorithms described in this book. At this writing, the most popular seems to be R, which is a free software environment for statistical computing and graphics. There are versions for UNIX, Windows, and MacOS, and it may be downloaded from your preferred CRAN mirror. R is explained in a large number of books and online material. An excellent general introduction is Maindonald and Braun (2010), and Springer publishes a large number of titles in its “Use R!” series, some of which cover Bayesian methods. Another important feature of R is the extensive set of packages that provide tools for specialized tasks.
Two useful packages for Bayesian model fitting in R are:
• MCMCpack is available at http://mcmcpack.wustl.edu; it contains some of the models discussed in this book as well as some additional measurement and ecological inference models of interest to political scientists. Its lead developers are Andrew Martin, Kevin M. Quinn, and Jong Hee Park.
MCMCpack utilizes the coda package (http://cran.r-project.org/web/packages/coda/ coda.pdf) to summarize the MCMC output by preparing summaries, computing convergence diagnostics, and making plots. Functions in the coda package can be used to analyze any MCMC output.
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