MCMCpack Markov chain Monte Carlo in strongRstrong - mnsu.edu.pdfVIP

JSS Journal of Statistical Software MMMMMM YYYY, Volume VV, Issue II. / MCMCpack: Markov chain Monte Carlo in R Andrew D. Martin Kevin M. Quinn Jong Hee Park Washington University in St. Louis Harvard University University of Chicago Abstract We introduce MCMCpack (Martin and Quinn 2007), an R package that contains func- tions to perform Bayesian inference using posterior simulation for a number of statistical models. In addition to code that can be used to fit commonly used models, MCMCpack also contains some useful utility functions, including some additional density functions and pseudo-random number generators for statistical distributions, a general purpose Metropolis sampling algorithm, and tools for visualization. Keywords : Bayesian inference, Markov chain Monte Carlo, R. 1. Introduction The Bayesian paradigm for statistical inference is appealing to researchers on both theoretical (Jeffreys 1998 [1961]; Bernardo and Smith 1994) and practical (Gelman, Carlin, Stern, and Rubin 2003) grounds. The interest in Bayesian methods in the social sciences is growing, and a number of researchers are using these approaches in substantive applications dealing with: deliberative bodies (Clinton, Jackman, and Rivers 2004; Martin and Quinn 2002), economic performance (Western 1998), income dynamics (Geweke and Keane 2000), legislative redistricting (Gelman and King 1990), mass voting (King, Rosen, and Tanner 1999), party competition (Quinn, Martin, and Whitford 1999; Quinn and Martin 2002), and social networks (Hoff, Raftery, and Handcock 2002; Hoff and Ward 2004). Despite t