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ggmcmc: Analysis of MCMC Samples and Bayesian Inference

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  • Fernández-i-Marín, Xavier

Abstract

ggmcmc is an R package for analyzing Markov chain Monte Carlo simulations from Bayesian inference. By using a well known example of hierarchical/multilevel modeling, the article reviews the potential uses and options of the package, ranging from classical convergence tests to caterpillar plots or posterior predictive checks.

Suggested Citation

  • Fernández-i-Marín, Xavier, 2016. "ggmcmc: Analysis of MCMC Samples and Bayesian Inference," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 70(i09).
    • Handle: RePEc:jss:jstsof:v:070:i09
    DOI: http://hdl.handle.net/10.18637/jss.v070.i09
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    References listed on IDEAS

    as
    1. Brian Greenhill & Michael D. Ward & Audrey Sacks, 2011. "The Separation Plot: A New Visual Method for Evaluating the Fit of Binary Models," American Journal of Political Science, John Wiley & Sons, vol. 55(4), pages 991-1002, October.
    2. Martin, Andrew D. & Quinn, Kevin M. & Park, Jong Hee, 2011. "MCMCpack: Markov Chain Monte Carlo in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 42(i09).
    3. Smith, Brian J., 2007. "boa: An R Package for MCMC Output Convergence Assessment and Posterior Inference," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 21(i11).
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