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conting: An R Package for Bayesian Analysis of Complete and Incomplete Contingency Tables

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  • Overstall, Antony M.
  • King, Ruth

Abstract

The aim of this paper is to demonstrate the R package conting for the Bayesian analysis of complete and incomplete contingency tables using hierarchical log-linear models. This package allows a user to identify interactions between categorical factors (via complete contingency tables) and to estimate closed population sizes using capture-recapture studies (via incomplete contingency tables). The models are fitted using Markov chain Monte Carlo methods. In particular, implementations of the Metropolis-Hastings and reversible jump algorithms appropriate for log-linear models are employed. The conting package is demonstrated on four real examples.

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  • Overstall, Antony M. & King, Ruth, 2014. "conting: An R Package for Bayesian Analysis of Complete and Incomplete Contingency Tables," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 58(i07).
    • Handle: RePEc:jss:jstsof:v:058:i07
    DOI: http://hdl.handle.net/10.18637/jss.v058.i07
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    1. S. P. Brooks & P. Giudici & G. O. Roberts, 2003. "Efficient construction of reversible jump Markov chain Monte Carlo proposal distributions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(1), pages 3-39, January.
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    2. Chang Xuan Mao & Ruochen Huang & Sijia Zhang, 2017. "Petersen estimator, Chapman adjustment, list effects, and heterogeneity," Biometrics, The International Biometric Society, vol. 73(1), pages 167-173, March.

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