Abstract / Description of output
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.
| Original language | English |
|---|---|
| Pages (from-to) | 1-27 |
| Number of pages | 27 |
| Journal | Journal of Statistical Software |
| Volume | 58 |
| Issue number | 7 |
| Publication status | Published - Jun 2014 |
Keywords / Materials (for Non-textual outputs)
- Contingency tables
- Capture-recapture studies
- Reversible jump
- Log-linear models
- QA Mathematics
- QA76 Computer software
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Dive into the research topics of 'conting: an R package for Bayesian analysis of complete and incomplete contingency tables'. Together they form a unique fingerprint.Profiles
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Ruth King
- School of Mathematics - The Thomas Bayes Chair of Statistics
- Bayes Centre - Director of the Bayes Centre
Person: Academic: Research Active (Teaching)