Show simple item record

Files in this item

Thumbnail

Item metadata

dc.contributor.authorOverstall, Antony
dc.contributor.authorKing, Ruth
dc.date.accessioned2014-07-21T13:31:02Z
dc.date.available2014-07-21T13:31:02Z
dc.date.issued2014-06
dc.identifier.citationOverstall , A & King , R 2014 , ' conting : an R package for Bayesian analysis of complete and incomplete contingency tables ' , Journal of Statistical Software , vol. 58 , no. 7 , pp. 1-27 . https://doi.org/10.18637/jss.v058.i07en
dc.identifier.otherPURE: 41270428
dc.identifier.otherPURE UUID: 356e0069-5430-467f-8306-c6576b9c0cab
dc.identifier.otherScopus: 84920749060
dc.identifier.otherWOS: 000341584600001
dc.identifier.urihttps://hdl.handle.net/10023/5050
dc.description.abstractThe 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.
dc.language.isoeng
dc.relation.ispartofJournal of Statistical Softwareen
dc.rights© 2014 The Authors. This work is licensed under the licenses Paper: Creative Commons Attribution 3.0 Unported License.en
dc.subjectContingency tablesen
dc.subjectCapture-recapture studiesen
dc.subjectReversible jumpen
dc.subjectLog-linear modelsen
dc.subjectQA Mathematicsen
dc.subjectQA76 Computer softwareen
dc.subject.lccQAen
dc.subject.lccQA76en
dc.titleconting : an R package for Bayesian analysis of complete and incomplete contingency tablesen
dc.typeJournal articleen
dc.contributor.sponsorMedical Research Councilen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews. School of Mathematics and Statisticsen
dc.contributor.institutionUniversity of St Andrews. Scottish Oceans Instituteen
dc.contributor.institutionUniversity of St Andrews. Centre for Research into Ecological & Environmental Modellingen
dc.identifier.doihttps://doi.org/10.18637/jss.v058.i07
dc.description.statusPeer revieweden
dc.identifier.urlhttp://www.jstatsoft.org/v58/i07en
dc.identifier.grantnumberPO: MCZ907778en


This item appears in the following Collection(s)

Show simple item record