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  "Title": "Clustering Multinomial Count Data under the Presence of\nCovariates",
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  "Authors@R": "c(person(given = \"Panagiotis\",\nfamily = \"Papastamoulis\",\nemail = \"papapast@yahoo.gr\",\nrole = c( \"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0001-9468-7613\")))",
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  "Description": "Methods for model-based clustering of multinomial counts\nunder the presence of covariates using mixtures of multinomial\nlogit models, as implemented in Papastamoulis (2023)\n<DOI:10.1007/s11634-023-00547-5>. These models are estimated\nunder a frequentist as well as a Bayesian setup using the\nExpectation-Maximization algorithm and Markov chain Monte Carlo\nsampling (MCMC), respectively. The (unknown) number of clusters\nis selected according to the Integrated Completed Likelihood\ncriterion (for the frequentist model), and estimating the\nnumber of non-empty components using overfitting mixture models\nafter imposing suitable sparse prior assumptions on the mixing\nproportions (in the Bayesian case), see Rousseau and Mengersen\n(2011) <DOI:10.1111/j.1467-9868.2011.00781.x>. In the latter\ncase, various MCMC chains run in parallel and are allowed to\nswitch states. The final MCMC output is suitably post-processed\nin order to undo label switching using the Equivalence Classes\nRepresentatives (ECR) algorithm, as described in Papastamoulis\n(2016) <DOI:10.18637/jss.v069.c01>.",
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