{ "_id": "6aca7e4ffe5cc25786e8a355", "Package": "fabMix", "Type": "Package", "Title": "Overfitting Bayesian Mixtures of Factor Analyzers with\nParsimonious Covariance and Unknown Number of Components", "Version": "5.1", "Date": "2024-02-12", "Authors@R": "c(person(given = \"Panagiotis\",\nfamily = \"Papastamoulis\",\nemail = \"papapast@yahoo.gr\",\nrole = c( \"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0001-9468-7613\")))", "Maintainer": "Panagiotis Papastamoulis ", "Description": "Model-based clustering of multivariate continuous data\nusing Bayesian mixtures of factor analyzers (Papastamoulis\n(2019) (2018)\n). The number of clusters is\nestimated using overfitting mixture models (Rousseau and\nMengersen (2011) ):\nsuitable prior assumptions ensure that asymptotically the extra\ncomponents will have zero posterior weight, therefore, the\ninference is based on the ``alive'' components. A Gibbs sampler\nis implemented in order to (approximately) sample from the\nposterior distribution of the overfitting mixture. A prior\nparallel tempering scheme is also available, which allows to\nrun multiple parallel chains with different prior distributions\non the mixture weights. These chains run in parallel and can\nswap states using a Metropolis-Hastings move. Eight different\nparameterizations give rise to parsimonious representations of\nthe covariance per cluster (following Mc Nicholas and Murphy\n(2008) ). The model\nparameterization and number of factors is selected according to\nthe Bayesian Information Criterion. 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