{ "_id": "6a1bee481d7bb097a0a209f0", "Package": "Rankcluster", "Type": "Package", "Title": "Model-Based Clustering for Multivariate Partial Ranking Data", "Version": "0.98.0", "Date": "2022-11-11", "Authors@R": "c(person(\"Quentin\", \"Grimonprez\", role = c(\"aut\", \"cre\"), email = \"quentingrim@yahoo.fr\"),\nperson(\"Julien\", \"Jacques\", role = \"aut\", email = \"julien.jacques@univ-lyon2.fr\"),\nperson(\"Christophe\", \"Biernacki\", role = \"aut\", email = \"christophe.biernacki@inria.fr\"))", "Description": "Implementation of a model-based clustering algorithm for\nranking data (C. Biernacki, J. Jacques (2013)\n). Multivariate rankings as\nwell as partial rankings are taken into account. This algorithm\nis based on an extension of the Insertion Sorting Rank (ISR)\nmodel for ranking data, which is a meaningful and effective\nmodel parametrized by a position parameter (the modal ranking,\nquoted by mu) and a dispersion parameter (quoted by pi). 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