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  "Title": "Fit Mixture Models Using the Expectation Maximisation (EM)\nAlgorithm",
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  "Date": "2023-01-28",
  "Authors@R": "c(\nperson(\"Murray\", \"Jorgensen\", , \"majmurr@gmail.com\", role = \"aut\"),\nperson(\"James\", \"Curran\", , \"j.curran@auckland.ac.nz\", role = c(\"cre\", \"ctb\"))\n)",
  "Description": "A set of functions which use the Expectation Maximisation\n(EM) algorithm (Dempster, A. P., Laird, N. M., and Rubin, D. B.\n(1977) <doi:10.1111/j.2517-6161.1977.tb01600.x> Maximum\nlikelihood from incomplete data via the EM algorithm, Journal\nof the Royal Statistical Society, 39(1), 1--22) to take a\nfinite mixture model approach to clustering. The package is\ndesigned to cluster multivariate data that have categorical and\ncontinuous variables and that possibly contain missing values.\nThe method is described in Hunt, L. and Jorgensen, M. (1999)\n<doi:10.1111/1467-842X.00071> Australian & New Zealand Journal\nof Statistics 41(2), 153--171 and Hunt, L. and Jorgensen, M.\n(2003) <doi:10.1016/S0167-9473(02)00190-1> Mixture model\nclustering for mixed data with missing information,\nComputational Statistics & Data Analysis, 41(3-4), 429--440.",
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  "Author": "Murray Jorgensen [aut],\nJames Curran [cre, ctb]",
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