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  "Authors@R": "c(person(\"Jing\", \"Li\", \nemail = \"jli178@crimson.ua.edu\", role = c(\"aut\", \"cre\")),\nperson(\"Yana\", \"Melnykov\",\nemail = \"ymelnykov@ua.edu\", role = \"aut\"))",
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  "Description": "Generating, evaluating, and selecting initialization\nstrategies for Gaussian Mixture Models (GMMs), along with\nfunctions to run the Expectation-Maximization (EM) algorithm.\nInitialization methods are compared using log-likelihood, and\nthe best-fitting model can be selected using BIC. Methods build\non initialization strategies for finite mixture models\ndescribed in Michael and Melnykov (2016)\n<doi:10.1007/s11634-016-0264-8> and Biernacki et al. (2003)\n<doi:10.1016/S0167-9473(02)00163-9>, and on the EM algorithm of\nDempster et al. (1977)\n<doi:10.1111/j.2517-6161.1977.tb01600.x>. Background on\nmodel-based clustering includes Fraley and Raftery (2002)\n<doi:10.1198/016214502760047131> and McLachlan and Peel (2000,\nISBN:9780471006268).",
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      "title": "Optimal Initial Value for Gaussian Mixture Model",
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        "GMMinit"
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      "title": "Select the Best Gaussian Mixture Model (GMM) Based on BIC",
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      "title": "Select the Best Initialization Method for a Gaussian Mixture Model (GMM)",
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      "title": "Initialize Parameters for the EM Algorithm in Gaussian Mixture Models",
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      "title": "Expectation-Maximization (EM) Algorithm for Gaussian Mixture Models",
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