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  "Package": "MGMM",
  "Title": "Missingness-Aware Gaussian Mixture Models",
  "Date": "2026-02-26",
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  "Author": "Zachary McCaw [aut, cre] (ORCID:\n<https://orcid.org/0000-0002-2006-9828>)",
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  "Description": "Parameter estimation and classification for Gaussian\nMixture Models (GMMs) in the presence of missing data. This\npackage complements existing implementations by allowing for\nboth missing elements in the input vectors and full (as opposed\nto strictly diagonal) covariance matrices. Estimation is\nperformed using an expectation conditional maximization\nalgorithm that accounts for missingness of both the cluster\nassignments and the vector components. The output includes the\nmarginal cluster membership probabilities; the mean and\ncovariance of each cluster; the posterior probabilities of\ncluster membership; and a completed version of the input data,\nwith missing values imputed to their posterior expectations.\nFor additional details, please see McCaw ZR, Julienne H,\nAschard H. \"Fitting Gaussian mixture models on incomplete\ndata.\" <doi:10.1186/s12859-022-04740-9>.",
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      "title": "Calinski-Harabasz Index",
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      "title": "Davies-Bouldin Index",
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        "DavBou"
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    {
      "page": "FitGMM",
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