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  "Title": "Robust and Flexible Model-Based Clustering for Data Sets with\nMissing Values at Random",
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  "Authors@R": "c(person(given = \"Hung\",\nfamily = \"Tong\",\nrole = c(\"aut\", \"cre\"),\nemail = \"hungtongmx@gmail.com\"),\nperson(given = \"Cristina\",\nfamily = \"Tortora\",\nrole = c(\"aut\", \"ths\", \"dgs\"),\nemail = \"cristina.tortora@sjsu.edu\"))",
  "Description": "Implementations of various robust and flexible model-based\nclustering methods for data sets with missing values at random\n(Tong and Tortora, 2025, <doi:10.18637/jss.v115.i03>). Two main\nmodels are: Multivariate Contaminated Normal Mixture (MCNM,\nTong and Tortora, 2022, <doi:10.1007/s11634-021-00476-1>) and\nMultivariate Generalized Hyperbolic Mixture (MGHM, Wei et al.,\n2019, <doi:10.1016/j.csda.2018.08.016>). Mixtures via some\nspecial or limiting cases of the multivariate generalized\nhyperbolic distribution are also included: Normal-Inverse\nGaussian, Symmetric Normal-Inverse Gaussian, Skew-Cauchy,\nCauchy, Skew-t, Student's t, Normal, Symmetric Generalized\nHyperbolic, Hyperbolic Univariate Marginals, Hyperbolic, and\nSymmetric Hyperbolic. Funding: This work was partially\nsupported by the National Science foundation NSF Grant NO.\n2209974.",
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