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  "Package": "MatrixHMM",
  "Title": "Parsimonious Families of Hidden Markov Models for Matrix-Variate\nLongitudinal Data",
  "Version": "1.0.0",
  "Authors@R": "c(\nperson(given = \"Salvatore D.\",\nfamily = \"Tomarchio\",\nrole = c(\"aut\",\"cre\"),\nemail = \"daniele.tomarchio@unict.it\"))",
  "Description": "Implements three families of parsimonious hidden Markov\nmodels (HMMs) for matrix-variate longitudinal data using the\nExpectation-Conditional Maximization (ECM) algorithm. The\npackage supports matrix-variate normal, t, and contaminated\nnormal distributions as emission distributions. For each hidden\nstate, parsimony is achieved through the eigen-decomposition of\nthe covariance matrices associated with the emission\ndistribution. This approach results in a comprehensive set of\n98 parsimonious HMMs for each type of emission distribution.\nAtypical matrix detection is also supported, utilizing the\nfitted (heavy-tailed) models.",
  "License": "GPL (>= 3)",
  "Encoding": "UTF-8",
  "RoxygenNote": "7.3.2",
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  "NeedsCompilation": "no",
  "Packaged": {
    "Date": "2026-05-07 07:58:09 UTC",
    "User": "root"
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  "Author": "Salvatore D. Tomarchio [aut, cre]",
  "Maintainer": "Salvatore D. Tomarchio <daniele.tomarchio@unict.it>",
  "Repository": "https://cran.r-universe.dev",
  "Date/Publication": "2024-08-29 02:51:20 UTC",
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      "title": "Atypical Detection Points Using Matrix-Variate Contaminated Normal Hidden Markov Models",
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