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  "Title": "Heterogeneous Graphical Model for Non-Negative Data",
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  "Description": "Graphical model is an informative and powerful tool to\nexplore the conditional dependence relationships among\nvariables. The traditional Gaussian graphical model and its\nextensions either have a Gaussian assumption on the data\ndistribution or assume the data are homogeneous. However, there\nare data with complex distributions violating these two\nassumptions. For example, the air pollutant concentration\nrecords are non-negative and, hence, non-Gaussian. Moreover,\ndue to climate changes, distributions of these concentration\nrecords in different months of a year can be far different,\nwhich means it is uncertain whether datasets from different\nmonths are homogeneous. Methods with a Gaussian or homogeneous\nassumption may incorrectly model the conditional dependence\nrelationships among variables. Therefore, we propose a\nheterogeneous graphical model for non-negative data (HGMND) to\nsimultaneously cluster multiple datasets and estimate the\nconditional dependence matrix of variables from a non-Gaussian\nand non-negative exponential family in each cluster.",
  "Authors@R": "c(person(\"Jiaqi\" , \"Zhang\", role = c(\"aut\", \"cre\"), email = \"boarzhang@gmail.com\"),\nperson(\"Xinyan\", \"Fan\"  , role = c(\"aut\")),\nperson(\"Yang\"  , \"Li\"   , role = c(\"aut\")))",
  "License": "GPL-3",
  "Encoding": "UTF-8",
  "LazyData": "true",
  "NeedsCompilation": "no",
  "Author": "Jiaqi Zhang [aut, cre], Xinyan Fan [aut], Yang Li [aut]",
  "Maintainer": "Jiaqi Zhang <boarzhang@gmail.com>",
  "Packaged": {
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  "Date/Publication": "2021-04-19 08:00:02 UTC",
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      "title": "Get the cluster structure of the HGMND estimate",
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