{ "_id": "6a1f17d6b401979e7341ebc0", "Package": "HGMND", "Type": "Package", "Title": "Heterogeneous Graphical Model for Non-Negative Data", "Version": "0.1.0", "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 ", "Packaged": { "Date": "2026-05-17 05:53:08 UTC", "User": "root" }, "Config/pak/sysreqs": "libicu-dev", "Repository": "https://zjq-ruc.r-universe.dev", "Date/Publication": "2021-04-19 08:00:02 UTC", "RemoteUrl": "https://github.com/cran/HGMND", "RemoteRef": "HEAD", "RemoteSha": "40027c4d77d1ea60f80abb66bc91717f6ad1c811", "MD5sum": 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