{
  "_id": "6a229070cd65a98ecbd559fe",
  "Package": "jeek",
  "Type": "Package",
  "Date": "2018-07-03",
  "Title": "A Fast and Scalable Joint Estimator for Integrating Additional\nKnowledge in Learning Multiple Related Sparse Gaussian\nGraphical Models",
  "Version": "1.1.1",
  "Authors@R": "c(person(\"Beilun\", \"Wang\", role = c(\"aut\", \"cre\"), email = \"bw4mw@virginia.edu\"), person(\"Yanjun\", \"Qi\", role = \"aut\", email = \"yanjun@virginia.edu\"),person(\"Zhaoyang\", \"Wang\", role = c(\"aut\"), email = \"zw4dn@virginia.edu\"))",
  "Maintainer": "Beilun Wang <bw4mw@virginia.edu>",
  "Description": "Provides a fast and scalable joint estimator for\nintegrating additional knowledge in learning multiple related\nsparse Gaussian Graphical Models (JEEK). The JEEK algorithm can\nbe used to fast estimate multiple related precision matrices in\na large-scale. For instance, it can identify multiple gene\nnetworks from multi-context gene expression datasets. By\nperforming data-driven network inference from high-dimensional\nand heterogeneous data sets, this tool can help users\neffectively translate aggregated data into knowledge that take\nthe form of graphs among entities. Please run demo(jeek) to\nlearn the basic functions provided by this package. For further\ndetails, please read the original paper: Beilun Wang, Arshdeep\nSekhon, Yanjun Qi \"A Fast and Scalable Joint Estimator for\nIntegrating Additional Knowledge in Learning Multiple Related\nSparse Gaussian Graphical Models\" (ICML 2018)\n<arXiv:1806.00548>.",
  "License": "GPL-2",
  "Encoding": "UTF-8",
  "URL": "https://github.com/QData/jeek",
  "BugReports": "https://github.com/QData/jeek",
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  "NeedsCompilation": "no",
  "Packaged": {
    "Date": "2026-06-05 08:58:09 UTC",
    "User": "root"
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  "Author": "Beilun Wang [aut, cre], Yanjun Qi [aut], Zhaoyang Wang [aut]",
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  "Repository": "https://blw921.r-universe.dev",
  "Date/Publication": "2018-07-07 14:40:13 UTC",
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      "title": "A Fast and Scalable Joint Estimator for Integrating Additional Knowledge in Learning Multiple Related Sparse Gaussian Graphical Models",
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    },
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      "title": "A simulated toy dataset that includes 2 data matrices (from 2 related tasks).",
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    {
      "page": "jeek",
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