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  "Title": "Diagonally Dominant Principal Component Analysis",
  "Version": "1.1",
  "Date": "2019-09-14",
  "Author": "Tracy Ke [aut], Lingzhou Xue [aut], Fan Yang [aut, cre]",
  "Maintainer": "Fan Yang <fyang1@uchicago.edu>",
  "Authors@R": "c(person(\"Tracy\", \"Ke\", email=\"zke@fas.harvard.edu\", role=c(\"aut\")),\nperson(\"Lingzhou\", \"Xue\", email=\"lzxue@psu.edu\", role=c(\"aut\")),\nperson(\"Fan\", \"Yang\", email=\"fyang1@uchicago.edu\", role=c(\"aut\", \"cre\")))",
  "Description": "Efficient procedures for fitting the DD-PCA (Ke et al.,\n2019, <arXiv:1906.00051>) by decomposing a large covariance\nmatrix into a low-rank matrix plus a diagonally dominant\nmatrix. The implementation of DD-PCA includes the convex\napproach using the Alternating Direction Method of Multipliers\n(ADMM) and the non-convex approach using the iterative\nprojection algorithm. Applications of DD-PCA to large\ncovariance matrix estimation and global multiple testing are\nalso included in this package.",
  "License": "GPL-2",
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    "User": "root"
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  "Date/Publication": "2019-09-14 19:50:02 UTC",
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      "page": "ddpca-package",
      "title": "Diagonally Dominant Principal Component Analysis",
      "topics": [
        "ddpca-package",
        "ddpca"
      ]
    },
    {
      "page": "DDHC",
      "title": "DD-HC test",
      "topics": [
        "DDHC"
      ]
    },
    {
      "page": "DDPCA_convex",
      "title": "Diagonally Dominant Principal Component Analysis using Convex approach",
      "topics": [
        "DDPCA_convex"
      ]
    },
    {
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      "title": "Diagonally Dominant Principal Component Analysis using Nonconvex approach",
      "topics": [
        "DDPCA_nonconvex"
      ]
    },
    {
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      "title": "Higher Criticism for detecting rare and weak signals",
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        "HCdetection"
      ]
    },
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      "title": "Projection onto the Diagonally Dominant Cone",
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    },
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