{
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  "Package": "ddpca",
  "Type": "Package",
  "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",
  "NeedsCompilation": "no",
  "Packaged": {
    "Date": "2026-05-21 07:47:32 UTC",
    "User": "root"
  },
  "Repository": "https://cran.r-universe.dev",
  "Date/Publication": "2019-09-14 19:50:02 UTC",
  "RemoteUrl": "https://github.com/cran/ddpca",
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    "author": "Fan Yang <fyang1@uchicago.edu>",
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    "message": "version 1.1\n",
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    "email": "fyang1@uchicago.edu"
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  "_dependencies": [
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      "package": "RSpectra",
      "role": "Imports"
    },
    {
      "package": "Matrix",
      "role": "Imports"
    },
    {
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    },
    {
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    "type": "organization",
    "name": "cran",
    "description": "Unofficial read-only mirror of all CRAN R packages"
  },
  "_downloads": {
    "count": 209,
    "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/ddpca"
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  "_assets": [
    "extra/citation.cff",
    "extra/citation.html",
    "extra/citation.json",
    "extra/citation.txt",
    "extra/contents.json",
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    "manual.pdf"
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      "date": "2019-07-19"
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  "_exports": [
    "DDHC",
    "DDPCA_convex",
    "DDPCA_nonconvex",
    "HCdetection",
    "IHCDD",
    "ProjDD",
    "ProjSDD"
  ],
  "_help": [
    {
      "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"
      ]
    },
    {
      "page": "DDPCA_nonconvex",
      "title": "Diagonally Dominant Principal Component Analysis using Nonconvex approach",
      "topics": [
        "DDPCA_nonconvex"
      ]
    },
    {
      "page": "HCdetection",
      "title": "Higher Criticism for detecting rare and weak signals",
      "topics": [
        "HCdetection"
      ]
    },
    {
      "page": "IHCDD",
      "title": "IHC-DD test",
      "topics": [
        "IHCDD"
      ]
    },
    {
      "page": "ProjDD",
      "title": "Projection onto the Diagonally Dominant Cone",
      "topics": [
        "ProjDD"
      ]
    },
    {
      "page": "ProjSDD",
      "title": "Projection onto the Symmetric Diagonally Dominant Cone",
      "topics": [
        "ProjSDD"
      ]
    }
  ],
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  "_nocasepkg": "ddpca",
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