{
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  "Title": "Eigenvectors from Eigenvalues Sparse Principal Component\nAnalysis (EESPCA)",
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  "Maintainer": "H. Robert Frost <rob.frost@dartmouth.edu>",
  "Authors@R": "person(given = c(\"H.\", \"Robert\"),\nfamily = \"Frost\",\nrole = c(\"aut\", \"cre\"),\nemail = \"rob.frost@dartmouth.edu\")",
  "Description": "Contains logic for computing sparse principal components\nvia the EESPCA method, which is based on an approximation of\nthe eigenvector/eigenvalue identity. Includes logic to support\nexecution of the TPower and rifle sparse PCA methods, as well\nas logic to estimate the sparsity parameters used by EESPCA,\nTPower and rifle via cross-validation to minimize the\nout-of-sample reconstruction error. H. Robert Frost (2021)\n<doi:10.1080/10618600.2021.1987254>.",
  "License": "GPL (>= 2)",
  "Copyright": "Dartmouth College",
  "Encoding": "UTF-8",
  "NeedsCompilation": "no",
  "Packaged": {
    "Date": "2026-05-27 14:29:45 UTC",
    "User": "root"
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  "Author": "H. Robert Frost [aut, cre]",
  "Repository": "https://cranhaven.r-universe.dev",
  "Date/Publication": "2026-05-27 05:02:00 UTC",
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    "packages",
    "r-universe"
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    "description": "CRANhaven - Repository for Recently Archived CRAN Packages"
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    "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/EESPCA"
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    "extra/citation.json",
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    "computeApproxNormSquaredEigenvector",
    "computeResidualMatrix",
    "eespca",
    "eespcaCV",
    "eespcaForK",
    "powerIteration",
    "reconstruct",
    "reconstructionError",
    "rifleInit",
    "riflePCACV",
    "tpower",
    "tpowerPCACV"
  ],
  "_help": [
    {
      "page": "EESPCA-package",
      "title": "Eigenvectors",
      "topics": [
        "EESPCA-package"
      ]
    },
    {
      "page": "ComputeApproxNormSquaredEigenvector",
      "title": "Approximates the normed squared eigenvector loadings",
      "topics": [
        "computeApproxNormSquaredEigenvector"
      ]
    },
    {
      "page": "ComputeResidualMatrix",
      "title": "Calculates the residual matrix from the reduced rank reconstruction",
      "topics": [
        "computeResidualMatrix"
      ]
    },
    {
      "page": "EESPCA",
      "title": "Eigenvectors from Eigenvalues Sparse Principal Component Analysis (EESPCA)",
      "topics": [
        "eespca"
      ]
    },
    {
      "page": "EESPCACV",
      "title": "Cross-validation for Eigenvectors from Eigenvalues Sparse Principal Component Analysis (EESPCA)",
      "topics": [
        "eespcaCV"
      ]
    },
    {
      "page": "EESPCAForK",
      "title": "Multi-PC version of Eigenvectors from Eigenvalues Sparse Principal Component Analysis (EESPCA)",
      "topics": [
        "eespcaForK"
      ]
    },
    {
      "page": "PowerIteration",
      "title": "Power iteration method for calculating principal eigenvector and eigenvalue.",
      "topics": [
        "powerIteration"
      ]
    },
    {
      "page": "Reconstruct",
      "title": "Calculates the reduced rank reconstruction",
      "topics": [
        "reconstruct"
      ]
    },
    {
      "page": "ReconstructionError",
      "title": "Calculates the reduced rank reconstruction error",
      "topics": [
        "reconstructionError"
      ]
    },
    {
      "page": "RifleInit",
      "title": "Computes the initial eigenvector for the rifle method of Tan et al.",
      "topics": [
        "rifleInit"
      ]
    },
    {
      "page": "RiflePCACV",
      "title": "Sparsity parameter selection via cross-validation for rifle method of Tan et al.",
      "topics": [
        "riflePCACV"
      ]
    },
    {
      "page": "Tpower",
      "title": "Implementation of the Yuan and Zhang TPower method.",
      "topics": [
        "tpower"
      ]
    },
    {
      "page": "TpowerPCACV",
      "title": "Sparsity parameter selection for the Yuan and Zhang TPower method using cross-validation.",
      "topics": [
        "tpowerPCACV"
      ]
    }
  ],
  "_rundeps": [
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    "rifle"
  ],
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      "source": "EESPCA_Example.Rnw",
      "filename": "EESPCA_Example.pdf",
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      "engine": "utils::Sweave",
      "headings": [],
      "created": "2026-05-27 05:02:00",
      "modified": "2026-05-27 05:02:00",
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