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  "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>.",
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  "Copyright": "Dartmouth College",
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  "NeedsCompilation": "no",
  "Packaged": {
    "Date": "2026-05-20 09:16:48 UTC",
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    "computeResidualMatrix",
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    "eespcaCV",
    "eespcaForK",
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    "reconstruct",
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      "page": "EESPCA-package",
      "title": "Eigenvectors",
      "topics": [
        "EESPCA-package"
      ]
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      "page": "ComputeApproxNormSquaredEigenvector",
      "title": "Approximates the normed squared eigenvector loadings",
      "topics": [
        "computeApproxNormSquaredEigenvector"
      ]
    },
    {
      "page": "ComputeResidualMatrix",
      "title": "Calculates the residual matrix from the reduced rank reconstruction",
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      ]
    },
    {
      "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"
      ]
    },
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      "page": "PowerIteration",
      "title": "Power iteration method for calculating principal eigenvector and eigenvalue.",
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      ]
    },
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      "title": "Calculates the reduced rank reconstruction",
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      ]
    },
    {
      "page": "ReconstructionError",
      "title": "Calculates the reduced rank reconstruction error",
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      ]
    },
    {
      "page": "RifleInit",
      "title": "Computes the initial eigenvector for the rifle method of Tan et al.",
      "topics": [
        "rifleInit"
      ]
    },
    {
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      "title": "Sparsity parameter selection via cross-validation for rifle method of Tan et al.",
      "topics": [
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    },
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      "title": "Implementation of the Yuan and Zhang TPower method.",
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      ]
    },
    {
      "page": "TpowerPCACV",
      "title": "Sparsity parameter selection for the Yuan and Zhang TPower method using cross-validation.",
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      ]
    }
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