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  "Package": "pcaMethods",
  "Maintainer": "Henning Redestig <henning.red@gmail.com>",
  "License": "GPL (>= 3)",
  "Title": "A collection of PCA methods",
  "LazyLoad": "Yes",
  "Author": "Wolfram Stacklies, Henning Redestig, Kevin Wright",
  "Authors@R": "c(person(given=\"Wolfram\",\nfamily=\"Stacklies\",\nrole=c(\"aut\")),\nperson(given=\"Henning\",\nfamily=\"Redestig\",\nemail=\"henning.red@gmail.com\",\ncomment=c(ORCID=\"0000-0003-2130-9288\"),\nrole=c(\"aut\",\"cre\")),\nperson(given=\"Kevin\",\nfamily=\"Wright\",\ncomment=c(ORCID=\"0000-0002-0617-8673\"),\nrole=\"aut\"))",
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  "Description": "Provides Bayesian PCA, Probabilistic PCA, Nipals PCA,\nInverse Non-Linear PCA and the conventional SVD PCA. A cluster\nbased method for missing value estimation is included for\ncomparison. BPCA, PPCA and NipalsPCA may be used to perform PCA\non incomplete data as well as for accurate missing value\nestimation. A set of methods for printing and plotting the\nresults is also provided. All PCA methods make use of the same\ndata structure (pcaRes) to provide a common interface to the\nPCA results. Initiated at the Max-Planck Institute for\nMolecular Plant Physiology, Golm, Germany.",
  "Version": "1.93.0",
  "URL": "https://github.com/hredestig/pcamethods",
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  "Collate": "'derrorHierarchic.R' 'errorHierarchic.R' 'AllClasses.R'\n'AllGenerics.R' 'BPCA_dostep.R' 'BPCA_initmodel.R' 'bpca.R'\n'checkData.R' 'forkNlpcaNet.R' 'kEstimate.R' 'kEstimateFast.R'\n'lineSearch.R' 'llsImpute.R' 'methods-ExpressionSet.R'\n'methods-nniRes.R' 'methods-pcaRes.R' 'nipalsPca.R' 'nlpca.R'\n'optiAlgCgd.R' 'orth.R' 'pca.R' 'pcaMethods-package.R' 'ppca.R'\n'prep.R' 'repmat.R' 'robustPca.R' 'sortFeatures.R'\n'svdImpute.R' 'vector2matrices.R' 'xval.R'",
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  "Repository": "https://mrcieu.r-universe.dev",
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    "scl",
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    "slplot",
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    "svdPca",
    "wasna"
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      "object": "metaboliteDataComplete",
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    {
      "page": "asExprSet",
      "title": "Convert pcaRes object to an expression set",
      "topics": [
        "asExprSet"
      ]
    },
    {
      "page": "biplot-methods",
      "title": "Plot a overlaid scores and loadings plot",
      "topics": [
        "biplot,pcaRes-method",
        "biplot-methods",
        "biplot.pcaRes"
      ]
    },
    {
      "page": "bpca",
      "title": "Bayesian PCA missing value estimation",
      "topics": [
        "bpca"
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    {
      "page": "BPCA_dostep",
      "title": "Do BPCA estimation step",
      "topics": [
        "BPCA_dostep"
      ]
    },
    {
      "page": "BPCA_initmodel",
      "title": "Initialize BPCA model",
      "topics": [
        "BPCA_initmodel"
      ]
    },
    {
      "page": "center-pcaRes-method",
      "title": "Get the centers of the original variables",
      "topics": [
        "center",
        "center,pcaRes-method"
      ]
    },
    {
      "page": "centered-pcaRes-method",
      "title": "Check centering was part of the model",
      "topics": [
        "centered",
        "centered,pcaRes-method"
      ]
    },
    {
      "page": "checkData",
      "title": "Do some basic checks on a given data matrix",
      "topics": [
        "checkData"
      ]
    },
    {
      "page": "completeObs-nniRes-method",
      "title": "Get the original data with missing values replaced with predicted values.",
      "topics": [
        "completeObs",
        "completeObs,nniRes-method",
        "completeObs,pcaRes-method"
      ]
    },
    {
      "page": "cvseg",
      "title": "Get CV segments",
      "topics": [
        "cvseg"
      ]
    },
    {
      "page": "cvstat-pcaRes-method",
      "title": "Get cross-validation statistics (e.g. Q^2).",
      "topics": [
        "cvstat",
        "cvstat,pcaRes-method"
      ]
    },
    {
      "page": "deletediagonals",
      "title": "Delete diagonals",
      "topics": [
        "deletediagonals"
      ]
    },
    {
      "page": "derrorHierarchic",
      "title": "Later",
      "topics": [
        "derrorHierarchic"
      ]
    },
    {
      "page": "dim.pcaRes",
      "title": "Dimensions of a PCA model",
      "topics": [
        "dim.pcaRes"
      ]
    },
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      "page": "DModX-pcaRes-method",
      "title": "DModX",
      "topics": [
        "DModX",
        "DModX,pcaRes-method"
      ]
    },
    {
      "page": "errorHierarchic",
      "title": "Later",
      "topics": [
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      ]
    },
    {
      "page": "fitted-methods",
      "title": "Extract fitted values from PCA.",
      "topics": [
        "fitted,pcaRes-method",
        "fitted-methods",
        "fitted.pcaRes"
      ]
    },
    {
      "page": "forkNlpcaNet",
      "title": "Complete copy of nlpca net object",
      "topics": [
        "forkNlpcaNet"
      ]
    },
    {
      "page": "getHierarchicIdx",
      "title": "Index in hiearchy",
      "topics": [
        "getHierarchicIdx"
      ]
    },
    {
      "page": "helix",
      "title": "A helix structured toy data set",
      "topics": [
        "helix"
      ]
    },
    {
      "page": "kEstimate",
      "title": "Estimate best number of Components for missing value estimation",
      "topics": [
        "kEstimate"
      ]
    },
    {
      "page": "kEstimateFast",
      "title": "Estimate best number of Components for missing value estimation",
      "topics": [
        "kEstimateFast"
      ]
    },
    {
      "page": "leverage-pcaRes-method",
      "title": "Extract leverages of a PCA model",
      "topics": [
        "leverage",
        "leverage,pcaRes-method"
      ]
    },
    {
      "page": "lineSearch",
      "title": "Line search for conjugate gradient",
      "topics": [
        "lineSearch"
      ]
    },
    {
      "page": "linr",
      "title": "Linear kernel",
      "topics": [
        "linr"
      ]
    },
    {
      "page": "listPcaMethods",
      "title": "List PCA methods",
      "topics": [
        "listPcaMethods"
      ]
    },
    {
      "page": "llsImpute",
      "title": "LLSimpute algorithm",
      "topics": [
        "llsImpute"
      ]
    },
    {
      "page": "loadings-ANY-method",
      "title": "Crude way to unmask the function with the same name from 'stats'",
      "topics": [
        "loadings",
        "loadings,ANY-method"
      ]
    },
    {
      "page": "loadings-pcaRes-method",
      "title": "Get loadings from a pcaRes object",
      "topics": [
        "loadings,pcaRes-method"
      ]
    },
    {
      "page": "loadings.pcaRes",
      "title": "Get loadings from a pcaRes object",
      "topics": [
        "loadings.pcaRes"
      ]
    },
    {
      "page": "metaboliteData",
      "title": "A incomplete metabolite data set from an Arabidopsis coldstress experiment",
      "topics": [
        "metaboliteData"
      ]
    },
    {
      "page": "metaboliteDataComplete",
      "title": "A complete metabolite data set from an Arabidopsis coldstress experiment",
      "topics": [
        "metaboliteDataComplete"
      ]
    },
    {
      "page": "method-pcaRes-method",
      "title": "Get the used PCA method",
      "topics": [
        "method",
        "method,pcaRes-method"
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    {
      "page": "nipalsPca",
      "title": "NIPALS PCA",
      "topics": [
        "nipalsPca"
      ]
    },
    {
      "page": "nlpca",
      "title": "Non-linear PCA",
      "topics": [
        "nlpca"
      ]
    },
    {
      "page": "nmissing-pcaRes-method",
      "title": "Missing values",
      "topics": [
        "nmissing",
        "nmissing,nniRes-method",
        "nmissing,pcaRes-method"
      ]
    },
    {
      "page": "nni",
      "title": "Nearest neighbour imputation",
      "topics": [
        "nni"
      ]
    },
    {
      "page": "nniRes",
      "title": "Class for representing a nearest neighbour imputation result",
      "topics": [
        "nniRes",
        "nniRes-class"
      ]
    },
    {
      "page": "nObs-pcaRes-method",
      "title": "Get the number of observations used to build the PCA model.",
      "topics": [
        "nObs",
        "nObs,pcaRes-method"
      ]
    },
    {
      "page": "nP-pcaRes-method",
      "title": "Get number of PCs",
      "topics": [
        "nP",
        "nP,pcaRes-method"
      ]
    },
    {
      "page": "nPcs-pcaRes-method",
      "title": "Get number of PCs.",
      "topics": [
        "nPcs",
        "nPcs,pcaRes-method"
      ]
    },
    {
      "page": "nVar-pcaRes-method",
      "title": "Get the number of variables used to build the PCA model.",
      "topics": [
        "nVar",
        "nVar,pcaRes-method"
      ]
    },
    {
      "page": "optiAlgCgd",
      "title": "Conjugate gradient optimization",
      "topics": [
        "optiAlgCgd"
      ]
    },
    {
      "page": "orth",
      "title": "Calculate an orthonormal basis",
      "topics": [
        "orth"
      ]
    },
    {
      "page": "pca",
      "title": "Perform principal component analysis",
      "topics": [
        "pca"
      ]
    },
    {
      "page": "pcaMethods",
      "title": "pcaMethods",
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