{
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  "Package": "parafac4microbiome",
  "Title": "Parallel Factor Analysis Modelling of Longitudinal Microbiome\nData",
  "Version": "1.3.2.9000",
  "Authors@R": "c(\nperson(\"Geert Roelof\", \"van der Ploeg\", , \"g.r.ploeg@uva.nl\", role = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0009-0007-5204-3386\")),\nperson(\"Johan\", \"Westerhuis\", , \"j.a.westerhuis@uva.nl\", role = \"ctb\",\ncomment = c(ORCID = \"0000-0002-6747-9779\")),\nperson(\"Anna\", \"Heintz-Buschart\", , \"a.u.s.heintzbuschart@uva.nl\", role = \"ctb\",\ncomment = c(ORCID = \"0000-0002-9780-1933\")),\nperson(\"Age\", \"Smilde\", , \"a.k.smilde@uva.nl\", role = \"ctb\",\ncomment = c(ORCID = \"0000-0002-3052-4644\")),\nperson(\"University of Amsterdam\", role = c(\"cph\", \"fnd\")))",
  "Description": "Creation and selection of PARAllel FACtor Analysis\n(PARAFAC) models of longitudinal microbiome data. You can\nimport your own data with our import functions or use one of\nthe example datasets to create your own PARAFAC models.\nSelection of the optimal number of components can be done using\nassessModelQuality() and assessModelStability(). The selected\nmodel can then be plotted using plotPARAFACmodel(). The\nParallel Factor Analysis method was originally described by\nCaroll and Chang (1970) <doi:10.1007/BF02310791> and Harshman\n(1970)\n<https://www.psychology.uwo.ca/faculty/harshman/wpppfac0.pdf>.",
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  "URL": "https://grvanderploeg.com/parafac4microbiome/,\nhttps://github.com/GRvanderPloeg/parafac4microbiome/",
  "BugReports": "https://github.com/GRvanderPloeg/parafac4microbiome/issues",
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  "Repository": "https://grvanderploeg.r-universe.dev",
  "Date/Publication": "2025-09-24 09:12:10 UTC",
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  "Packaged": {
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  "Author": "Geert Roelof van der Ploeg [aut, cre] (ORCID:\n<https://orcid.org/0009-0007-5204-3386>),\nJohan Westerhuis [ctb] (ORCID: <https://orcid.org/0000-0002-6747-9779>),\nAnna Heintz-Buschart [ctb] (ORCID:\n<https://orcid.org/0000-0002-9780-1933>),\nAge Smilde [ctb] (ORCID: <https://orcid.org/0000-0002-3052-4644>),\nUniversity of Amsterdam [cph, fnd]",
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    "plotModelStability",
    "plotModelTCCs",
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    "processDataCube",
    "reinflateFac",
    "reinflateTensor",
    "reshapeData",
    "sortComponents",
    "transformPARAFACloadings",
    "vect_to_fac"
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      "title": "Fujita2023 longitudinal microbiome data",
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      ],
      "fields": [],
      "table": false,
      "tojson": true
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      "object": "Shao2019",
      "class": [
        "list"
      ],
      "fields": [],
      "table": false,
      "tojson": true
    },
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      "name": "vanderPloeg2024",
      "title": "vanderPloeg2024 longitudinal multi-omics dataset",
      "object": "vanderPloeg2024",
      "class": [
        "list"
      ],
      "fields": [],
      "table": false,
      "tojson": true
    }
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    {
      "page": "assessModelQuality",
      "title": "Create randomly initialized models to determine the correct number of components by assessing model quality metrics.",
      "topics": [
        "assessModelQuality"
      ]
    },
    {
      "page": "assessModelStability",
      "title": "Bootstrapping procedure to determine PARAFAC model stability for a given number of components.",
      "topics": [
        "assessModelStability"
      ]
    },
    {
      "page": "calculateFMS",
      "title": "Calculate Factor Match Score for all initialized models.",
      "topics": [
        "calculateFMS"
      ]
    },
    {
      "page": "calculateSparsity",
      "title": "Calculate sparsity across the feature mode of a multi-way array.",
      "topics": [
        "calculateSparsity"
      ]
    },
    {
      "page": "calculateVarExp",
      "title": "Calculate the variation explained by a PARAFAC model.",
      "topics": [
        "calculateVarExp"
      ]
    },
    {
      "page": "calcVarExpPerComponent",
      "title": "Calculate the variance explained of a PARAFAC model, per component",
      "topics": [
        "calcVarExpPerComponent"
      ]
    },
    {
      "page": "corcondia",
      "title": "Core Consistency Diagnostic (CORCONDIA) calculation",
      "topics": [
        "corcondia"
      ]
    },
    {
      "page": "fac_to_vect",
      "title": "Vectorize Fac object",
      "topics": [
        "fac_to_vect"
      ]
    },
    {
      "page": "flipLoadings",
      "title": "Sign flip the loadings of many randomly initialized models to make consistent overview plots.",
      "topics": [
        "flipLoadings"
      ]
    },
    {
      "page": "Fujita2023",
      "title": "Fujita2023 longitudinal microbiome data",
      "topics": [
        "Fujita2023"
      ]
    },
    {
      "page": "importPhyloseq",
      "title": "Import Phyloseq object for PARAFAC modelling",
      "topics": [
        "importPhyloseq"
      ]
    },
    {
      "page": "importTreeSummarizedExperiment",
      "title": "Import TreeSummarizedExperiment object for PARAFAC modelling",
      "topics": [
        "importTreeSummarizedExperiment"
      ]
    },
    {
      "page": "initializePARAFAC",
      "title": "Initialize PARAFAC algorithm input vectors",
      "topics": [
        "initializePARAFAC"
      ]
    },
    {
      "page": "multiwayCenter",
      "title": "Center a multi-way array",
      "topics": [
        "multiwayCenter"
      ]
    },
    {
      "page": "multiwayCLR",
      "title": "Perform a centered log-ratio transform over a multi-way array",
      "topics": [
        "multiwayCLR"
      ]
    },
    {
      "page": "multiwayScale",
      "title": "Scale a multi-way array",
      "topics": [
        "multiwayScale"
      ]
    },
    {
      "page": "parafac",
      "title": "Parallel Factor Analysis",
      "topics": [
        "parafac"
      ]
    },
    {
      "page": "parafac_core_als",
      "title": "Internal PARAFAC alternating least-squares (ALS) core algorithm",
      "topics": [
        "parafac_core_als"
      ]
    },
    {
      "page": "parafac_fun",
      "title": "PARAFAC loss function calculation",
      "topics": [
        "parafac_fun"
      ]
    },
    {
      "page": "plotModelMetric",
      "title": "Plot diagnostics of many initialized PARAFAC models.",
      "topics": [
        "plotModelMetric"
      ]
    },
    {
      "page": "plotModelStability",
      "title": "Plot a summary of the loadings of many initialized parafac models.",
      "topics": [
        "plotModelStability"
      ]
    },
    {
      "page": "plotModelTCCs",
      "title": "Plots Tucker Congruence Coefficients of randomly initialized models.",
      "topics": [
        "plotModelTCCs"
      ]
    },
    {
      "page": "plotPARAFACmodel",
      "title": "Plot a PARAFAC model",
      "topics": [
        "plotPARAFACmodel"
      ]
    },
    {
      "page": "processDataCube",
      "title": "Process a multi-way array of count data.",
      "topics": [
        "processDataCube"
      ]
    },
    {
      "page": "reinflateFac",
      "title": "Calculate Xhat from a model Fac object",
      "topics": [
        "reinflateFac"
      ]
    },
    {
      "page": "reinflateTensor",
      "title": "Create a tensor out of a set of matrices similar to a component model.",
      "topics": [
        "reinflateTensor"
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    },
    {
      "page": "reshapeData",
      "title": "Reorganize longitudinal microbiome into a data cube ready for PARAFAC modelling.",
      "topics": [
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    {
      "page": "Shao2019",
      "title": "Shao2019 longitudinal microbiome data",
      "topics": [
        "Shao2019"
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    },
    {
      "page": "sortComponents",
      "title": "Sort PARAFAC components based on variance explained per component.",
      "topics": [
        "sortComponents"
      ]
    },
    {
      "page": "transformPARAFACloadings",
      "title": "Transform PARAFAC loadings to an orthonormal basis. Note: this function only works for 3-way PARAFAC models.",
      "topics": [
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      "title": "vanderPloeg2024 longitudinal multi-omics dataset",
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      "title": "Convert vectorized output of PARAFAC to a Fac list object with all loadings per mode.",
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