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  "Title": "Group Factor Analysis",
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  "Date": "2023-10-21",
  "Authors@R": "c(person(\"Eemeli\", \"Leppäaho\", role = c(\"aut\",\"cre\"),\nemail = \"eemeli.leppaaho@gmail.com\"),\nperson(\"Seppo\", \"Virtanen\", role = c(\"aut\"),\nemail = \"s.virtanen@warwick.ac.uk\"),\nperson(\"Muhammad\", \"Ammad-ud-din\", role = \"ctb\",\nemail = \"muhammad.ammad-ud-din@aalto.fi\"),\nperson(\"Suleiman A\", \"Khan\", role = \"ctb\",\nemail = \"suleiman.khan@helsinki.fi\"),\nperson(\"Tommi\", \"Suvitaival\", role = \"ctb\",\nemail = \"tsvv@steno.dk\"),\nperson(\"Inka\", \"Saarinen\", role = \"ctb\",\nemail = \"inka.saarinen@aalto.fi\"),\nperson(\"Samuel\", \"Kaski\", role = \"ctb\",\nemail = \"samuel.kaski@aalto.fi\"))",
  "Author": "Eemeli Leppäaho [aut, cre], Seppo Virtanen [aut], Muhammad\nAmmad-ud-din [ctb], Suleiman A Khan [ctb], Tommi Suvitaival\n[ctb], Inka Saarinen [ctb], Samuel Kaski [ctb]",
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  "Description": "Factor analysis implementation for multiple data sources,\ni.e., for groups of variables. The whole data analysis pipeline\nis provided, including functions and recommendations for data\nnormalization and model definition, as well as missing value\nprediction and model visualization. The model group factor\nanalysis (GFA) is inferred with Gibbs sampling, and it has been\npresented originally by Virtanen et al. (2012), and extended in\nKlami et al. (2015) <DOI:10.1109/TNNLS.2014.2376974> and Bunte\net al. (2016) <DOI:10.1093/bioinformatics/btw207>; for details,\nsee the citation info.",
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  "Date/Publication": "2023-10-21 11:30:27 UTC",
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    "undoNormalizeData",
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      "page": "GFA-package",
      "title": "Group factor analysis.",
      "topics": [
        "GFA-package",
        "GFA"
      ]
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      "title": "A function for generating the default priors of GFA model",
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        "getDefaultOpts"
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    },
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      "page": "gfa",
      "title": "Gibbs sampling for group factor analysis",
      "topics": [
        "gfa"
      ]
    },
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    },
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      "title": "Normalize data to be used by GFA",
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      ]
    },
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      "title": "Full data reconstruction based on posterior samples",
      "topics": [
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      ]
    },
    {
      "page": "robustComponents",
      "title": "Robust GFA components",
      "topics": [
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      ]
    },
    {
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      "title": "Sequential prediction of new samples from observed data views to unobserved",
      "topics": [
        "sequentialGfaPrediction"
      ]
    },
    {
      "page": "undoNormalizeData",
      "title": "A function for returning predictions into the original data space",
      "topics": [
        "undoNormalizeData"
      ]
    },
    {
      "page": "visualizeComponents",
      "title": "Visualize GFA components",
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