{
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  "Package": "CJIVE",
  "Type": "Package",
  "Title": "Canonical Joint and Individual Variation Explained (CJIVE)",
  "Version": "0.1.0",
  "Authors@R": "c(person(\"Raphiel\", \"Murden\", email = \"rmurden@emory.edu\",role = c(\"aut\", \"cre\"),comment = c(ORCID = \"0000-0002-6396-9105\")),person(\"Benjamin\", \"Risk\", email = \"brisk@emory.edu\",role = \"aut\"))",
  "Maintainer": "Raphiel Murden <rmurden@emory.edu>",
  "Description": "Joint and Individual Variation Explained (JIVE) is a\nmethod for decomposing multiple datasets obtained on the same\nsubjects into shared structure, structure unique to each\ndataset, and noise. The two most common implementations are\nR.JIVE, an iterative approach, and AJIVE, which uses principal\nangle analysis. JIVE estimates subspaces but interpreting these\nsubspaces can be challenging with AJIVE or R.JIVE. We expand\nupon insights into AJIVE as a canonical correlation analysis\n(CCA) of principal component scores. This reformulation, which\nwe call CJIVE, 1) provides an ordering of joint components by\nthe degree of correlation between corresponding canonical\nvariables; 2) uses a computationally efficient permutation test\nfor the number of joint components, which provides a p-value\nfor each component; and 3) can be used to predict subject\nscores for out-of-sample observations. Please cite the\nfollowing article when utilizing this package: Murden, R.,\nZhang, Z., Guo, Y., & Risk, B. (2022)\n<doi:10.3389/fnins.2022.969510>.",
  "License": "MIT + file LICENSE",
  "Encoding": "UTF-8",
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  "NeedsCompilation": "no",
  "Packaged": {
    "Date": "2026-06-07 09:00:05 UTC",
    "User": "root"
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  "Author": "Raphiel Murden [aut, cre]\n(<https://orcid.org/0000-0002-6396-9105>), Benjamin Risk [aut]",
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  "Repository": "https://rmurden.r-universe.dev",
  "Date/Publication": "2023-01-20 09:10:13 UTC",
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  "_published": "2026-06-07T09:03:33.078Z",
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    "author": "Raphiel Murden <rmurden@emory.edu>",
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    "description": "Assistant Professor of Biostatistics, Rollins School of Public Health, Emory University. Main research areas: functional data analysis and data integration",
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    "description": "Assistant Professor of Biostatistics, Rollins School of Public Health, Emory University. Main research areas: functional data analysis and data integration"
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    "extra/citation.html",
    "extra/citation.json",
    "extra/citation.txt",
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    "cc.jive",
    "cc.jive.pred",
    "chord.norm.diff",
    "ConvSims_gg",
    "create.graph.long",
    "GenerateToyData",
    "GetSimResults_Dir",
    "gg.corr.plot",
    "gg.load.norm.plot",
    "gg.norm.plot",
    "gg.rank.plot",
    "gg.score.norm.plot",
    "MatVar",
    "MatVar2",
    "Melt.Sim.Cors",
    "perm.jntrank",
    "scale_loadings",
    "show.image.2",
    "sjive",
    "vec2net.l",
    "vec2net.u"
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  "_help": [
    {
      "page": "AdjSigVarExp",
      "title": "Adjust Signal Variation Explained",
      "topics": [
        "AdjSigVarExp"
      ]
    },
    {
      "page": "cc.jive",
      "title": "Canonical (Correlation) JIVE",
      "topics": [
        "cc.jive"
      ]
    },
    {
      "page": "cc.jive.pred",
      "title": "CJIVE joint subject score prediction",
      "topics": [
        "cc.jive.pred"
      ]
    },
    {
      "page": "chord.norm.diff",
      "title": "Chordal norm between column-subspaces of two matrices",
      "topics": [
        "chord.norm.diff"
      ]
    },
    {
      "page": "ConvSims_gg",
      "title": "Convert simulation study results",
      "topics": [
        "ConvSims_gg"
      ]
    },
    {
      "page": "create.graph.long",
      "title": "Function for plotting networks with ggplot",
      "topics": [
        "create.graph.long"
      ]
    },
    {
      "page": "GenerateToyData",
      "title": "Generate 'Toy' Data",
      "topics": [
        "GenerateToyData"
      ]
    },
    {
      "page": "GetSimResults_Dir",
      "title": "Retrieve simulation results",
      "topics": [
        "GetSimResults_Dir"
      ]
    },
    {
      "page": "gg.corr.plot",
      "title": "Function for plotting Pearson correlations between predicted and true subject scores within the simulation study described in CJIVE manuscript",
      "topics": [
        "gg.corr.plot"
      ]
    },
    {
      "page": "gg.load.norm.plot",
      "title": "Function for plotting chordal norms between estimated and true variable loading subspaces within the simulation study described in CJIVE manuscript",
      "topics": [
        "gg.load.norm.plot"
      ]
    },
    {
      "page": "gg.norm.plot",
      "title": "Function for plotting chordal norms between estimated and true subspaces within the simulation study described in CJIVE manuscript",
      "topics": [
        "gg.norm.plot"
      ]
    },
    {
      "page": "gg.rank.plot",
      "title": "Function for plotting selected joint ranks",
      "topics": [
        "gg.rank.plot"
      ]
    },
    {
      "page": "gg.score.norm.plot",
      "title": "Function for plotting chordal norms between estimated and true subject score subspaces within the simulation study described in CJIVE manuscript",
      "topics": [
        "gg.score.norm.plot"
      ]
    },
    {
      "page": "MatVar",
      "title": "Matrix variation (i.e. Frobenius norm)",
      "topics": [
        "MatVar"
      ]
    },
    {
      "page": "MatVar2",
      "title": "Alternative calculation - Matrix variation (i.e. Frobenius norm)",
      "topics": [
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      "title": "Permutation Test for Joint Rank in CJIVE",
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      "title": "Simple JIVE",
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