{
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  "Type": "Package",
  "Title": "Signal-Noise Separation in Random Matrices by using Eigenvalue\nSpectrum Analysis",
  "Version": "1.1",
  "Date": "2016-06-03",
  "Author": "Uwe Menzel",
  "Maintainer": "Uwe Menzel <uwemenzel@gmail.com>",
  "Description": "An algorithm which can be used to determine an objective\nthreshold for signal-noise separation in large random matrices\n(correlation matrices, mutual information matrices, network\nadjacency matrices) is provided. The package makes use of the\nresults of Random Matrix Theory (RMT). The algorithm increments\na suppositional threshold monotonically, thereby recording the\neigenvalue spacing distribution of the matrix. According to\nRMT, that distribution undergoes a characteristic change when\nthe threshold properly separates signal from noise. By using\nthe algorithm, the modular structure of a matrix - or of the\ncorresponding network - can be unraveled.",
  "License": "GPL",
  "LazyLoad": "yes",
  "NeedsCompilation": "no",
  "Packaged": {
    "Date": "2026-05-12 06:07:05 UTC",
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  "Repository": "https://uwemenzel.r-universe.dev",
  "Date/Publication": "2016-06-23 19:57:40 UTC",
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    "kld",
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    "rm.denoise.mat",
    "rm.discard.zeros",
    "rm.distance.plot",
    "rm.ev.density",
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    "rm.exp.distrib",
    "rm.get.distance",
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    "rm.get.sparseness",
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    "rm.show.plots",
    "rm.show.test",
    "rm.spacing.distribution",
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    "rm.sse",
    "rm.sse.plot",
    "rm.trapez.int",
    "rm.unfold.gauss",
    "rm.unfold.spline",
    "wigner.semi.circle",
    "wigner.surmise"
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    {
      "page": "RMThreshold-package",
      "title": "Signal-Noise Separation in Correlation Matrices by using Eigenvalue Spectrum Analysis",
      "topics": [
        "RMThreshold-package",
        "RMThreshold"
      ]
    },
    {
      "page": "add.Gaussian.noise",
      "title": "Add Gaussian noise to a matrix",
      "topics": [
        "add.Gaussian.noise"
      ]
    },
    {
      "page": "create.rand.mat",
      "title": "Create a real-valued, symmetric random matrix",
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        "create.rand.mat"
      ]
    },
    {
      "page": "rm.connections",
      "title": "Create ordered list of largest matrix elements",
      "topics": [
        "rm.connections"
      ]
    },
    {
      "page": "rm.denoise.mat",
      "title": "Remove noise from a random matrix by applying a threshold",
      "topics": [
        "rm.denoise.mat"
      ]
    },
    {
      "page": "rm.discard.zeros",
      "title": "Discard rows and columns from a matrix that exclusively contain zero-valued off-diagonal matrix elements",
      "topics": [
        "rm.discard.zeros"
      ]
    },
    {
      "page": "rm.ev.density",
      "title": "Create a density plot and a histogram of the eigenvalue distribution",
      "topics": [
        "rm.ev.density"
      ]
    },
    {
      "page": "rm.get.threshold",
      "title": "Estimate an objective threshold for signal-noise separation in random matrices",
      "topics": [
        "rm.get.threshold"
      ]
    },
    {
      "page": "rm.matrix.validation",
      "title": "Validate input matrix prior to threshold computation",
      "topics": [
        "rm.matrix.validation"
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    },
    {
      "page": "rm.show.plots",
      "title": "Display a sequence of plots on screen",
      "topics": [
        "rm.show.plots"
      ]
    },
    {
      "page": "rm.spacing.distribution",
      "title": "Plot the empirical distribution of the eigenvalue spacings",
      "topics": [
        "rm.spacing.distribution"
      ]
    },
    {
      "page": "RMThreshold-internal",
      "title": "Internal functions for the RMThreshold package",
      "topics": [
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        "kld",
        "rm.distance.plot",
        "rm.ev.unfold",
        "rm.exp.distrib",
        "rm.get.distance",
        "rm.get.file.extension",
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