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  "Title": "Model Order Selection for Clustering",
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  "Maintainer": "Jessica Gliozzo <jessica.gliozzo@gmail.com>",
  "Description": "Stability based methods for model order selection in\nclustering problems (Valentini, G (2007),\n<doi:10.1093/bioinformatics/btl600>). Using multiple\nperturbations of the data the stability of clustering solutions\nis assessed. Different perturbations may be used: resampling\ntechniques, random projections and noise injection. Stability\nmeasures for the estimate of clustering solutions and\nstatistical tests to assess their significance are provided.",
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  "URL": "https://valentini.di.unimi.it/SW/mosclust/",
  "BugReports": "https://github.com/AnacletoLAB/mosclust/issues",
  "Encoding": "UTF-8",
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  "Date/Publication": "2026-09-10 08:32:25 UTC",
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    "PAM.sim.projection",
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    "plot_hist.similarity",
    "plot_multiple.hist.similarity",
    "plot_pvalues",
    "sFM",
    "sJaccard",
    "sM"
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    {
      "page": "mosclust-package",
      "title": "Model order selection for clustering",
      "topics": [
        "mosclust-package",
        "mosclust"
      ]
    },
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      "page": "Bernstein.compute.pvalues",
      "title": "Function to compute the stability indices and the p-values associated to a set of clusterings according to Bernstein inequality.",
      "topics": [
        "Bernstein.compute.pvalues",
        "Bernstein.ind.compute.pvalues"
      ]
    },
    {
      "page": "Bernstein.p.value",
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      ]
    },
    {
      "page": "Chi.square.compute.pvalues",
      "title": "Function to compute the stability indices and the p-values associated to a set of clusterings according to the chi-square test between multiple proportions.",
      "topics": [
        "Chi.square.compute.pvalues"
      ]
    },
    {
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      "title": "Function to evaluate if a set of similarity distributions significantly differ using the chi square test.",
      "topics": [
        "Compute.Chi.sq"
      ]
    },
    {
      "page": "compute.cumulative.multiple",
      "title": "Function to compute the empirical cumulative distribution function (ECDF) of the similarity measures.",
      "topics": [
        "compute.cumulative.multiple",
        "cumulative.values"
      ]
    },
    {
      "page": "compute.integral",
      "title": "Functions to compute the integral of the ecdf of the similarity values",
      "topics": [
        "compute.integral",
        "compute.integral.from.similarity"
      ]
    },
    {
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    },
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      "title": "Function that computes sets of similarity indices using injection of gaussian noise.",
      "topics": [
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      "title": "Function to compute similarity indices using random projections and hierarchical clustering.",
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        "Hierarchical.sim.projection"
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    },
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      "topics": [
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      ]
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      "title": "Function to compute similarity indices using random projections and PAM clustering.",
      "topics": [
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      "topics": [
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