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  "Title": "Agglomerative Partitioning Framework for Dimension Reduction",
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  "Description": "A fast and flexible framework for agglomerative\npartitioning. 'partition' uses an approach called\nDirect-Measure-Reduce to create new variables that maintain the\nuser-specified minimum level of information. Each reduced\nvariable is also interpretable: the original variables map to\none and only one variable in the reduced data set. 'partition'\nis flexible, as well: how variables are selected to reduce, how\ninformation loss is measured, and the way data is reduced can\nall be customized.  'partition' is based on the Partition\nframework discussed in Millstein et al. (2020)\n<doi:10.1093/bioinformatics/btz661>.",
  "License": "MIT + file LICENSE",
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        "unnest_reduced"
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      "title": "Calculate the intraclass correlation coefficient",
      "topics": [
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      "title": "Is this object a partition?",
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      "title": "Is this object a partitioner?",
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    },
    {
      "page": "mapping_key",
      "title": "Return partition mapping key",
      "topics": [
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        "mapping_key",
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      "page": "measure_icc",
      "title": "Measure the information loss of reduction using intraclass correlation coefficient",
      "concept": [
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    },
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      "concept": [
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      "page": "part_stdmi",
      "title": "Partitioner: distance, mutual information, scaled means",
      "concept": [
        "partitioners"
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      "topics": [
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      "page": "partition",
      "title": "Agglomerative partitioning",
      "topics": [
        "partition"
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      "page": "partition_scores",
      "title": "Return the reduced data from a partition",
      "topics": [
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        "partition_scores"
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    {
      "page": "permute_df",
      "title": "Permute a data set",
      "topics": [
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    },
    {
      "page": "plot_partitions",
      "title": "Plot partitions",
      "topics": [
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        "plot_information",
        "plot_ncluster",
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      "title": "Plot permutation tests",
      "topics": [
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      "page": "reduce_target",
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      "topics": [
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