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  "Package": "cclustr",
  "Title": "Consensus Clustering Methods for Multiple Imputed Data",
  "Version": "0.1.1",
  "Authors@R": "c(\nperson(\"Anhuar\", \"Duran Mendoza\", email = \"asteyduran15@gmail.com\", role = \"aut\"),\nperson(\"Andres\", \"Montenegro Lemus\", email = \"andresfemole@gmail.com\", role = c(\"aut\", \"cre\")),\nperson(\"Mario\", \"Pacheco Lopez\", email = \"mariopachecolopez@gmail.com\", role = \"aut\")\n)",
  "Description": "Provides tools for performing consensus clustering on\nmultiple imputed datasets. The package supports a range of\nclustering algorithms across imputations, including\nhierarchical methods (e.g., Ward, single, complete, average)\nand partition-based approaches such as k-means, k-medoids\n(PAM), fuzzy clustering, model-based clustering ('mclust'), and\nmethods for mixed or categorical data (k-modes and\nk-prototypes). A co-assignment matrix is constructed to\nquantify agreement between partitions, and consensus solutions\nare derived via hierarchical clustering applied to the\nresulting dissimilarity matrix. Additional functions are\nprovided for validation and visualization of clustering\nresults, facilitating robust analysis in the presence of\nmissing data. Consensus clustering framework is based on Monti\net al. (2003) <doi:10.1023/A:1023949509487>, rank aggregation\nmethods follow Pihur et al. (2007)\n<doi:10.1093/bioinformatics/btm158>, and the PAC (Proportion of\nAmbiguous Clustering) metric is based on Senbabaoglu et al.\n(2014) <doi:10.1038/srep06207>.",
  "License": "MIT + file LICENSE",
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  "Author": "Anhuar Duran Mendoza [aut], Andres Montenegro Lemus [aut, cre],\nMario Pacheco Lopez [aut]",
  "Maintainer": "Andres Montenegro Lemus <andresfemole@gmail.com>",
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    {
      "page": "as_mild_list",
      "title": "Standardize multiple imputation outputs into a unified list",
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      "title": "Select the optimal number of clusters from a validation table",
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      "title": "Perform clustering on multiple imputed datasets",
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      "title": "Build a consensus partition from multiple imputation clustering results",
      "topics": [
        "consensus_clustering"
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      "page": "plot_consensus_dendrogram",
      "title": "Plot a consensus clustering dendrogram",
      "topics": [
        "plot_consensus_dendrogram"
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    },
    {
      "page": "plot_consensus_matrix",
      "title": "Plot the consensus co-assignment matrix",
      "topics": [
        "plot_consensus_matrix"
      ]
    },
    {
      "page": "plot_validation_metrics",
      "title": "Plot validation metrics across candidate numbers of clusters",
      "topics": [
        "plot_validation_metrics"
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    },
    {
      "page": "run_mi_clustering",
      "title": "Run the full multiple-imputation clustering pipeline",
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    },
    {
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      "title": "Validate consensus clustering results across multiple imputed datasets",
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      "title": "Consensus Clustering with Multiple Imputed Data using cclustr",
      "author": "Anhuar Duran Mendoza, Andres Montenegro Lemus, Mario Pacheco Lopez",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Introduction",
        "Dataset: Pima Indians Diabetes",
        "Variables",
        "Loading the data",
        "Step 1: Multiple Imputation with mice",
        "Step 2: Clustering Each Imputed Dataset",
        "Step 3: Consensus Clustering",
        "The consensus_method parameter",
        "The cluster_method parameter",
        "Step 4: Validation and Optimal k Selection",
        "Metric Reference",
        "Plotting Validation Metrics",
        "Visualizing the Consensus Solution",
        "Co-assignment Matrix",
        "Consensus Dendrogram",
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