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  "Title": "Consensus Clustering",
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  "Authors@R": "c(person(\"Behnam\", \"Yousefi\", role = c(\"aut\", \"cre\",\"cph\"),\nemail = \"yousefi.bme@gmail.com\"))",
  "Description": "Clustering, or cluster analysis, is a widely used\ntechnique in bioinformatics to identify groups of similar\nbiological data points. Consensus clustering is an extension to\nclustering algorithms that aims to construct a robust result\nfrom those clustering features that are invariant under\ndifferent sources of variation. For the reference, please cite\nthe following paper: Yousefi, Melograna, et. al., (2023)\n<doi:10.3389/fmicb.2023.1170391>.",
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    "Date": "2026-05-18 06:05:39 UTC",
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  "Author": "Behnam Yousefi [aut, cre, cph]",
  "Maintainer": "Behnam Yousefi <yousefi.bme@gmail.com>",
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  "Repository": "https://behnam-yousefi.r-universe.dev",
  "Date/Publication": "2024-07-31 02:26:34 UTC",
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    "adj_mat",
    "cc_cluster_count",
    "cluster_relabel",
    "coCluster_matrix",
    "connectivity_matrix",
    "consensus_matrix",
    "consensus_matrix_data_prtrb",
    "consensus_matrix_multiview",
    "gaussian_clusters",
    "gaussian_clusters_with_param",
    "gaussian_mixture_clusters",
    "generate_data_prtrb",
    "generate_gaussian_data",
    "generate_method_prtrb",
    "generate_multiview",
    "hir_clust_from_adj_mat",
    "indicator_matrix",
    "label_similarity",
    "Logit",
    "majority_voting",
    "multi_cluster_gen",
    "multi_kmeans_gen",
    "multi_pam_gen",
    "multiview_cluster_gen",
    "multiview_clusters",
    "multiview_kmeans_gen",
    "multiview_pam_gen",
    "pam_clust_from_adj_mat",
    "spect_clust_from_adj_mat"
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    {
      "page": "adj_conv",
      "title": "Convert adjacency function to the affinity matrix",
      "topics": [
        "adj_conv"
      ]
    },
    {
      "page": "adj_mat",
      "title": "Covert data matrix to adjacency matrix",
      "topics": [
        "adj_mat"
      ]
    },
    {
      "page": "cc_cluster_count",
      "title": "Count the number of clusters based on stability score.",
      "topics": [
        "cc_cluster_count"
      ]
    },
    {
      "page": "cluster_relabel",
      "title": "Relabeling clusters based on cluster similarities",
      "topics": [
        "cluster_relabel"
      ]
    },
    {
      "page": "coCluster_matrix",
      "title": "Calculate the Co-cluster matrix for a given set of clustering results.",
      "topics": [
        "coCluster_matrix"
      ]
    },
    {
      "page": "connectivity_matrix",
      "title": "Build connectivity matrix",
      "topics": [
        "connectivity_matrix"
      ]
    },
    {
      "page": "consensus_matrix",
      "title": "Calculate consensus matrix for data perturbation consensus clustering",
      "topics": [
        "consensus_matrix"
      ]
    },
    {
      "page": "consensus_matrix_data_prtrb",
      "title": "Calculate consensus matrix for data perturbation consensus clustering",
      "topics": [
        "consensus_matrix_data_prtrb"
      ]
    },
    {
      "page": "consensus_matrix_multiview",
      "title": "Calculate consensus matrix for multi-data consensus clustering",
      "topics": [
        "consensus_matrix_multiview"
      ]
    },
    {
      "page": "gaussian_clusters",
      "title": "Generate clusters of data points from Gaussian distribution with randomly generated parameters",
      "topics": [
        "gaussian_clusters"
      ]
    },
    {
      "page": "gaussian_clusters_with_param",
      "title": "Generate clusters of data points from Gaussian distribution with given parameters",
      "topics": [
        "gaussian_clusters_with_param"
      ]
    },
    {
      "page": "gaussian_mixture_clusters",
      "title": "Generate clusters of data points from Gaussian-mixture-model distributions with randomly generated parameters",
      "topics": [
        "gaussian_mixture_clusters"
      ]
    },
    {
      "page": "generate_data_prtrb",
      "title": "Generation mechanism for data perturbation consensus clustering",
      "topics": [
        "generate_data_prtrb"
      ]
    },
    {
      "page": "generate_gaussian_data",
      "title": "Generate a set of data points from Gaussian distribution",
      "topics": [
        "generate_gaussian_data"
      ]
    },
    {
      "page": "generate_method_prtrb",
      "title": "Multiple method generation",
      "topics": [
        "generate_method_prtrb"
      ]
    },
    {
      "page": "generate_multiview",
      "title": "Multiview generation",
      "topics": [
        "generate_multiview"
      ]
    },
    {
      "page": "hir_clust_from_adj_mat",
      "title": "Hierarchical clustering from adjacency matrix",
      "topics": [
        "hir_clust_from_adj_mat"
      ]
    },
    {
      "page": "indicator_matrix",
      "title": "Build indicator matrix",
      "topics": [
        "indicator_matrix"
      ]
    },
    {
      "page": "label_similarity",
      "title": "Similarity between different clusters",
      "topics": [
        "label_similarity"
      ]
    },
    {
      "page": "Logit",
      "title": "Logit function",
      "topics": [
        "Logit"
      ]
    },
    {
      "page": "majority_voting",
      "title": "Consensus mechanism based on majority voting",
      "topics": [
        "majority_voting"
      ]
    },
    {
      "page": "multi_cluster_gen",
      "title": "Multiple cluster generation",
      "topics": [
        "multi_cluster_gen"
      ]
    },
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      "title": "Multiple K-means generation",
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      ]
    },
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        "multi_pam_gen"
      ]
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        "multiview_cluster_gen"
      ]
    },
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      "topics": [
        "multiview_clusters"
      ]
    },
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    },
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      ]
    },
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      "topics": [
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      ]
    },
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    }
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