{
  "_id": "6a101aebacfb0bcc41c86c64",
  "Package": "BioNAR",
  "Title": "Biological Network Analysis in R",
  "Version": "1.15.0",
  "Authors@R": "c(\nperson(\"Colin\", \"Mclean\",\nemail=\"Colin.D.Mclean@ed.ac.uk\", role=\"aut\"),\nperson(\"Anatoly\", \"Sorokin\",\nemail=\"lptolik@gmail.com\", role=c(\"aut\", \"cre\")),\nperson(\"Oksana\", \"Sorokina\",\nemail=\"oksana.sorokina@ed.ac.uk\", role=\"aut\"),\nperson(\"J. Douglas\", \"Armstrong\",\nemail=\"Douglas.Armstrong@ed.ac.uk\", role = c(\"aut\",\"fnd\")),\nperson(\"T. Ian\", \"Simpson\",\nemail=\"Ian.Simpson@ed.ac.uk\", role = c(\"ctb\",\"fnd\")) )",
  "Description": "the R package BioNAR, developed to step by step analysis\nof PPI network. The aim is to quantify and rank each protein’s\nsimultaneous impact into multiple complexes based on network\ntopology and clustering. Package also enables estimating of\nco-occurrence of diseases across the network and specific\nclusters pointing towards shared/common mechanisms.",
  "License": "Artistic-2.0",
  "Encoding": "UTF-8",
  "Roxygen": "list(markdown = TRUE)",
  "RoxygenNote": "7.3.2",
  "RdMacros": "Rdpack",
  "Config/testthat/edition": "3",
  "VignetteBuilder": "knitr",
  "BugReports": "https://github.com/lptolik/BioNAR/issues/",
  "biocViews": "Software, GraphAndNetwork, Network",
  "LazyData": "true",
  "Config/pak/sysreqs": "cmake libglpk-dev make libicu-dev libpng-dev\nlibuv1-dev libxml2-dev libssl-dev zlib1g-dev",
  "Repository": "https://bioc.r-universe.dev",
  "Date/Publication": "2026-04-29 11:47:06 UTC",
  "RemoteUrl": "https://github.com/bioc/BioNAR",
  "RemoteRef": "HEAD",
  "RemoteSha": "5acd4d1e61c1fd9129eca169c68fdd6097b60a72",
  "NeedsCompilation": "no",
  "Packaged": {
    "Date": "2026-05-14 09:42:58 UTC",
    "User": "root"
  },
  "Author": "Colin Mclean [aut],\nAnatoly Sorokin [aut, cre],\nOksana Sorokina [aut],\nJ. Douglas Armstrong [aut, fnd],\nT. Ian Simpson [ctb, fnd]",
  "Maintainer": "Anatoly Sorokin <lptolik@gmail.com>",
  "MD5sum": "623554a4d5be2d43aa4c96abb75bf19d",
  "_user": "bioc",
  "_type": "src",
  "_file": "BioNAR_1.15.0.tar.gz",
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  "_filesize": 2825770,
  "_sha256": "4ab01523650cb519b4e0321f6af095d35c8831502c022b99bb74f95c8808bd91",
  "_created": "2026-05-14T09:42:58.000Z",
  "_published": "2026-05-22T08:59:23.931Z",
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  "_buildurl": "https://github.com/r-universe/bioc/actions/runs/25852923061",
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  "_host": "GitHub-Actions",
  "_upstream": "https://github.com/bioc/BioNAR",
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    "author": "Anatoly Sorokin <lptolik@gmail.com>",
    "committer": "Anatoly Sorokin <lptolik@gmail.com>",
    "message": "Merge commit '2a185b929475492134236fa44eddc37b03434864' into devel\n",
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  "_owner": "bioc",
  "_selfowned": true,
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  "_updates": [
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    },
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  "_tags": [],
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      "version": "1.15.0",
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    "network"
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  "_contributors": [
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  "_userbio": {
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    "type": "organization",
    "name": "Bioconductor",
    "description": "Software for the analysis and comprehension of high-throughput genomic data"
  },
  "_downloads": {
    "count": 369,
    "source": "https://www.bioconductor.org/packages/stats/bioc/BioNAR"
  },
  "_devurl": "https://github.com/lptolik/bionar",
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  "_rbuild": "4.6.0",
  "_assets": [
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    "extra/citation.cff",
    "extra/citation.html",
    "extra/citation.json",
    "extra/citation.txt",
    "extra/contents.json",
    "extra/NEWS.html",
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    "extra/readme.md",
    "manual.pdf"
  ],
  "_homeurl": "https://github.com/lptolik/bionar",
  "_realowner": "bioc",
  "_cranurl": false,
  "_exports": [
    "addEdgeAtts",
    "annotateGeneNames",
    "annotateGoBP",
    "annotateGoCC",
    "annotateGoMF",
    "annotateGOont",
    "annotateInterpro",
    "annotatePresynaptic",
    "annotateSCHanno",
    "annotateTopOntoOVG",
    "annotateVertex",
    "applpMatrixToGraph",
    "buildConsensusMatrix",
    "buildNetwork",
    "calcAllClustering",
    "calcBridgeness",
    "calcCentrality",
    "calcCentralityExternalDistances",
    "calcCentralityInternalDistances",
    "calcClustering",
    "calcDiseasePairs",
    "calcEntropy",
    "calcMembership",
    "calcReclusterMatrix",
    "calcSparsness",
    "clusteringSummary",
    "clusterORA",
    "degreeBinnedGDAs",
    "escapeAnnotation",
    "evalCentralitySignificance",
    "findLCC",
    "fitDegree",
    "fitSigmoid",
    "getAnnotationList",
    "getAnnotationVertexList",
    "getBridgeness",
    "getCentralityMatrix",
    "getClustering",
    "getClusterSubgraphByID",
    "getCommunityGraph",
    "getDiseases",
    "getDType",
    "getDYNAMO",
    "getEntropy",
    "getEntropyRate",
    "getGNP",
    "getGraphCentralityECDF",
    "getPA",
    "getRandomGraphCentrality",
    "getRobustness",
    "gofs",
    "layoutByCluster",
    "layoutByRecluster",
    "makeConsensusMatrix",
    "makeMembership",
    "markBowTie",
    "metlMatrix",
    "normModularity",
    "permute",
    "plotBridgeness",
    "plotEntropy",
    "plotRatio",
    "plotSigmoid",
    "prepareGDA",
    "recluster",
    "removeVertexTerm",
    "runPermDisease",
    "sampleDegBinnedGDA",
    "sampleGraphClust",
    "summaryStats",
    "unescapeAnnotation",
    "zeroNA"
  ],
  "_datasets": [
    {
      "name": "diseasome",
      "title": "Barabasi's Diseasome Network",
      "object": "diseasome",
      "class": [
        "igraph"
      ],
      "fields": [],
      "table": false,
      "tojson": false
    }
  ],
  "_help": [
    {
      "page": "addEdgeAtts",
      "title": "Copy edge attributes from one graph to another",
      "topics": [
        "addEdgeAtts"
      ]
    },
    {
      "page": "annotateGeneNames",
      "title": "Annotate Human Gene Names",
      "topics": [
        "annotateGeneNames"
      ]
    },
    {
      "page": "annotateGoBP",
      "title": "Add GO BP annotation to the graph vertices",
      "topics": [
        "annotateGoBP"
      ]
    },
    {
      "page": "annotateGoCC",
      "title": "Add GO CC annotation to the graph vertices",
      "topics": [
        "annotateGoCC"
      ]
    },
    {
      "page": "annotateGoMF",
      "title": "Add GO MF annotation to the graph vertices",
      "topics": [
        "annotateGoMF"
      ]
    },
    {
      "page": "annotateGOont",
      "title": "Annotate nodes with GO terms",
      "topics": [
        "annotateGOont"
      ]
    },
    {
      "page": "annotateInterpro",
      "title": "Add InterPro Family and Domain annotation to the graph vertices",
      "topics": [
        "annotateInterpro"
      ]
    },
    {
      "page": "annotatePresynaptic",
      "title": "Add presynaptic functional groups",
      "topics": [
        "annotatePresynaptic"
      ]
    },
    {
      "page": "annotateSCHanno",
      "title": "Add SCHanno synaptic functional groups",
      "topics": [
        "annotateSCHanno"
      ]
    },
    {
      "page": "annotateTopOntoOVG",
      "title": "Annotate graph with disease terms",
      "topics": [
        "annotateTopOntoOVG"
      ]
    },
    {
      "page": "annotateVertex",
      "title": "Generic annotation function",
      "topics": [
        "annotateVertex"
      ]
    },
    {
      "page": "applpMatrixToGraph",
      "title": "Add attributes to the vertex.",
      "topics": [
        "applpMatrixToGraph"
      ]
    },
    {
      "page": "BioNAR",
      "title": "BioNAR: Biological Network Analysis in R",
      "topics": [
        "BioNAR-package",
        "BioNAR"
      ]
    },
    {
      "page": "buildNetwork",
      "title": "Build network from data.table",
      "topics": [
        "buildNetwork"
      ]
    },
    {
      "page": "calcAllClustering",
      "title": "Calculate memberships for all clustering algorithms and store them on the graph vertices.",
      "topics": [
        "calcAllClustering"
      ]
    },
    {
      "page": "calcBridgeness",
      "title": "Helper function that uses 'getBridgeness' to calculate graph node bridgeness values for selected algorithm and consensus matrix and save them as a graph attribute 'BRIDGENESS.<alg>' with '<alg>' replaced by the selected algorithm name.",
      "topics": [
        "calcBridgeness"
      ]
    },
    {
      "page": "calcCentrality",
      "title": "Calculate the vertex centrality measures",
      "topics": [
        "calcCentrality"
      ]
    },
    {
      "page": "calcCentralityExternalDistances",
      "title": "Function to calculate a distance matrix between a list of permuted vertex centrality matrices and a unperturbed reference matrix.",
      "topics": [
        "calcCentralityExternalDistances"
      ]
    },
    {
      "page": "calcCentralityInternalDistances",
      "title": "Function calculates a set of distance metrics between each vertex pair given a list of vertex centrality matrices",
      "topics": [
        "calcCentralityInternalDistances"
      ]
    },
    {
      "page": "calcClustering",
      "title": "Calculate community membership for given clustering algorithm and store the results as new vertex attributes in the graph..",
      "topics": [
        "calcClustering"
      ]
    },
    {
      "page": "calcDiseasePairs",
      "title": "Calculate each disease-disease pair overlap given a list of disease terms.",
      "topics": [
        "calcDiseasePairs"
      ]
    },
    {
      "page": "calcEntropy",
      "title": "Calculate the graph entropy for each perturbed vertex, and save the results as new vertex attributes in the graph.",
      "concept": [
        "Entropy Functions"
      ],
      "topics": [
        "calcEntropy"
      ]
    },
    {
      "page": "calcMembership",
      "title": "Calculate cluster memberships for the graph.",
      "topics": [
        "calcMembership"
      ]
    },
    {
      "page": "calcReclusterMatrix",
      "title": "Hierarchical graph clustering",
      "topics": [
        "calcReclusterMatrix"
      ]
    },
    {
      "page": "calcSparsness",
      "title": "Calculate sparsness of the graph.",
      "topics": [
        "calcSparsness"
      ]
    },
    {
      "page": "clusteringSummary",
      "title": "Matrix of cluster characteristics",
      "topics": [
        "clusteringSummary"
      ]
    },
    {
      "page": "clusterORA",
      "title": "Calculate annotation enrichment for clusters in the graph",
      "topics": [
        "clusterORA"
      ]
    },
    {
      "page": "compMembership",
      "title": "Calculate cluster memberships for one of the graph component.",
      "topics": [
        "compMembership"
      ]
    },
    {
      "page": "degreeBinnedGDAs",
      "title": "Prepare mapping for degree-aware annotation shuffling.",
      "topics": [
        "degreeBinnedGDAs"
      ]
    },
    {
      "page": "diseasome",
      "title": "Barabasi's Diseasome Network",
      "topics": [
        "diseasome"
      ]
    },
    {
      "page": "escapeAnnotation",
      "title": "Escapes elements of list in annotation.",
      "topics": [
        "escapeAnnotation"
      ]
    },
    {
      "page": "evalCentralitySignificance",
      "title": "Compare distance distributions of internal and external distances",
      "topics": [
        "evalCentralitySignificance"
      ]
    },
    {
      "page": "findLCC",
      "title": "Find Largest Connected Component of the graph",
      "topics": [
        "findLCC"
      ]
    },
    {
      "page": "FitDegree",
      "title": "Fit Power Law to degree distribution.",
      "topics": [
        "fitDegree"
      ]
    },
    {
      "page": "fitSigmoid",
      "title": "Fit Fold-enrichment distribution to sigmoid function",
      "topics": [
        "fitSigmoid"
      ]
    },
    {
      "page": "flatfile_human_gene2HDO.csv",
      "title": "Human Gene Disease Associations (GDA)",
      "topics": [
        "flatfile_human_gene2HDO.csv"
      ]
    },
    {
      "page": "flatfile.go.BP.csv",
      "title": "Annotation from Gene Ontology Biological Process (GO_BP)",
      "topics": [
        "flatfile.go.BP.csv"
      ]
    },
    {
      "page": "flatfile.go.CC.csv",
      "title": "Annotation from Gene Ontology Cellular Compartment (GO_CC)",
      "topics": [
        "flatfile.go.CC.csv"
      ]
    },
    {
      "page": "flatfile.go.MF.csv",
      "title": "Annotation from Gene Ontology Molecular Function (GO_MF)",
      "topics": [
        "flatfile.go.MF.csv"
      ]
    },
    {
      "page": "getAnnotationList",
      "title": "Extract unique values from annotations.",
      "topics": [
        "getAnnotationList"
      ]
    },
    {
      "page": "getAnnotationVertexList",
      "title": "Return vertex list for each term in annotation attribute",
      "topics": [
        "getAnnotationVertexList"
      ]
    },
    {
      "page": "getBridgeness",
      "title": "Calculate bridginess from consensus matrix",
      "topics": [
        "getBridgeness"
      ]
    },
    {
      "page": "getCentralityMatrix",
      "title": "Calculate centrality measures for graph nodes.",
      "topics": [
        "getCentralityMatrix"
      ]
    },
    {
      "page": "getClustering",
      "title": "Get clustering results for the graph.",
      "topics": [
        "getClustering"
      ]
    },
    {
      "page": "getClusterSubgraphByID",
      "title": "Return induced subgraph for cluster",
      "topics": [
        "getClusterSubgraphByID"
      ]
    },
    {
      "page": "getCommunityGraph",
      "title": "Create new graph with communities as a nodes.",
      "topics": [
        "getCommunityGraph"
      ]
    },
    {
      "page": "getDiseases",
      "title": "Get HDO disease IDs",
      "topics": [
        "getDiseases"
      ]
    },
    {
      "page": "getDType",
      "title": "Get DiseaseTypes",
      "topics": [
        "getDType"
      ]
    },
    {
      "page": "getDYNAMO",
      "title": "Calculate DYNAMO sensitivity matrix.",
      "topics": [
        "getDYNAMO"
      ]
    },
    {
      "page": "getEntropy",
      "title": "Calculates vertex perturbation graph entropy.",
      "concept": [
        "Entropy Functions"
      ],
      "topics": [
        "getEntropy"
      ]
    },
    {
      "page": "getEntropyRate",
      "title": "Calculate the maximum entropy rate and initial entropy rate .",
      "concept": [
        "Entropy Functions"
      ],
      "topics": [
        "getEntropyRate"
      ]
    },
    {
      "page": "getGNP",
      "title": "Generate random graph from reference",
      "topics": [
        "getGNP"
      ]
    },
    {
      "page": "getGraphCentralityECDF",
      "title": "Convert centrality matrix into ECDF",
      "topics": [
        "getGraphCentralityECDF"
      ]
    },
    {
      "page": "getIDs",
      "title": "Utility function to get vertex ids from vertex attributes The function obtain attribute values and check duplicates in it. It fails if any duplicate found.",
      "topics": [
        "getIDs"
      ]
    },
    {
      "page": "getPA",
      "title": "Generate random graph from reference",
      "topics": [
        "getPA"
      ]
    },
    {
      "page": "getRandomGraphCentrality",
      "title": "Centrality measures for random graphs induced by input one",
      "topics": [
        "getRandomGraphCentrality"
      ]
    },
    {
      "page": "getRobustness",
      "title": "Calculate cluster robustness from consensus matrix",
      "concept": [
        "Robustness Functions"
      ],
      "topics": [
        "getRobustness"
      ]
    },
    {
      "page": "gofs",
      "title": "Goodnes of fit KS test",
      "topics": [
        "gofs"
      ]
    },
    {
      "page": "law-class",
      "title": "Result of PawerLaw fit",
      "topics": [
        "law-class"
      ]
    },
    {
      "page": "layoutByCluster",
      "title": "Calculate layout based upon membership",
      "topics": [
        "layoutByCluster"
      ]
    },
    {
      "page": "layoutByRecluster",
      "title": "Calculate two-level layout from recluster matrix",
      "topics": [
        "layoutByRecluster"
      ]
    },
    {
      "page": "makeConsensusMatrix",
      "title": "Function to make random resampling consensus matrix in memory",
      "concept": [
        "Robustness Functions"
      ],
      "topics": [
        "makeConsensusMatrix"
      ]
    },
    {
      "page": "makeMembership",
      "title": "Create membership 'data.frame' from graph for arbitrary annotation",
      "topics": [
        "makeMembership"
      ]
    },
    {
      "page": "markBowTie",
      "title": "Calculates bow-tie decomposition and marks vertices with one of the following in the 'BowTie' attribute:         • SCC - maximal strong connected component;         • IN - vertices not in SCC, but SCC is reachable from them;         • OUT - vertices not in SCC, but reachable from SCC;         • TU - vertices not in all three above, but reachable from IN           and OUT is reachable from them (TUBES);         • IDR - vertices not in SCC, but they are reachable from IN and           OUT is NOT reachable from them (INTENDRILS);         • ODR - vertices not is SCC, but they are NOT reachable from IN           and OUT is reachable from them (OUTTENDRILS);         • OTR - all other vertices.",
      "topics": [
        "markBowTie"
      ]
    },
    {
      "page": "metlMatrix",
      "title": "Convert sparce matrix into triplet 'data.frame'.",
      "topics": [
        "metlMatrix"
      ]
    },
    {
      "page": "normModularity",
      "title": "Calculates the normalised network modularity value.",
      "topics": [
        "normModularity"
      ]
    },
    {
      "page": "permute",
      "title": "Randomly shuffle annotations",
      "topics": [
        "permute"
      ]
    },
    {
      "page": "plotBridgeness",
      "title": "Plot Bridgeness values",
      "topics": [
        "plotBridgeness"
      ]
    },
    {
      "page": "plotEntropy",
      "title": "Plot graph entropy values versus vertex degree for each perturbed vertex value.",
      "concept": [
        "Entropy Functions"
      ],
      "topics": [
        "plotEntropy"
      ]
    },
    {
      "page": "plotRatio",
      "title": "Plot fraction of enriched communities",
      "topics": [
        "plotRatio"
      ]
    },
    {
      "page": "plotSigmoid",
      "title": "Plot results of the sigmoid fit",
      "topics": [
        "plotSigmoid"
      ]
    },
    {
      "page": "PPI_Presynaptic.csv",
      "title": "Table of protein protein interactions for presynaptic compartment",
      "topics": [
        "PPI_Presynaptic.csv"
      ]
    },
    {
      "page": "PPI_Presynaptic.gml",
      "title": "PPI graph for presynaptic compartment",
      "topics": [
        "PPI_Presynaptic.gml"
      ]
    },
    {
      "page": "prepareGDA",
      "title": "Function to return vertex annotation from a graph in the Vertex Annotation form and format it for further analysis.",
      "topics": [
        "prepareGDA"
      ]
    },
    {
      "page": "PresynAn.csv",
      "title": "Presynaptic genes specific functional annotation",
      "topics": [
        "PresynAn.csv"
      ]
    },
    {
      "page": "recluster",
      "title": "Hierarchical graph clustering",
      "topics": [
        "recluster"
      ]
    },
    {
      "page": "removeVertexTerm",
      "title": "Remove vertex property.",
      "topics": [
        "removeVertexTerm"
      ]
    },
    {
      "page": "runPermDisease",
      "title": "Calculate disease-disease pair overlaps on permuted network to estimate its statistical significance",
      "topics": [
        "runPermDisease"
      ]
    },
    {
      "page": "sampleDegBinnedGDA",
      "title": "Function to randomly shuffle vertex annotation terms, whilst preserving the vertex degree originally found with that annotation term..",
      "topics": [
        "sampleDegBinnedGDA"
      ]
    },
    {
      "page": "sampleGraphClust",
      "title": "Perturbe graph and calculate its clustering",
      "topics": [
        "sampleGraphClust"
      ]
    },
    {
      "page": "SCH_flatfile.csv",
      "title": "Schizopherina related synaptic gene functional annotation.",
      "topics": [
        "SCH_flatfile.csv"
      ]
    },
    {
      "page": "summaryStats",
      "title": "Calculate summary statistics from enrichment table",
      "topics": [
        "summaryStats"
      ]
    },
    {
      "page": "unescapeAnnotation",
      "title": "Unescape annotation strings",
      "topics": [
        "unescapeAnnotation"
      ]
    },
    {
      "page": "zeroNA",
      "title": "Auxiliary function to replace NAs with zeros.",
      "topics": [
        "zeroNA"
      ]
    }
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    "survival",
    "sys",
    "tibble",
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    "tinytex",
    "utf8",
    "vctrs",
    "viridis",
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    "WGCNA",
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    "XVector",
    "yaml"
  ],
  "_vignettes": [
    {
      "source": "BioNAR_overview.Rmd",
      "filename": "BioNAR_overview.html",
      "title": "BioNAR: Biological Network Analysis in R",
      "author": "Colin Mclean, Anatoly Sorokin, T. I. Simpson, J. Douglas Armstrong, Oksana Sorokina",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Introduction",
        "Overview of capabilities",
        "Build the network",
        "Build a network from a given data frame",
        "Use a predifined network",
        "Annotate the nodes with node attributes",
        "Gene name",
        "Diseases",
        "Schizopherina related synaptic gene functional annotation.",
        "Presynaptic functional annotation",
        "Functional annotation with Gene Ontology (GO)",
        "Estimate vertex centrality measures",
        "Estimate centrality measures with values added as vertex attributes.",
        "Get vertex centralities as a matrix.",
        "Get the centrality measures for random graph",
        "Power law fit",
        "Get entropy rate",
        "Get modularity. Normalised modularity.",
        "Clustering",
        "Reclustering",
        "Consensus matrix",
        "Cluster robustness",
        "Bridgeness",
        "Bridgeness plot",
        "Disease/annotation pairs",
        "Cluster overrepresentation",
        "Session Info"
      ],
      "created": "2022-10-21 06:01:46",
      "modified": "2025-02-26 01:23:33",
      "commits": 13
    }
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