{
  "_id": "6a1ee876b401979e73411ac1",
  "Package": "brainGraph",
  "Type": "Package",
  "Version": "3.0.3",
  "Date": "2024-02-20",
  "Title": "Graph Theory Analysis of Brain MRI Data",
  "Authors@R": "person(given=\"Christopher G.\", family=\"Watson\",\nemail=\"cgwatson@bu.edu\", role=c(\"aut\", \"cre\"),\ncomment=c(ORCID=\"0000-0002-7082-7631\"))",
  "Description": "A set of tools for performing graph theory analysis of\nbrain MRI data. It works with data from a Freesurfer analysis\n(cortical thickness, volumes, local gyrification index, surface\narea), diffusion tensor tractography data (e.g., from FSL) and\nresting-state fMRI data (e.g., from DPABI). It contains a\ngraphical user interface for graph visualization and data\nexploration, along with several functions for generating useful\nfigures.",
  "URL": "https://github.com/cwatson/brainGraph",
  "BugReports": "https://groups.google.com/forum/?hl=en#!forum/brainGraph-help",
  "LazyData": "true",
  "License": "GPL-3",
  "RoxygenNote": "6.1.1",
  "Collate": "'glm_stats.R' 'brainGraph_GLM.R' 'glm_methods.R' 'NBS.R'\n'analysis_random_graphs.R' 'atlas.R' 'auc.R' 'boot_global.R'\n'brainGraph_mediate.R' 'centr_lev.R' 'communicability.R'\n'contract_brainGraph.R' 'corr_matrix.R' 'count_edges.R'\n'create_graphs.R' 'create_mats.R' 'data.R' 'data_tables.R'\n'distances.R' 'edge_asymmetry.R' 'get_resid.R' 'glm_design.R'\n'glm_fit.R' 'glm_randomise.R' 'graph_efficiency.R' 'hubs.R'\n'import.R' 'individ_contrib.R' 'list.R' 'method_helpers.R'\n'mtpc.R' 'methods.R' 'permute_group.R' 'plot_brainGraph.R'\n'plot_brainGraph_gui.R' 'plot_brainGraph_multi.R'\n'plot_global.R' 'plot_group_means.R' 'plot_rich_norm.R'\n'plot_vertex_measures.R' 'random_graphs.R' 'rich_club.R'\n'robustness.R' 's_core.R' 'set_brainGraph_attributes.R'\n'small_world.R' 'spatial_dist.R' 'update_brainGraph_gui.R'\n'utils.R' 'utils_matrix.R' 'vertex_roles.R' 'vulnerability.R'\n'write_brainnet.R' 'zzz.R'",
  "Config/pak/sysreqs": "libglpk-dev libxml2-dev",
  "Repository": "https://cwatson.r-universe.dev",
  "Date/Publication": "2024-02-20 16:44:48 UTC",
  "RemoteUrl": "https://github.com/cwatson/braingraph",
  "RemoteRef": "HEAD",
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  "NeedsCompilation": "no",
  "Packaged": {
    "Date": "2026-05-19 10:37:25 UTC",
    "User": "root"
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  "Author": "Christopher G. Watson [aut, cre] (ORCID:\n<https://orcid.org/0000-0002-7082-7631>)",
  "Maintainer": "Christopher G. Watson <cgwatson@bu.edu>",
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  "_created": "2026-05-19T10:37:25.000Z",
  "_published": "2026-06-02T14:28:06.015Z",
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    "author": "Chris Watson <cgwatson@bu.edu>",
    "committer": "Chris Watson <cgwatson@bu.edu>",
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    "login": "cwatson",
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  "_usedby": 3,
  "_updates": [],
  "_tags": [],
  "_topics": [
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    "brain-imaging",
    "complex-networks",
    "connectome",
    "connectomics",
    "fmri",
    "graph-theory",
    "mri",
    "network-analysis",
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    "statistics",
    "tractography"
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    "extra/citation.html",
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    {
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    {
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    {
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      "date": "2019-11-06"
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      "date": "2019-11-07"
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      "date": "2020-09-29"
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      "date": "2025-10-16"
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      "date": "2026-04-30"
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    "analysis_random_graphs",
    "aop",
    "apply_thresholds",
    "as_atlas",
    "as_brainGraphList",
    "bg_to_mediate",
    "brainGraph_boot",
    "brainGraph_GLM",
    "brainGraph_GLM_design",
    "brainGraph_mediate",
    "brainGraph_permute",
    "centr_betw_comm",
    "centr_lev",
    "check_sID",
    "coeff_determ",
    "coeff_table",
    "coeff_var",
    "colMax",
    "colMaxAbs",
    "colMin",
    "communicability",
    "contract_brainGraph",
    "cor.diff.test",
    "corr.matrix",
    "count_homologous",
    "count_inter",
    "covratio.bg_GLM",
    "create_atlas",
    "create_mats",
    "dffits.bg_GLM",
    "diag_sq",
    "edge_asymmetry",
    "edge_spatial_dist",
    "efficiency",
    "fastLmBG",
    "fastLmBG_3d",
    "fastLmBG_3dY",
    "fastLmBG_3dY_1p",
    "fastLmBG_f",
    "fastLmBG_t",
    "gateway_coeff",
    "get_thresholds",
    "get.resid",
    "graph_attr_dt",
    "guess_atlas",
    "hubness",
    "import_scn",
    "inv",
    "is_binary",
    "is.brainGraph",
    "is.brainGraphList",
    "loo",
    "make_auc_brainGraph",
    "make_brainGraph",
    "make_brainGraphList",
    "make_ego_brainGraph",
    "make_empty_brainGraph",
    "make_intersection_brainGraph",
    "mean_distance_wt",
    "mtpc",
    "NBS",
    "nregions",
    "pad_zeros",
    "part_coeff",
    "partition",
    "pinv",
    "plot_brainGraph_gui",
    "plot_brainGraph_multi",
    "plot_global",
    "plot_rich_norm",
    "plot_vertex_measures",
    "plot_volumetric",
    "qr_Q2",
    "qr_R2",
    "randomise",
    "randomise_3d",
    "region.names",
    "rich_club_all",
    "rich_club_attrs",
    "rich_club_coeff",
    "rich_club_norm",
    "rich_core",
    "robustness",
    "s_core",
    "set_brainGraph_attr",
    "sim.rand.graph.clust",
    "sim.rand.graph.hqs",
    "sim.rand.graph.par",
    "slicer",
    "small.world",
    "symm_mean",
    "symmetrize",
    "vertex_attr_dt",
    "vertex_spatial_dist",
    "vif.bg_GLM",
    "vulnerability",
    "within_module_deg_z_score",
    "write_brainnet",
    "xfm.weights"
  ],
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      "name": "aal116",
      "title": "Coordinates for data from brain atlases",
      "object": "aal116",
      "class": [
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        "y.mni",
        "z.mni",
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        "hemi",
        "index",
        "network",
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        "Brodmann"
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      "rows": 264,
      "table": true,
      "tojson": true
    }
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      "page": "apply_thresholds",
      "title": "Threshold additional set of matrices",
      "topics": [
        "apply_thresholds"
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    },
    {
      "page": "atlas_helpers",
      "title": "Atlas helper functions",
      "topics": [
        "as_atlas",
        "Atlas Helpers",
        "create_atlas",
        "guess_atlas"
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    },
    {
      "page": "attributes",
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      "topics": [
        "Attributes",
        "set_brainGraph_attr",
        "xfm.weights"
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      "page": "Bootstrapping",
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      "concept": [
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        "Structural covariance network functions"
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      "page": "brain_atlases",
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        "aal2.120",
        "aal2.94",
        "aal90",
        "Brain Atlases",
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        "brainsuite",
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      "page": "brainGraph",
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      "topics": [
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        "brainGraph",
        "brainGraph-options"
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    {
      "page": "brainGraph_permute",
      "title": "Permutation test for group difference of graph measures",
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        "Structural covariance network functions"
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        "brainGraph_permute",
        "plot.brainGraph_permute",
        "summary.brainGraph_permute"
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    },
    {
      "page": "brainGraph-methods",
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      "topics": [
        "brainGraph-methods",
        "groups.brainGraphList",
        "groups.corr_mats",
        "nregions",
        "region.names",
        "region.names.data.table"
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    },
    {
      "page": "brainGraphList",
      "title": "Create a list of brainGraph graphs",
      "concept": [
        "Graph creation functions"
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        "as_brainGraphList",
        "brainGraphList",
        "Extract.brainGraphList",
        "is.brainGraphList",
        "make_brainGraphList",
        "make_brainGraphList.array",
        "make_brainGraphList.corr_mats",
        "nobs.brainGraphList",
        "print.brainGraphList",
        "[.brainGraphList"
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    },
    {
      "page": "centr_betw_comm",
      "title": "Calculate communicability betweenness centrality",
      "concept": [
        "Centrality functions"
      ],
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        "centr_betw_comm"
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    },
    {
      "page": "centr_lev",
      "title": "Calculate a vertex's leverage centrality",
      "concept": [
        "Centrality functions"
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        "centr_lev"
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    },
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      "page": "pad_zeros",
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      "topics": [
        "check_sID",
        "pad_zeros"
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    },
    {
      "page": "coeff_var",
      "title": "Calculate coefficient of variation",
      "topics": [
        "coeff_var",
        "coeff_var.default"
      ]
    },
    {
      "page": "communicability",
      "title": "Calculate communicability",
      "topics": [
        "communicability"
      ]
    },
    {
      "page": "contract_brainGraph",
      "title": "Contract graph vertices based on brain lobe and hemisphere",
      "topics": [
        "contract_brainGraph"
      ]
    },
    {
      "page": "cor.diff.test",
      "title": "Calculate the p-value for differences in correlation coefficients",
      "topics": [
        "cor.diff.test"
      ]
    },
    {
      "page": "correlation_matrices",
      "title": "Calculate correlation matrix and threshold",
      "concept": [
        "Structural covariance network functions"
      ],
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        "corr.matrix",
        "Extract.corr_mats",
        "nregions.corr_mats",
        "plot.corr_mats",
        "region.names.corr_mats",
        "[.corr_mats"
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    },
    {
      "page": "count_edges",
      "title": "Count number of edges of a brain graph",
      "topics": [
        "Count Edges",
        "count_homologous",
        "count_inter"
      ]
    },
    {
      "page": "create_mats",
      "title": "Create connection matrices for tractography or fMRI data",
      "topics": [
        "create_mats"
      ]
    },
    {
      "page": "make_brainGraph",
      "title": "Create a brainGraph object",
      "concept": [
        "Graph creation functions"
      ],
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        "Creating_Graphs",
        "is.brainGraph",
        "make_brainGraph",
        "make_brainGraph.bg_mediate",
        "make_brainGraph.igraph",
        "make_brainGraph.matrix",
        "make_empty_brainGraph",
        "summary.brainGraph"
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    },
    {
      "page": "glm_brainGraphList",
      "title": "Create a graph list with GLM-specific attributes",
      "concept": [
        "Graph creation functions"
      ],
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        "Creating_Graphs_GLM",
        "make_brainGraphList.bg_GLM",
        "make_brainGraphList.mtpc",
        "make_brainGraphList.NBS"
      ]
    },
    {
      "page": "edge_asymmetry",
      "title": "Calculate an asymmetry index based on edge counts",
      "topics": [
        "edge_asymmetry"
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    },
    {
      "page": "efficiency",
      "title": "Calculate graph global, local, or nodal efficiency",
      "topics": [
        "efficiency"
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    },
    {
      "page": "glm",
      "title": "Fit General Linear Models at each vertex of a graph",
      "concept": [
        "GLM functions",
        "Group analysis functions"
      ],
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        "GLM",
        "plot.bg_GLM",
        "print.bg_GLM",
        "summary.bg_GLM",
        "[.bg_GLM"
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    },
    {
      "page": "glm_info",
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        "formula.bg_GLM",
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        "labels.bg_GLM",
        "nobs.bg_GLM",
        "nregions.bg_GLM",
        "region.names.bg_GLM",
        "terms.bg_GLM",
        "variable.names.bg_GLM"
      ]
    },
    {
      "page": "glm_design",
      "title": "Create a design matrix for linear model analysis",
      "concept": [
        "GLM functions"
      ],
      "topics": [
        "brainGraph_GLM_design",
        "GLM design"
      ]
    },
    {
      "page": "glm_fit",
      "title": "Fit design matrices to one or multiple outcomes",
      "concept": [
        "GLM functions"
      ],
      "topics": [
        "fastLmBG",
        "fastLmBG_3d",
        "fastLmBG_3dY",
        "fastLmBG_3dY_1p",
        "fastLmBG_f",
        "fastLmBG_t",
        "GLM fits"
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    },
    {
      "page": "glm_influence",
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      "topics": [
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        "covratio.bg_GLM",
        "dfbeta.bg_GLM",
        "dfbetas.bg_GLM",
        "dffits.bg_GLM",
        "GLM influence measures",
        "hatvalues.bg_GLM",
        "influence.bg_GLM",
        "rstandard.bg_GLM",
        "rstudent.bg_GLM"
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    },
    {
      "page": "glm_model_select",
      "title": "Model selection for bg_GLM objects",
      "topics": [
        "extractAIC.bg_GLM",
        "GLM model selection",
        "logLik.bg_GLM"
      ]
    },
    {
      "page": "glm_stats",
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      "topics": [
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        "coef.bg_GLM",
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        "confint.bg_GLM",
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        "fitted.bg_GLM",
        "GLM statistics",
        "residuals.bg_GLM",
        "sigma.bg_GLM",
        "vcov.bg_GLM"
      ]
    },
    {
      "page": "data_tables",
      "title": "Create a data table with graph global and vertex measures",
      "topics": [
        "Graph Data Tables",
        "graph_attr_dt",
        "vertex_attr_dt"
      ]
    },
    {
      "page": "spatial_dist",
      "title": "Calculate Euclidean distance of edges and vertices",
      "topics": [
        "edge_spatial_dist",
        "Graph Distances",
        "vertex_spatial_dist"
      ]
    },
    {
      "page": "hubness",
      "title": "Calculate vertex hubness",
      "topics": [
        "hubness"
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    },
    {
      "page": "import_scn",
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        "import_scn"
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    {
      "page": "individ_contrib",
      "title": "Approaches to estimate individual network contribution",
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        "Structural covariance network functions"
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      "topics": [
        "aop",
        "IndividualContributions",
        "loo",
        "plot.IC",
        "summary.IC"
      ]
    },
    {
      "page": "inverse",
      "title": "Calculate the inverse of the cross product of a design matrix",
      "topics": [
        "inv",
        "inv.array",
        "inv.list",
        "inv.matrix",
        "inv.qr",
        "Inverse",
        "pinv"
      ]
    },
    {
      "page": "make_auc_brainGraph",
      "title": "Calculate the AUC across densities of given attributes",
      "topics": [
        "make_auc_brainGraph"
      ]
    },
    {
      "page": "make_ego_brainGraph",
      "title": "Create a graph of the union of multiple vertex neighborhoods",
      "concept": [
        "Graph creation functions"
      ],
      "topics": [
        "make_ego_brainGraph"
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    },
    {
      "page": "make_intersection_brainGraph",
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      "topics": [
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    },
    {
      "page": "matrix_utils",
      "title": "Matrix/array utility functions",
      "topics": [
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        "colMaxAbs",
        "colMin",
        "diag_sq",
        "get_thresholds",
        "is_binary",
        "Matrix utilities",
        "qr.array",
        "qr_Q2",
        "qr_R2",
        "symmetrize",
        "symmetrize.array",
        "symmetrize.matrix",
        "symm_mean"
      ]
    },
    {
      "page": "mean_distance_wt",
      "title": "Calculate weighted shortest path lengths",
      "topics": [
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      ]
    },
    {
      "page": "mediation",
      "title": "Mediation analysis with brain graph measures as mediator variables",
      "concept": [
        "Group analysis functions"
      ],
      "topics": [
        "bg_to_mediate",
        "brainGraph_mediate",
        "Mediation",
        "summary.bg_mediate"
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      "page": "mtpc",
      "title": "Multi-threshold permutation correction",
      "concept": [
        "GLM functions",
        "Group analysis functions"
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        "formula.mtpc",
        "labels.mtpc",
        "mtpc",
        "nobs.mtpc",
        "nregions.mtpc",
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        "summary.mtpc",
        "terms.mtpc",
        "variable.names.mtpc"
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      "page": "NBS",
      "title": "Network-based statistic for brain MRI data",
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        "Group analysis functions"
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        "formula.NBS",
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        "NBS",
        "nobs.NBS",
        "nregions.NBS",
        "summary.NBS",
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        "variable.names.NBS"
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    },
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      "page": "plot_brainGraph_gui",
      "title": "GUI for plotting graphs overlaid on an MNI152 image or in a circle",
      "concept": [
        "Plotting functions"
      ],
      "topics": [
        "plot_brainGraph_gui"
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    },
    {
      "page": "plot_brainGraph_multi",
      "title": "Save PNG of one or three views for all graphs in a brainGraphList",
      "concept": [
        "Plotting functions"
      ],
      "topics": [
        "plot_brainGraph_multi",
        "slicer"
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    {
      "page": "plot_global",
      "title": "Plot global graph measures across densities",
      "topics": [
        "plot_global"
      ]
    },
    {
      "page": "plot_rich_norm",
      "title": "Plot normalized rich club coefficients against degree threshold",
      "concept": [
        "Rich-club functions"
      ],
      "topics": [
        "plot_rich_norm"
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    },
    {
      "page": "plot_vertex_measures",
      "title": "Plot vertex-level graph measures at a single density or threshold",
      "topics": [
        "plot_vertex_measures"
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    },
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      "page": "plot_volumetric",
      "title": "Plot group distributions of volumetric measures for a given brain region",
      "concept": [
        "Structural covariance network functions"
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      "page": "plot.brainGraph",
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      "page": "plot.brainGraphList",
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    },
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      "page": "random_graphs",
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      "page": "rich_club",
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      "page": "small.world",
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      "page": "write_brainnet",
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