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  "Title": "Statistical Network Analysis of Animal Social Networks",
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    "correlation_analyze",
    "distance_radian_coordinates",
    "get_coordinates_in_radian",
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    "get_network_summary",
    "get_spatial_threshold",
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    "obtain_bootstrapped_samples",
    "obtain_network_subsamples",
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    "subsampled_network_metrics",
    "subsampled_permuted_network_metrics"
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      "name": "elk_2010_permutations",
      "title": "A list of 100 igraph objects obtained by permuting the raw elk_data_2010 and obtaining network from those",
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      "class": [
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        "distance"
      ],
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      "table": true,
      "tojson": true
    },
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      "name": "elk_data_2010",
      "title": "Data to showcase functions in our package",
      "object": "elk_data_2010",
      "class": [
        "data.frame"
      ],
      "fields": [
        "animal_id",
        "datetime",
        "latitude_rad",
        "longitude_rad"
      ],
      "rows": 123568,
      "table": true,
      "tojson": true
    },
    {
      "name": "elk_interactions_2010",
      "title": "Dataset of interactions from elk_data_2010 using first mode as the spatial threshold",
      "object": "elk_interactions_2010",
      "class": [
        "data.frame"
      ],
      "fields": [
        "Animal_A",
        "Animal_B",
        "Timestamp_A",
        "Timestamp_B",
        "distance"
      ],
      "rows": 2393,
      "table": true,
      "tojson": true
    },
    {
      "name": "elk_network_2010",
      "title": "An igraph object depicting the network obtained from elk_interactions_2010",
      "object": "elk_network_2010",
      "class": [
        "igraph"
      ],
      "fields": [],
      "table": false,
      "tojson": false
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  "_help": [
    {
      "page": "aniSNA",
      "title": "aniSNA",
      "topics": [
        "aniSNA"
      ]
    },
    {
      "page": "bootstrapped_difference_pvalues",
      "title": "To obtain two non-overlapping bootstrapped versions and obtain p-values for the significance of difference between them",
      "topics": [
        "bootstrapped_difference_pvalues"
      ]
    },
    {
      "page": "correlation_analyze",
      "title": "To perform correlation analysis for node-level network metrics",
      "topics": [
        "correlation_analyze"
      ]
    },
    {
      "page": "distance_radian_coordinates",
      "title": "Calculate distance between two pairs of radian coordinates",
      "topics": [
        "distance_radian_coordinates"
      ]
    },
    {
      "page": "elk_2010_permutations",
      "title": "A list of 100 igraph objects obtained by permuting the raw elk_data_2010 and obtaining network from those",
      "topics": [
        "elk_2010_permutations"
      ]
    },
    {
      "page": "elk_all_interactions_2010",
      "title": "Dataset of all possible interactions from elk_data_2010",
      "topics": [
        "elk_all_interactions_2010"
      ]
    },
    {
      "page": "elk_data_2010",
      "title": "Data to showcase functions in our package",
      "topics": [
        "elk_data_2010"
      ]
    },
    {
      "page": "elk_interactions_2010",
      "title": "Dataset of interactions from elk_data_2010 using first mode as the spatial threshold",
      "topics": [
        "elk_interactions_2010"
      ]
    },
    {
      "page": "elk_network_2010",
      "title": "An igraph object depicting the network obtained from elk_interactions_2010",
      "topics": [
        "elk_network_2010"
      ]
    },
    {
      "page": "get_coordinates_in_radian",
      "title": "To convert latitude and longitude values from degrees to radians",
      "topics": [
        "get_coordinates_in_radian"
      ]
    },
    {
      "page": "get_interactions",
      "title": "To obtain interactions from raw GPS observations",
      "topics": [
        "get_interactions"
      ]
    },
    {
      "page": "get_network_summary",
      "title": "Calculates and prints network summary statistics",
      "topics": [
        "get_network_summary"
      ]
    },
    {
      "page": "get_spatial_threshold",
      "title": "To obtain spatial threshold for calculating interactions from raw GPS observations. The threshold is obtained as the distance interval that captures maximum number of inter-individual interactions.",
      "topics": [
        "get_spatial_threshold"
      ]
    },
    {
      "page": "global_CI",
      "title": "To obtain confidence intervals around the observed global network statistics",
      "topics": [
        "global_CI"
      ]
    },
    {
      "page": "global_width_CI",
      "title": "To obtain width of confidence intervals for global network metrics using bootstrapped versions at each level of sub-sampling",
      "topics": [
        "global_width_CI"
      ]
    },
    {
      "page": "interacting_pairs",
      "title": "Function to obtain pairs of interacting animals",
      "topics": [
        "interacting_pairs"
      ]
    },
    {
      "page": "network_from_interactions",
      "title": "Function to obtain a network structure from interactions dataframe",
      "topics": [
        "network_from_interactions"
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    },
    {
      "page": "node_level_CI",
      "title": "To obtain confidence intervals for node-level network metrics",
      "topics": [
        "node_level_CI"
      ]
    },
    {
      "page": "obtain_bootstrapped_samples",
      "title": "To obtain bootstrapped versions of a network's adjacency matrix",
      "topics": [
        "obtain_bootstrapped_samples"
      ]
    },
    {
      "page": "obtain_network_subsamples",
      "title": "To obtain sub-networks of the observed network",
      "topics": [
        "obtain_network_subsamples"
      ]
    },
    {
      "page": "obtain_permuted_network_versions",
      "title": "Function to obtain permuted networks from raw datastream",
      "topics": [
        "obtain_permuted_network_versions"
      ]
    },
    {
      "page": "plot_network",
      "title": "Visualize Animal Network",
      "topics": [
        "plot_network"
      ]
    },
    {
      "page": "plot.bootstrapped_pvalue_matrix",
      "title": "To plot the results obtained from bootstrapped_difference_pvalues function",
      "topics": [
        "plot.bootstrapped_pvalue_matrix"
      ]
    },
    {
      "page": "plot.list_correlation_matrices",
      "title": "To plot correlation analysis results",
      "topics": [
        "plot.list_correlation_matrices"
      ]
    },
    {
      "page": "plot.list_node_level_CI",
      "title": "To plot the results for node-level confidence intervals",
      "topics": [
        "plot.list_node_level_CI"
      ]
    },
    {
      "page": "plot.list_permuted_networks",
      "title": "Function to plot the network metrics distribution of permuted networks",
      "topics": [
        "plot.list_permuted_networks"
      ]
    },
    {
      "page": "plot.list_regression_matrices",
      "title": "To plot regression analysis results",
      "topics": [
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    },
    {
      "page": "plot.Subsampled_Network_Metrics",
      "title": "To plot sub-sampling results",
      "topics": [
        "plot.Subsampled_Network_Metrics"
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    },
    {
      "page": "plot.Subsampled_Permuted_Network_Metrics",
      "title": "To plot sub-sampling results of the original network and permuted networks",
      "topics": [
        "plot.Subsampled_Permuted_Network_Metrics"
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    },
    {
      "page": "plot.Width_CI_matrix",
      "title": "To plot the results obtained from width_CI function",
      "topics": [
        "plot.Width_CI_matrix"
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    },
    {
      "page": "regression_slope_analyze",
      "title": "To perform regression analysis for local network metrics",
      "topics": [
        "regression_slope_analyze"
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    {
      "page": "subsampled_network_metrics",
      "title": "To generate subsamples and obtain network metrics of the subsamples",
      "topics": [
        "subsampled_network_metrics"
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    },
    {
      "page": "subsampled_permuted_network_metrics",
      "title": "To generate subsamples of the permuted networks and obtain network metrics of those subsamples",
      "topics": [
        "subsampled_permuted_network_metrics"
      ]
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