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  "Title": "Extracting and Visualizing Bayesian Graphical Models",
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  "Description": "Fit and visualize the results of a Bayesian analysis of\nnetworks commonly found in psychology. The package supports\ncross-sectional network models for ordinal, binary, continuous,\nand mixed data, fitted using the packages 'bgms' (default),\n'BDgraph', and 'BGGM', as well as network comparison tests\nfitted using the packages 'bgms' and 'BGGM'. The package\nprovides the parameter estimates, posterior inclusion\nprobabilities, inclusion Bayes factor, and the posterior\ndensity of the parameters. In addition, for 'BDgraph' and\n'bgms' it allows to assess the posterior structure space.\nFurthermore, the package comes with an extensive suite for\nvisualizing results.",
  "License": "GPL (>= 2)",
  "URL": "https://github.com/KarolineHuth/easybgm",
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  "Repository": "https://karolinehuth.r-universe.dev",
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      "page": "bgm_extract",
      "title": "Extract the results of a Bayesian analysis of networks",
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        "bgm_extract"
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      "page": "bgm_fit",
      "title": "Fit a Bayesian analysis of networks",
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      ]
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      "page": "centrality",
      "title": "Plot strength centralities and 95% highest density interval",
      "topics": [
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        "plot_centrality"
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    },
    {
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      "title": "Test whether a network splits into clusters",
      "topics": [
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    },
    {
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      "title": "Plot posterior complexity probabilities",
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        "plot_complexity_probabilities"
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    },
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      "page": "easybgm",
      "title": "Bayesian analysis of networks",
      "topics": [
        "easybgm"
      ]
    },
    {
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      "topics": [
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      ]
    },
    {
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      "topics": [
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        "plot_edgeevidence"
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    },
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      "topics": [
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        "plot_parameterHDI"
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    },
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      "title": "Network plot",
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        "plot_network"
      ]
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      "title": "Print method for 'easybgm' objects",
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    },
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    },
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        "structure"
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        "structure_probs"
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    },
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