{
  "_id": "6abd088999f7aa8c6aa4ef80",
  "Package": "BLSM",
  "Title": "Bayesian Latent Space Model",
  "Version": "0.1.1",
  "Date": "2026-08-31",
  "Authors@R": "c(\nperson(\"Alberto\", \"Donizetti\", email = \"albe.donizetti@gmail.com\", role = c(\"aut\", \"cre\")),\nperson(\"Francesca\", \"Ieva\", email = \"francesca.ieva@polimi.it\", role = \"ctb\"))",
  "Description": "Provides a Bayesian latent space model for complex\nnetworks, either weighted or unweighted. Given an observed\ninput graph, the estimates for the latent coordinates of the\nnodes are obtained through a Bayesian MCMC algorithm. The\noverall likelihood of the graph depends on a fundamental\nprobability equation, which is defined so that ties are more\nlikely to exist between nodes whose latent space coordinates\nare close. The package is mainly based on the model by Hoff,\nRaftery and Handcock (2002) <doi:10.1198/016214502388618906>\nand contains some extra features (e.g., removal of the\nProcrustean step, weights implemented as coefficients of the\nlatent distances, 3D plots). The original code related to the\nabove model was retrieved from\n<https://www2.stat.duke.edu/~pdh10/Code/hoff_raftery_handcock_2002_jasa/>.\nUsers can inspect the MCMC simulation, create and customize\ninsightful graphical representations or apply clustering\ntechniques.",
  "License": "GPL (>= 2)",
  "LazyData": "true",
  "Config/roxygen2/version": "8.1.0",
  "Repository": "https://adonizetti.r-universe.dev",
  "Date/Publication": "2026-08-31 16:11:53 UTC",
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    "Date": "2026-09-30 12:56:28 UTC",
    "User": "root"
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  "Author": "Alberto Donizetti [aut, cre],\nFrancesca Ieva [ctb]",
  "Maintainer": "Alberto Donizetti <albe.donizetti@gmail.com>",
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  "_published": "2026-09-30T13:03:05.716Z",
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  "_selfowned": true,
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  "_updates": [
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  "_stars": 3,
  "_contributors": [
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    "extra/citation.html",
    "extra/citation.json",
    "extra/citation.txt",
    "extra/contents.json",
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  "_realowner": "adonizetti",
  "_cranurl": true,
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      "date": "2018-04-26"
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      "date": "2026-09-08"
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    "dst",
    "estimate_latent_positions",
    "lpz_dist",
    "plot_latent_positions",
    "plot_traceplots_acf",
    "proc_crr"
  ],
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      "name": "example_adjacency_matrix",
      "title": "Example Adjacency Matrix",
      "object": "example_adjacency_matrix",
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        "array"
      ],
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    },
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      "title": "Example BLSM object",
      "object": "example_blsm_obj",
      "class": [
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      ],
      "fields": [],
      "table": false,
      "tojson": true
    },
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      "name": "example_weights_matrix",
      "title": "Example Weights Matrix",
      "object": "example_weights_matrix",
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        "matrix",
        "array"
      ],
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      "rows": 10,
      "table": true,
      "tojson": true
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      "page": "alpha_up",
      "title": "Update step for the alpha variable",
      "topics": [
        "alpha_up"
      ]
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      "title": "Bayesian Latent Space Model",
      "topics": [
        "BLSM-package",
        "BLSM"
      ]
    },
    {
      "page": "dst",
      "title": "Geodesic distance",
      "topics": [
        "dst"
      ]
    },
    {
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      "title": "BLSM simulation",
      "topics": [
        "estimate_latent_positions"
      ]
    },
    {
      "page": "example_adjacency_matrix",
      "title": "Example Adjacency Matrix",
      "topics": [
        "example_adjacency_matrix"
      ]
    },
    {
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      "title": "Example BLSM object",
      "topics": [
        "example_blsm_obj"
      ]
    },
    {
      "page": "example_weights_matrix",
      "title": "Example Weights Matrix",
      "topics": [
        "example_weights_matrix"
      ]
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      "topics": [
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