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  "Title": "Bayesian Modeling of Spatial Count Data",
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  "Authors@R": "c(\nperson(\"Mahsa\", \"Nadifar\", email = \"mahsa.nst@gmail.com\", role = c(\"aut\", \"cre\"), comment = c(ORCID = \"0000-0002-9130-125X\")),\nperson(\"Hossein\", \"Baghishani\", email = \"hbaghishani@gmail.com\", role = \"aut\", comment = c(ORCID = \"0000-0002-6396-0303\"))\n)",
  "Maintainer": "Mahsa Nadifar <mahsa.nst@gmail.com>",
  "Description": "Provides a collection of functions for preparing data and\nfitting Bayesian count spatial regression models, with a\nspecific focus on the Gamma-Count (GC) model. The GC model is\nwell-suited for modeling dispersed count data, including\nunder-dispersed or over-dispersed counts, or counts with\nequivalent dispersion, using Integrated Nested Laplace\nApproximations (INLA). The package includes functions for\ngenerating data from the GC model, as well as spatially\ncorrelated versions of the model. See Nadifar, Baghishani,\nFallah (2023) <doi:10.1007/s13253-023-00550-5>.",
  "License": "GPL (>= 2)",
  "URL": "https://github.com/mahsanst/SpatGC",
  "Encoding": "UTF-8",
  "RoxygenNote": "7.3.1",
  "Additional_repositories": "https://inla.r-inla-download.org/R/testing",
  "Config/pak/sysreqs": "libabsl-dev cmake libgdal-dev gdal-bin libgeos-dev\nlibssl-dev libproj-dev libsqlite3-dev libudunits2-dev",
  "Repository": "https://mahsanst.r-universe.dev",
  "Date/Publication": "2024-04-27 09:11:07 UTC",
  "RemoteUrl": "https://github.com/mahsanst/spatgc",
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    "User": "root"
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  "Author": "Mahsa Nadifar [aut, cre] (ORCID:\n<https://orcid.org/0000-0002-9130-125X>),\nHossein Baghishani [aut] (ORCID:\n<https://orcid.org/0000-0002-6396-0303>)",
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    "G",
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    {
      "page": "GC",
      "title": "Gamma-Count (GC) Distribution",
      "topics": [
        "dGC",
        "G",
        "GC",
        "pGC",
        "qGC",
        "rGC"
      ]
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    {
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      "title": "Fit ICAR Spatial Gamma-Count Model",
      "topics": [
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      ]
    },
    {
      "page": "NBlat",
      "title": "Fit Negative Binomial Spatial Model",
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    {
      "page": "Poislat",
      "title": "Fit Poisson Spatial Model",
      "topics": [
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      ]
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      "title": "Generate a Random Adjacency Matrix",
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      ]
    },
    {
      "page": "rGCgeo",
      "title": "Generate Data from GC Spatial Regression Model with Geospatial Dependency",
      "topics": [
        "rGCgeo"
      ]
    },
    {
      "page": "rGClat",
      "title": "Generate Data from GC Spatial Regression Model with Lattice Spatial Effect",
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        "rGClat"
      ]
    },
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      "page": "spatCAR",
      "title": "Generate Spatial Random Fields from CAR Models",
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      ]
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    {
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      "title": "Generate Spatial Random Fields from Matern Covariance Model",
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    },
    {
      "page": "spatICAR",
      "title": "Generate Spatial Random Fields from ICAR Models",
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