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  "Title": "Hierarchical Bayesian Species Distribution Models",
  "Version": "1.4.4",
  "Date": "2023-09-06",
  "NeedsCompilation": "yes",
  "SystemRequirements": "GNU GSL",
  "Authors@R": "c(\nperson(\"Ghislain\", \"Vieilledent\", role=c(\"aut\",\"cre\"),\nemail=\"ghislain.vieilledent@cirad.fr\",\ncomment=c(ORCID=\"0000-0002-1685-4997\")),\nperson(\"Matthieu\", \"Autier\", role=\"ctb\"),\nperson(\"Alan E.\", \"Gelfand\", role=\"ctb\"),\nperson(\"Jérôme\", \"Guélat\", role=\"ctb\"),\nperson(\"Marc\", \"Kéry\", role=\"ctb\"),\nperson(\"Andrew M.\", \"Latimer\", role=\"ctb\"),\nperson(\"Cory\", \"Merow\", role=\"ctb\"),\nperson(\"Frédéric\", \"Mortier\", role=\"ctb\"),\nperson(\"John A.\", \"Silander Jr.\", role=\"ctb\"),\nperson(\"Adam M.\", \"Wilson\", role=\"ctb\"),\nperson(\"Shanshan\", \"Wu\", role=\"ctb\"),\nperson(\"CIRAD\", role=c(\"cph\", \"fnd\"))\n)",
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  "Description": "User-friendly and fast set of functions for estimating\nparameters of hierarchical Bayesian species distribution models\n(Latimer and others 2006 <doi:10.1890/04-0609>). Such models\nallow interpreting the observations (occurrence and abundance\nof a species) as a result of several hierarchical processes\nincluding ecological processes (habitat suitability, spatial\ndependence and anthropogenic disturbance) and observation\nprocesses (species detectability). Hierarchical species\ndistribution models are essential for accurately characterizing\nthe environmental response of species, predicting their\nprobability of occurrence, and assessing uncertainty in the\nmodel results.",
  "License": "GPL-3",
  "URL": "https://ecology.ghislainv.fr/hSDM/,\nhttps://github.com/ghislainv/hSDM/",
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  "Date/Publication": "2023-09-06 15:39:53 UTC",
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    "hSDM.ZIB.iCAR.alteration",
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    "hSDM.ZIP.iCAR",
    "hSDM.ZIP.iCAR.alteration",
    "inv.logit",
    "logit"
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      "object": "frogs",
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      ],
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        "y"
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      "table": true,
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      "title": "Occurrence data for _Protea punctata_ Meisn. in the Cap Floristic Region",
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      "title": "hierarchical Bayesian species distribution models",
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        "hSDM"
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      "title": "Virtual altitudinal data",
      "topics": [
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    },
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      "title": "Environmental data for South Africa's Cap Floristic Region",
      "topics": [
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      "topics": [
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    },
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      "title": "Binomial logistic regression model",
      "topics": [
        "hSDM.binomial"
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      "title": "Binomial logistic regression model with CAR process",
      "topics": [
        "hSDM.binomial.iCAR"
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      "title": "N-mixture model with CAR process",
      "topics": [
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      "title": "N-mixture model with K, the maximal theoretical abundance",
      "topics": [
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      ]
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      "title": "Poisson log regression model",
      "topics": [
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    },
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      "title": "Poisson log regression model with CAR process",
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      "topics": [
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      "title": "Site-occupancy model with CAR process",
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      "topics": [
        "hSDM.ZIB"
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      "topics": [
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      "topics": [
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    },
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      "topics": [
        "hSDM.ZIP"
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    },
    {
      "page": "hSDM.ZIP.iCAR",
      "title": "ZIP (Zero-Inflated Poisson) model with CAR process",
      "topics": [
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      "topics": [
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        "logit"
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      "title": "Predict method for models fitted with hSDM",
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      "title": "Occurrence data for _Protea punctata_ Meisn. in the Cap Floristic Region",
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