{
  "_id": "6a227df1cd65a98ecbd502a7",
  "Package": "GHRmodel",
  "Title": "Bayesian Hierarchical Modelling of Spatio-Temporal Health Data",
  "Version": "0.1.1",
  "Authors@R": "c(\nperson(\"Carles\", \"Milà\", , \"carles.milagarcia@bsc.es\",\nrole = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0003-0470-0760\")),\nperson(\"Giovenale\", \"Moirano\", role = \"aut\",\ncomment = c(ORCID = \"0000-0001-8748-3321\")),\nperson(\"Anna B.\", \"Kawiecki\", role = \"aut\",\ncomment = c(ORCID = \"0000-0002-0499-2612\")),\nperson(\"Rachel\", \"Lowe\", role = \"aut\",\ncomment = c(ORCID = \"0000-0003-3939-7343\")))",
  "Description": "Supports modeling health outcomes using Bayesian\nhierarchical spatio-temporal models with complex covariate\neffects (e.g., linear, non-linear, interactions, distributed\nlag linear and non-linear models) in the 'INLA' framework. It\nis designed to help users identify key drivers and predictors\nof disease risk by enabling streamlined model exploration,\ncomparison, and visualization of complex covariate effects. See\nan application of the modelling framework in Lowe, Lee,\nO'Reilly et al. (2021) <doi:10.1016/S2542-5196(20)30292-8>.",
  "License": "GPL (>= 2)",
  "URL": "https://gitlab.earth.bsc.es/ghr/ghrmodel,\nhttps://bsc-es.github.io/GHRtools/docs/GHRmodel/GHRmodel",
  "BugReports": "https://gitlab.earth.bsc.es/ghr/ghrmodel/-/issues",
  "Additional_repositories": "https://inla.r-inla-download.org/R/stable",
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  "NeedsCompilation": "no",
  "Packaged": {
    "Date": "2026-06-05 07:37:11 UTC",
    "User": "root"
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  "Author": "Carles Milà [aut, cre] (ORCID:\n<https://orcid.org/0000-0003-0470-0760>), Giovenale Moirano\n[aut] (ORCID: <https://orcid.org/0000-0001-8748-3321>), Anna B.\nKawiecki [aut] (ORCID:\n<https://orcid.org/0000-0002-0499-2612>), Rachel Lowe [aut]\n(ORCID: <https://orcid.org/0000-0003-3939-7343>)",
  "Maintainer": "Carles Milà <carles.milagarcia@bsc.es>",
  "Repository": "https://cran.r-universe.dev",
  "Date/Publication": "2025-11-07 16:41:19 UTC",
  "RemoteUrl": "https://github.com/cran/GHRmodel",
  "RemoteRef": "HEAD",
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  "MD5sum": "50ed1010878e27114245bb2f871a86f6",
  "_user": "cran",
  "_type": "src",
  "_file": "GHRmodel_0.1.1.tar.gz",
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  "_created": "2026-06-05T07:37:11.000Z",
  "_published": "2026-06-05T07:42:41.145Z",
  "_distro": "noble",
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  "_commit": {
    "id": "d994990ac7c1d05cb9626e666f6fcb75605392de",
    "author": "Carles Milà <carles.milagarcia@bsc.es>",
    "committer": "cran-robot <csardi.gabor+cran@gmail.com>",
    "message": "version 0.1.1\n",
    "time": 1762533679
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    "name": "Carles Milà",
    "email": "carles.milagarcia@bsc.es",
    "orcid": "0000-0003-0470-0760"
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  "_dependencies": [
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      "version": ">= 4.1.0",
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      "name": "0.1.0",
      "date": "2025-10-21"
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    "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/GHRmodel"
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  "_exports": [
    "as_GHRformulas",
    "cov_add",
    "cov_interact",
    "cov_multi",
    "cov_nl",
    "cov_uni",
    "cov_varying",
    "crossbasis_inla",
    "crosspred_inla",
    "extract_names",
    "fit_models",
    "get_covariates",
    "lag_cov",
    "onebasis_inla",
    "plot_coef_crosspred",
    "plot_coef_lin",
    "plot_coef_nl",
    "plot_coef_varying",
    "plot_fit",
    "plot_gof",
    "plot_ppd",
    "plot_re",
    "rank_models",
    "sample_ppd",
    "stack_models",
    "subset_models",
    "write_inla_formulas"
  ],
  "_datasets": [
    {
      "name": "dengue_MS",
      "title": "Dengue cases from the \"Mato Grosso do Sul\" state of Brazil",
      "object": "dengue_MS",
      "class": [
        "spec_tbl_df",
        "tbl_df",
        "tbl",
        "data.frame"
      ],
      "fields": [
        "micro_code",
        "micro_name",
        "micro_name_ibge",
        "meso_code",
        "meso_name",
        "state_code",
        "state_name",
        "region_code",
        "region_name",
        "biome_code",
        "biome_name",
        "ecozone_code",
        "ecozone_name",
        "main_climate",
        "month",
        "year",
        "time",
        "dengue_cases",
        "population",
        "pop_density",
        "tmax",
        "tmin",
        "pdsi",
        "urban",
        "water_network",
        "water_shortage",
        "date"
      ],
      "rows": 2640,
      "table": true,
      "tojson": true
    },
    {
      "name": "dengue_SP",
      "title": "Dengue cases from the \"São Paulo\" state of Brazil",
      "object": "dengue_SP",
      "class": [
        "data.frame"
      ],
      "fields": [
        "date",
        "geocode",
        "cases",
        "year",
        "temp_med",
        "precip_tot",
        "enso",
        "pop"
      ],
      "rows": 678,
      "table": true,
      "tojson": true
    },
    {
      "name": "map_MS",
      "title": "Administrative Map for Municipalities in the Mato Grosso do Sul",
      "object": "map_MS",
      "class": [
        "sf",
        "data.frame"
      ],
      "fields": [
        "code",
        "geometry"
      ],
      "rows": 11,
      "table": false,
      "tojson": true
    }
  ],
  "_help": [
    {
      "page": "as_GHRformulas",
      "title": "Convert R-INLA Model Formulas into a GHRformulas Object",
      "topics": [
        "as_GHRformulas"
      ]
    },
    {
      "page": "cov_add",
      "title": "Add Covariates to All Combinations",
      "topics": [
        "cov_add"
      ]
    },
    {
      "page": "cov_interact",
      "title": "Generate Interaction Terms Between Covariates",
      "topics": [
        "cov_interact"
      ]
    },
    {
      "page": "cov_multi",
      "title": "Create Covariate Combinations Across Groups",
      "topics": [
        "cov_multi"
      ]
    },
    {
      "page": "cov_nl",
      "title": "Create Non-Linear Effects for INLA",
      "topics": [
        "cov_nl"
      ]
    },
    {
      "page": "cov_uni",
      "title": "Build Univariable Covariate Sets",
      "topics": [
        "cov_uni"
      ]
    },
    {
      "page": "cov_varying",
      "title": "Create Spatially or Temporally Varying Effects for INLA",
      "topics": [
        "cov_varying"
      ]
    },
    {
      "page": "crossbasis_inla",
      "title": "Create a Two-Dimensional INLA-compatible Cross-basis Matrix",
      "topics": [
        "crossbasis_inla"
      ]
    },
    {
      "page": "crosspred_inla",
      "title": "Generate DLNM Predictions from 'GHRmodels' Objects",
      "topics": [
        "crosspred_inla"
      ]
    },
    {
      "page": "dengue_MS",
      "title": "Dengue cases from the \"Mato Grosso do Sul\" state of Brazil",
      "topics": [
        "dengue_MS"
      ]
    },
    {
      "page": "dengue_SP",
      "title": "Dengue cases from the \"São Paulo\" state of Brazil",
      "topics": [
        "dengue_SP"
      ]
    },
    {
      "page": "extract_names",
      "title": "Extract Covariate Names",
      "topics": [
        "extract_names"
      ]
    },
    {
      "page": "fit_models",
      "title": "Fit Multiple INLA Models",
      "topics": [
        "fit_models"
      ]
    },
    {
      "page": "get_covariates",
      "title": "Retrieve Covariates from a 'GHRmodels' Object as a List of Character Vectors",
      "topics": [
        "get_covariates"
      ]
    },
    {
      "page": "lag_cov",
      "title": "Generate lagged variables for one or more lags",
      "topics": [
        "lag_cov"
      ]
    },
    {
      "page": "map_MS",
      "title": "Administrative Map for Municipalities in the Mato Grosso do Sul",
      "topics": [
        "map_MS"
      ]
    },
    {
      "page": "onebasis_inla",
      "title": "Create a One-Dimensional Basis for INLA",
      "topics": [
        "onebasis_inla"
      ]
    },
    {
      "page": "plot_coef_crosspred",
      "title": "Plot 'crosspred' Objects: Overall, Slices, or Heatmap",
      "topics": [
        "plot_coef_crosspred"
      ]
    },
    {
      "page": "plot_coef_lin",
      "title": "Produce a Forest Plot of Linear Covariates from a 'GHRmodels' Object",
      "topics": [
        "plot_coef_lin"
      ]
    },
    {
      "page": "plot_coef_nl",
      "title": "Plot Nonlinear Effects from a 'GHRmodels' Object",
      "topics": [
        "plot_coef_nl"
      ]
    },
    {
      "page": "plot_coef_varying",
      "title": "Produce a Forest Plot for a Spatially or Temporally Varying Effects from a 'GHRmodels' object.",
      "topics": [
        "plot_coef_varying"
      ]
    },
    {
      "page": "plot_fit",
      "title": "Plot Observed vs. Fitted Cases",
      "topics": [
        "plot_fit"
      ]
    },
    {
      "page": "plot_gof",
      "title": "Plot Models by Goodness-of-Fit",
      "topics": [
        "plot_gof"
      ]
    },
    {
      "page": "plot_ppd",
      "title": "Plot Posterior Predictive Densities Versus Observed Data",
      "topics": [
        "plot_ppd"
      ]
    },
    {
      "page": "plot_re",
      "title": "Plot Random Effects",
      "topics": [
        "plot_re"
      ]
    },
    {
      "page": "rank_models",
      "title": "Rank Models by Goodness-of-Fit",
      "topics": [
        "rank_models"
      ]
    },
    {
      "page": "sample_ppd",
      "title": "Sample from the Posterior Predictive Distribution",
      "topics": [
        "sample_ppd"
      ]
    },
    {
      "page": "stack_models",
      "title": "Merge GHRmodels",
      "topics": [
        "stack_models"
      ]
    },
    {
      "page": "subset_models",
      "title": "Subset 'GHRmodels' Objects",
      "topics": [
        "subset_models"
      ]
    },
    {
      "page": "write_inla_formulas",
      "title": "Generate INLA-compatible Model Formulas",
      "topics": [
        "write_inla_formulas"
      ]
    }
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      "source": "GHRmodel_covariates.Rmd",
      "filename": "GHRmodel_covariates.html",
      "title": "Complex Covariate Structures",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Overview",
        "GHRmodel formula helper functions",
        "0. Prepare data",
        "Load libraries",
        "Data pre-processing",
        "Spatial data and graphs",
        "Pre-process covariates",
        "Lagged covariates",
        "Define priors",
        "Example 1: GHRmodel helper functions",
        "1. Model development",
        "Select variables",
        "Linear covariates",
        "Non-linear covariates",
        "Non-linear covariates replicated by group",
        "Covariates for multivariate models",
        "Add a covariate to all covariate lists",
        "Interacting covariates",
        "Varying covariates",
        "Varying vs. Replicated Effects in INLA",
        "Write INLA-compatible model formulas",
        "2. Model fitting",
        "3. Model evaluation",
        "Interaction effects",
        "Varying linear coefficients",
        "Replicated nonlinear coefficients",
        "Example 2: INLA-compatible formulas",
        "References"
      ],
      "created": "2025-10-21 18:45:27",
      "modified": "2025-11-07 16:41:19",
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    {
      "source": "GHRmodel_DLNM.Rmd",
      "filename": "GHRmodel_DLNM.html",
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      "headings": [
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        "Example: DLNMs in GHRmodel",
        "0. Prepare data",
        "Load libraries",
        "Data pre-processing",
        "Spatial data and graphs",
        "1. Model development",
        "One-dimensional basis matrix",
        "Cross-basis matrix",
        "Model formulas including DLNM terms",
        "2. Fit DLNMs with INLA",
        "3. DLNM output",
        "One-basis terms",
        "Cross-basis terms",
        "References"
      ],
      "created": "2025-10-21 18:45:27",
      "modified": "2025-11-07 16:41:19",
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      "engine": "knitr::rmarkdown",
      "headings": [
        "Overview",
        "Installation",
        "Data requirements",
        "Methodology",
        "GHRmodel structure",
        "1. Model development",
        "2. Model fitting",
        "3. Model evaluation",
        "GHRmodel workflow",
        "0. Data",
        "Dataset description",
        "Data pre-processing",
        "Spatial data and graphs",
        "Create lagged covariates",
        "Write covariates",
        "Write formulas",
        "Rank models",
        "Posterior predictive checks",
        "Goodness-of-fit metrics",
        "Fitted vs. Observed",
        "Covariate effects",
        "Evaluate random effects",
        "4. Iterative model selection",
        "Subset models",
        "Extract covariates",
        "Add an additional covariate",
        "Fit new models",
        "Combine models",
        "Evaluate combined models",
        "References"
      ],
      "created": "2025-10-21 18:45:27",
      "modified": "2025-11-07 16:41:19",
      "commits": 2
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