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  "Package": "sdmTMB",
  "Title": "Spatial and Spatiotemporal SPDE-Based GLMMs with 'TMB'",
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  "Description": "Implements spatial and spatiotemporal GLMMs (Generalized\nLinear Mixed Effect Models) using 'TMB', 'fmesher', and the\nSPDE (Stochastic Partial Differential Equation) Gaussian Markov\nrandom field approximation to Gaussian random fields. One\ncommon application is for spatially explicit species\ndistribution models (SDMs). See Anderson et al. (2025)\n<doi:10.18637/jss.v115.i02>.",
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  "Copyright": "inst/COPYRIGHTS",
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  "Repository": "https://sdmtmb.r-universe.dev",
  "Date/Publication": "2026-05-22 21:26:02 UTC",
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    "get_index_sims",
    "get_index_split",
    "get_pars",
    "get_range_edge",
    "get_weighted_average",
    "ggplot2_installed",
    "halfnormal",
    "lognormal",
    "lognormal_mix",
    "make_areal_domain",
    "make_areal_grid",
    "make_category_svc",
    "make_mesh",
    "mvnormal",
    "nbinom1",
    "nbinom2",
    "nbinom2_mix",
    "normal",
    "pc_matern",
    "plot_anisotropy",
    "plot_anisotropy2",
    "plot_diffused_covariate",
    "plot_diffusion_kernel",
    "plot_pc_matern",
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    "project",
    "replicate_df",
    "run_extra_optimization",
    "sanity",
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    "sdmTMB_stacking",
    "sdmTMBcontrol",
    "sdmTMBpriors",
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    "truncated_nbinom2",
    "tweedie",
    "visreg_delta",
    "visreg2d_delta"
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      "title": "Example fish survey data",
      "object": "dogfish",
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    {
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        "depth"
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    {
      "name": "ohio_df",
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      "object": "ohio_df",
      "class": [
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    {
      "name": "ohio_sf",
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      "object": "ohio_sf",
      "class": [
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        "geometry"
      ],
      "rows": 88,
      "table": false,
      "tojson": true
    },
    {
      "name": "pcod",
      "title": "Example fish survey data",
      "object": "pcod",
      "class": [
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        "tbl",
        "data.frame"
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      "fields": [
        "year",
        "X",
        "Y",
        "depth",
        "density",
        "present",
        "lat",
        "lon",
        "depth_mean",
        "depth_sd",
        "depth_scaled",
        "depth_scaled2"
      ],
      "rows": 2143,
      "table": true,
      "tojson": true
    },
    {
      "name": "pcod_2011",
      "title": "Example fish survey data",
      "object": "pcod_2011",
      "class": [
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        "present",
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        "lon",
        "depth_mean",
        "depth_sd",
        "depth_scaled",
        "depth_scaled2"
      ],
      "rows": 969,
      "table": true,
      "tojson": true
    },
    {
      "name": "pcod_mesh_2011",
      "title": "Example fish survey data",
      "object": "pcod_mesh_2011",
      "class": [
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    {
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        "Y",
        "depth",
        "depth_scaled",
        "depth_scaled2"
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      "table": true,
      "tojson": true
    },
    {
      "name": "wcvi_grid",
      "title": "Example fish survey data",
      "object": "wcvi_grid",
      "class": [
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      ],
      "fields": [
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        "Y",
        "depth"
      ],
      "rows": 2689,
      "table": true,
      "tojson": true
    },
    {
      "name": "yelloweye",
      "title": "Example fish survey data",
      "object": "yelloweye",
      "class": [
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      "fields": [
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        "longitude",
        "latitude",
        "X",
        "Y",
        "catch_count",
        "hook_count",
        "bait_returned",
        "depth"
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      "rows": 1559,
      "table": true,
      "tojson": true
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      "page": "add_utm_columns",
      "title": "Add UTM coordinates to a data frame",
      "topics": [
        "add_utm_columns",
        "get_crs"
      ]
    },
    {
      "page": "cAIC",
      "title": "Calculate conditional AIC",
      "topics": [
        "cAIC"
      ]
    },
    {
      "page": "coef.sdmTMB",
      "title": "Get fixed-effect coefficients",
      "topics": [
        "coef.sdmTMB"
      ]
    },
    {
      "page": "cv_to_waywiser",
      "title": "Convert 'sdmTMB_cv()' objects to 'sf' format for spatial assessment with 'waywiser'",
      "topics": [
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    },
    {
      "page": "dharma_residuals",
      "title": "DHARMa residuals",
      "topics": [
        "dharma_residuals"
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    },
    {
      "page": "Effect.sdmTMB",
      "title": "Calculate effects",
      "topics": [
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    },
    {
      "page": "emmeans.sdmTMB",
      "title": "Estimated marginal means with the 'emmeans' package with 'sdmTMB'",
      "topics": [
        "emmeans.sdmTMB"
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    },
    {
      "page": "get_index",
      "title": "Extract a relative biomass/abundance index, center of gravity, effective area occupied, or weighted average",
      "topics": [
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        "get_eao",
        "get_index",
        "get_index_split",
        "get_weighted_average"
      ]
    },
    {
      "page": "get_index_sims",
      "title": "Calculate a population index via simulation from the joint precision matrix",
      "topics": [
        "get_index_sims"
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    },
    {
      "page": "get_pars",
      "title": "Get TMB parameter list",
      "topics": [
        "get_pars"
      ]
    },
    {
      "page": "get_range_edge",
      "title": "Calculate range edges via simulation from the joint precision matrix",
      "topics": [
        "get_range_edge"
      ]
    },
    {
      "page": "make_areal_domain",
      "title": "Create an areal spatial domain for SAR/CAR models",
      "topics": [
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    },
    {
      "page": "make_areal_grid",
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      "topics": [
        "make_areal_grid"
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    },
    {
      "page": "make_category_svc",
      "title": "Set up spatially varying coefficients for category composition models",
      "topics": [
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    },
    {
      "page": "make_mesh",
      "title": "Construct an SPDE mesh for sdmTMB",
      "topics": [
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        "plot.sdmTMBmesh"
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    },
    {
      "page": "ohio_sf",
      "title": "Ohio lung cancer mortality data, reshaped for spatiotemporal examples",
      "topics": [
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        "ohio_sf"
      ]
    },
    {
      "page": "surveydata",
      "title": "Example fish survey data",
      "topics": [
        "dogfish",
        "hbll_s_grid",
        "pcod",
        "pcod_2011",
        "pcod_mesh_2011",
        "qcs_grid",
        "wcvi_grid",
        "yelloweye"
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    },
    {
      "page": "plot_anisotropy",
      "title": "Plot anisotropy from an sdmTMB model",
      "topics": [
        "plot_anisotropy",
        "plot_anisotropy2"
      ]
    },
    {
      "page": "covariate_diffusion_plots",
      "title": "Plot Covariate-Diffusion Diagnostics on the Mesh",
      "topics": [
        "plot_diffused_covariate",
        "plot_diffusion_kernel"
      ]
    },
    {
      "page": "plot_pc_matern",
      "title": "Plot PC Matérn priors",
      "topics": [
        "plot_pc_matern"
      ]
    },
    {
      "page": "plot_smooth",
      "title": "Plot a smooth term from an sdmTMB model",
      "topics": [
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      ]
    },
    {
      "page": "predict.sdmTMB",
      "title": "Predict from an sdmTMB model",
      "topics": [
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      ]
    },
    {
      "page": "project",
      "title": "Project from an 'sdmTMB' model using simulation",
      "topics": [
        "project"
      ]
    },
    {
      "page": "replicate_df",
      "title": "Replicate a prediction data frame over time",
      "topics": [
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      ]
    },
    {
      "page": "residuals.sdmTMB",
      "title": "Residuals method for sdmTMB models",
      "topics": [
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    },
    {
      "page": "run_extra_optimization",
      "title": "Run extra optimization on an already fitted object",
      "topics": [
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    },
    {
      "page": "sanity",
      "title": "Sanity check of an sdmTMB model",
      "topics": [
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      ]
    },
    {
      "page": "sdmTMB",
      "title": "Fit a spatial or spatiotemporal GLMM with TMB",
      "topics": [
        "sdmTMB"
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    },
    {
      "page": "sdmTMB_cv",
      "title": "Cross validation with sdmTMB models",
      "topics": [
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    },
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      "page": "sdmTMB_stacking",
      "title": "Perform stacking with log scores on 'sdmTMB_cv()' output",
      "topics": [
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    {
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      "title": "Optimization control options",
      "topics": [
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      "topics": [
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        "normal",
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    },
    {
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      "title": "Set delta model for 'ggeffects::ggpredict()'",
      "topics": [
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    },
    {
      "page": "sigma.sdmTMB",
      "title": "Extract residual standard deviation or dispersion parameter",
      "topics": [
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    },
    {
      "page": "simulate_new",
      "title": "Simulate from a spatial/spatiotemporal model",
      "topics": [
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        "simulate_new"
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    },
    {
      "page": "simulate.sdmTMB",
      "title": "Simulate from a fitted sdmTMB model",
      "topics": [
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    },
    {
      "page": "gather_sims",
      "title": "Extract parameter simulations from the joint precision matrix",
      "topics": [
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    },
    {
      "page": "tidy.sdmTMB",
      "title": "Turn sdmTMB model output into a tidy data frame",
      "topics": [
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    },
    {
      "page": "update.sdmTMB",
      "title": "Update an sdmTMB model",
      "topics": [
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    },
    {
      "page": "visreg_delta",
      "title": "Plot sdmTMB models with the 'visreg' package",
      "topics": [
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  "_readme": "https://github.com/sdmtmb/sdmtmb/raw/HEAD/README.md",
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      "title": "sdmTMB model description",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Introduction",
        "Other vignettes available",
        "Notation conventions",
        "sdmTMB model structure",
        "Main effects",
        "Smoothers",
        "Linear break-point threshold models",
        "Logistic threshold models",
        "Spatial random fields",
        "Spatiotemporal random fields",
        "IID spatiotemporal random fields",
        "AR(1) spatiotemporal random fields",
        "Random walk spatiotemporal random fields (RW)",
        "Time-varying regression parameters",
        "Spatially varying coefficients (SVC)",
        "Random intercepts and slopes",
        "Offset terms",
        "Covariate diffusion (distributed lags)",
        "Areal autoregressive models (SAR/CAR)",
        "Observation model families",
        "Bounded or binary outcomes (0–1, proportions)",
        "Binomial",
        "Beta-binomial",
        "Beta",
        "Count data",
        "Poisson",
        "Negative Binomial 2 (NB2)",
        "Negative Binomial 1 (NB1)",
        "Negative binomial 2 mixture",
        "Positive continuous outcomes",
        "Gamma",
        "Lognormal",
        "Generalized gamma",
        "Tweedie",
        "Gamma mixture",
        "Lognormal mixture",
        "Continuous real-valued outcomes",
        "Gaussian",
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        "Zero-inflated and hurdle structures",
        "Delta models",
        "Gaussian random fields",
        "Matérn parameterization",
        "Projection $\\bA$ matrix",
        "Anisotropy",
        "Incorporating physical barriers into the SPDE",
        "Optimization",
        "Optimization details",
        "Assessing convergence",
        "Notation reference tables",
        "References"
      ],
      "created": "2020-10-05 19:40:10",
      "modified": "2026-05-15 05:51:16",
      "commits": 40
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      "os": "win",
      "version": "1.0.0.9011",
      "date": "2026-05-22T22:14:12.000Z",
      "arch": "x86_64",
      "commit": "150bc704e24d247e679b96366a861e3d95235234",
      "fileid": "4d240c6b1dd0ea7f083e6e9085309c05db42389c3e8a76c472aed25bb5790416",
      "status": "success",
      "check": "OK",
      "buildurl": "https://github.com/r-universe/sdmtmb/actions/runs/26314219470"
    }
  ]
}