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  "_id": "6ac4b874f511ae58c09a0670",
  "Package": "SDALGCP2",
  "Title": "Discrete Log-Gaussian Cox Processes for Aggregated Disease\nCounts",
  "Version": "0.1.1",
  "Authors@R": "c(\nperson(\"Olatunji\", \"Johnson\", email = \"olatunjijohnson21111@gmail.com\", role = c(\"aut\", \"cre\")),\nperson(\"Emanuele\", \"Giorgi\", role = \"aut\"),\nperson(\"Peter\", \"Diggle\", role = \"aut\"))",
  "Description": "Fits a spatially discrete approximation to a log-Gaussian\nCox process model for spatially aggregated disease count data,\nestimated by Monte Carlo Maximum Likelihood as in Christensen\n(2004) <doi:10.1198/106186004X2525> and Johnson, Diggle and\nGiorgi (2019) <doi:10.1002/sim.8339>. Performance-critical\nsteps (aggregated correlation assembly, Metropolis-adjusted\nLangevin algorithm (MALA) sampling, the Monte Carlo likelihood,\nand the Kronecker-structured space-time likelihood) are\nimplemented in C++ via 'RcppArmadillo'. Provides a one-line,\n'glm'-like interface and statistical extensions including a\nnugget term, general 'Matern' smoothness, raster and misaligned\ncovariates, restricted spatial regression, importance-sampling\ndiagnostics and re-anchored Monte Carlo maximum likelihood\n(MCML).",
  "License": "GPL-2 | GPL-3",
  "Encoding": "UTF-8",
  "Language": "en-GB",
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  "URL": "https://github.com/olatunjijohnson/SDALGCP2,\nhttps://olatunjijohnson.github.io/SDALGCP2/",
  "BugReports": "https://github.com/olatunjijohnson/SDALGCP2/issues",
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  "Repository": "https://olatunjijohnson.r-universe.dev",
  "Date/Publication": "2026-07-07 08:14:02 UTC",
  "RemoteUrl": "https://github.com/olatunjijohnson/sdalgcp2",
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  "NeedsCompilation": "yes",
  "Packaged": {
    "Date": "2026-10-06 08:52:12 UTC",
    "User": "root"
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  "Author": "Olatunji Johnson [aut, cre],\nEmanuele Giorgi [aut],\nPeter Diggle [aut]",
  "Maintainer": "Olatunji Johnson <olatunjijohnson21111@gmail.com>",
  "_user": "olatunjijohnson",
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  "_expires": "2027-01-14T08:59:30.000Z",
  "_created": "2026-10-06T08:52:12.000Z",
  "_published": "2026-10-06T08:59:32.518Z",
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    "author": "olatunjijohnson <teejaystatistics@gmail.com>",
    "committer": "olatunjijohnson <teejaystatistics@gmail.com>",
    "message": "Title: keep 'Disease' -> 'Discrete Log-Gaussian Cox Processes for Aggregated Disease Counts'\n\n65 chars (<= CRAN's 65 limit). Aligns the package-doc title with the\nDESCRIPTION Title and regenerates man/ (adds the internal\nset_omp_num_threads.Rd that was previously missing).\n\nCo-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>\n",
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    "description": "Lecturer in Statistics at the University of Manchester"
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    "control_mcmc",
    "exceedance",
    "laplace_sampling",
    "map_exceedance",
    "mc_diagnostics",
    "mcml_fit",
    "model_check",
    "phi_profile",
    "precompute_corr",
    "report",
    "sda_points",
    "sdalgcp",
    "sdalgcp_control",
    "SDALGCP2",
    "SDALGCP2_misaligned",
    "SDALGCP2_raster",
    "SDALGCP2_ST"
  ],
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    {
      "name": "liver",
      "title": "Primary biliary cirrhosis incidence in North East England",
      "object": "liver",
      "class": [
        "sf",
        "data.frame"
      ],
      "fields": [
        "lsoa",
        "cases",
        "pop",
        "IMD",
        "Income",
        "Employment",
        "geometry"
      ],
      "rows": 545,
      "table": false,
      "tojson": true
    },
    {
      "name": "sdalgcp_data",
      "title": "Simulated aggregated disease-count data",
      "object": "sdalgcp_data",
      "class": [
        "sf",
        "data.frame"
      ],
      "fields": [
        "region",
        "cases",
        "x1",
        "pop",
        "geometry"
      ],
      "rows": 64,
      "table": false,
      "tojson": true
    }
  ],
  "_help": [
    {
      "page": "coef_plot",
      "title": "Coefficient plot of fixed effects (and sigma^2) with confidence intervals",
      "topics": [
        "coef_plot"
      ]
    },
    {
      "page": "confint.SDALGCP2",
      "title": "Wald confidence intervals for an SDALGCP2 fit",
      "topics": [
        "confint.SDALGCP2"
      ]
    },
    {
      "page": "control_mcmc",
      "title": "MCMC control settings for the MALA sampler",
      "topics": [
        "control_mcmc"
      ]
    },
    {
      "page": "exceedance",
      "title": "Exceedance probabilities P(risk > threshold)",
      "topics": [
        "exceedance"
      ]
    },
    {
      "page": "laplace_sampling",
      "title": "Sample the latent field [S | Y] (Poisson, non-nested) via C++ MALA",
      "topics": [
        "laplace_sampling"
      ]
    },
    {
      "page": "liver",
      "title": "Primary biliary cirrhosis incidence in North East England",
      "topics": [
        "liver"
      ]
    },
    {
      "page": "map_exceedance",
      "title": "Map exceedance probabilities P(risk > threshold)",
      "topics": [
        "map_exceedance"
      ]
    },
    {
      "page": "mc_diagnostics",
      "title": "Importance-sampling diagnostics for an MCML fit",
      "topics": [
        "mc_diagnostics"
      ]
    },
    {
      "page": "mcml_fit",
      "title": "Monte Carlo maximum likelihood estimation for the spatial SDA-LGCP",
      "topics": [
        "mcml_fit"
      ]
    },
    {
      "page": "model_check",
      "title": "Posterior-predictive model checking for an SDALGCP2 fit",
      "topics": [
        "model_check"
      ]
    },
    {
      "page": "phi_profile",
      "title": "Profile likelihood and confidence interval for the spatial scale phi",
      "topics": [
        "phi_profile"
      ]
    },
    {
      "page": "plot.sdalgcp",
      "title": "Map an sdalgcp fit",
      "topics": [
        "plot.sdalgcp"
      ]
    },
    {
      "page": "plot.SDALGCP2",
      "title": "Plot an SDALGCP2 fit (the phi profile deviance)",
      "topics": [
        "plot.SDALGCP2"
      ]
    },
    {
      "page": "plot.SDALGCP2_pred",
      "title": "Map a fitted SDALGCP2 prediction",
      "topics": [
        "plot.SDALGCP2_pred"
      ]
    },
    {
      "page": "plot.SDALGCP2_ST_pred",
      "title": "Map a spatio-temporal prediction for one time",
      "topics": [
        "plot.SDALGCP2_ST_pred"
      ]
    },
    {
      "page": "precompute_corr",
      "title": "Precompute aggregated region-level correlation matrices",
      "topics": [
        "precompute_corr"
      ]
    },
    {
      "page": "predict.sdalgcp",
      "title": "Predict relative risk from an sdalgcp fit",
      "topics": [
        "predict.sdalgcp"
      ]
    },
    {
      "page": "predict.SDALGCP2",
      "title": "Predict relative risk from a fitted SDALGCP2 model",
      "topics": [
        "predict.SDALGCP2"
      ]
    },
    {
      "page": "predict.SDALGCP2_ST",
      "title": "Discrete (region x time) prediction for a spatio-temporal fit",
      "topics": [
        "predict.SDALGCP2_ST"
      ]
    },
    {
      "page": "print.SDALGCP2",
      "title": "Print an SDALGCP2 fit",
      "topics": [
        "print.SDALGCP2"
      ]
    },
    {
      "page": "print.summary.SDALGCP2",
      "title": "Print a summary of an SDALGCP2 fit",
      "topics": [
        "print.summary.SDALGCP2"
      ]
    },
    {
      "page": "report",
      "title": "One-call panel of post-fit graphics",
      "topics": [
        "report"
      ]
    },
    {
      "page": "sda_points",
      "title": "Generate candidate sampling points inside each region",
      "topics": [
        "sda_points"
      ]
    },
    {
      "page": "sdalgcp",
      "title": "Fit a spatially discrete LGCP model for aggregated counts",
      "topics": [
        "sdalgcp"
      ]
    },
    {
      "page": "sdalgcp_control",
      "title": "Control settings for 'sdalgcp'",
      "topics": [
        "sdalgcp_control"
      ]
    },
    {
      "page": "sdalgcp_data",
      "title": "Simulated aggregated disease-count data",
      "topics": [
        "sdalgcp_data"
      ]
    },
    {
      "page": "SDALGCP2",
      "title": "Fit a spatial SDA-LGCP model",
      "topics": [
        "SDALGCP2"
      ]
    },
    {
      "page": "SDALGCP2_misaligned",
      "title": "Fit an SDA-LGCP with covariates measured on a different support",
      "topics": [
        "SDALGCP2_misaligned"
      ]
    },
    {
      "page": "SDALGCP2_raster",
      "title": "Fit an SDA-LGCP with spatially continuous (raster) covariates",
      "topics": [
        "SDALGCP2_raster"
      ]
    },
    {
      "page": "SDALGCP2_ST",
      "title": "Fit a spatio-temporal SDA-LGCP model (Kronecker-free)",
      "topics": [
        "SDALGCP2_ST"
      ]
    },
    {
      "page": "summary.SDALGCP2",
      "title": "Summary of an SDALGCP2 fit",
      "topics": [
        "summary.SDALGCP2"
      ]
    }
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        "The data",
        "Fit",
        "Map the two relative risks",
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        "Model checking",
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      "headings": [
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        "When does it matter?",
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        "Options"
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      "created": "2026-06-16 20:17:13",
      "modified": "2026-06-26 16:36:45",
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        "The data",
        "Fit",
        "Predict and map — pick a year and a quantity",
        "Covariates and confounding",
        "Tips"
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      "created": "2026-06-16 20:17:13",
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        "They agree — and continuous gives a standard error",
        "Which to use?"
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        "Restricted spatial regression (RSR)",
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        "Practical guidance",
        "References"
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      "created": "2026-06-16 21:18:40",
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