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  "Package": "ldmppr",
  "Type": "Package",
  "Title": "Estimate and Simulate from Location Dependent Marked Point\nProcesses",
  "Version": "1.1.3.9000",
  "Authors@R": "c(\nperson(\"Lane\", \"Drew\", role = c(\"aut\", \"cre\", \"cph\"), email = \"lanetdrew@gmail.com\", comment = c(ORCID = \"0009-0006-5427-4092\")),\nperson(\"Andee\", \"Kaplan\", role = \"aut\", comment = c(ORCID = \"0000-0002-2940-889X\")))",
  "Maintainer": "Lane Drew <lanetdrew@gmail.com>",
  "Description": "A suite of tools for estimating, assessing model fit,\nsimulating from, and visualizing location dependent marked\npoint processes characterized by regularity in the pattern. You\nprovide a reference marked point process, a set of raster\nimages containing location specific covariates, and select the\nestimation algorithm and type of mark model. 'ldmppr' estimates\nthe process and mark models and allows you to check the\nappropriateness of the model using a variety of diagnostic\ntools. Once a satisfactory model fit is obtained, you can\nsimulate from the model and visualize the results.\nDocumentation for the package 'ldmppr' is available in the form\nof a vignette.",
  "License": "GPL (>= 3)",
  "Encoding": "UTF-8",
  "LazyData": "true",
  "URL": "https://github.com/lanedrew/ldmppr,\nhttps://lanedrew.github.io/ldmppr/",
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  "Repository": "https://lanedrew.r-universe.dev",
  "Date/Publication": "2026-03-03 18:33:20 UTC",
  "RemoteUrl": "https://github.com/lanedrew/ldmppr",
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  "Packaged": {
    "Date": "2026-09-19 09:59:18 UTC",
    "User": "root"
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  "Author": "Lane Drew [aut, cre, cph] (ORCID:\n<https://orcid.org/0009-0006-5427-4092>),\nAndee Kaplan [aut] (ORCID: <https://orcid.org/0000-0002-2940-889X>)",
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  "_created": "2026-09-19T09:59:18.000Z",
  "_published": "2026-09-19T10:07:15.454Z",
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    "ldmppr_budgets",
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    "ldmppr_mark_model",
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    "predict_marks",
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    "simulate_mpp",
    "simulate_sc",
    "train_mark_model"
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      "title": "Medium Example Data",
      "object": "medium_example_data",
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        "y",
        "size"
      ],
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        "y",
        "size"
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      "tojson": true
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    {
      "page": "check_model_fit",
      "title": "Check the fit of an estimated model using global envelope tests",
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      ]
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    {
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      "title": "Estimate point process parameters using log-likelihood maximization",
      "topics": [
        "estimate_process_parameters"
      ]
    },
    {
      "page": "extract_covars",
      "title": "Extract covariate values from a set of rasters",
      "topics": [
        "extract_covars"
      ]
    },
    {
      "page": "generate_mpp",
      "title": "Generate a marked process given locations and marks",
      "topics": [
        "generate_mpp"
      ]
    },
    {
      "page": "ldmppr_budgets",
      "title": "Create an optimization budget specification for estimate_process_parameters()",
      "topics": [
        "ldmppr_budgets"
      ]
    },
    {
      "page": "ldmppr_budgets-class",
      "title": "Optimization budget specification object",
      "topics": [
        "as.data.frame.ldmppr_budgets",
        "as.list.ldmppr_budgets",
        "ldmppr_budgets-class",
        "length.ldmppr_budgets",
        "print.ldmppr_budgets",
        "print.summary.ldmppr_budgets",
        "summary.ldmppr_budgets",
        "[.ldmppr_budgets"
      ]
    },
    {
      "page": "ldmppr_fit",
      "title": "Fitted point-process model object",
      "topics": [
        "as.data.frame.ldmppr_fit",
        "as_nloptr",
        "as_nloptr.ldmppr_fit",
        "coef.ldmppr_fit",
        "ldmppr_fit",
        "logLik.ldmppr_fit",
        "nobs.ldmppr_fit",
        "plot.ldmppr_fit",
        "print.ldmppr_fit",
        "print.summary.ldmppr_fit",
        "summary.ldmppr_fit"
      ]
    },
    {
      "page": "ldmppr_grids",
      "title": "Create a grid schedule for estimate_process_parameters()",
      "topics": [
        "ldmppr_grids"
      ]
    },
    {
      "page": "ldmppr_grids-class",
      "title": "Grid schedule object",
      "topics": [
        "as.data.frame.ldmppr_grids",
        "as.list.ldmppr_grids",
        "ldmppr_grids-class",
        "length.ldmppr_grids",
        "print.ldmppr_grids",
        "print.summary.ldmppr_grids",
        "summary.ldmppr_grids",
        "[.ldmppr_grids"
      ]
    },
    {
      "page": "ldmppr_mark_model",
      "title": "Mark model object",
      "topics": [
        "ldmppr_mark_model",
        "load_mark_model",
        "predict.ldmppr_mark_model",
        "print.ldmppr_mark_model",
        "print.summary.ldmppr_mark_model",
        "save_mark_model",
        "save_mark_model.ldmppr_mark_model",
        "summary.ldmppr_mark_model"
      ]
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    {
      "page": "ldmppr_model_check",
      "title": "Model fit diagnostic object",
      "topics": [
        "ldmppr_model_check",
        "plot.ldmppr_model_check",
        "print.ldmppr_model_check",
        "print.summary.ldmppr_model_check",
        "summary.ldmppr_model_check"
      ]
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    {
      "page": "ldmppr_sim",
      "title": "Simulated marked point process object",
      "topics": [
        "as.data.frame.ldmppr_sim",
        "ldmppr_sim",
        "mpp.ldmppr_sim",
        "nobs.ldmppr_sim",
        "plot.ldmppr_sim",
        "print.ldmppr_sim",
        "print.summary.ldmppr_sim",
        "summary.ldmppr_sim"
      ]
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    {
      "page": "medium_example_data",
      "title": "Medium Example Data",
      "topics": [
        "medium_example_data"
      ]
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    {
      "page": "plot_mpp",
      "title": "Plot a marked point process",
      "topics": [
        "plot_mpp"
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    {
      "page": "power_law_mapping",
      "title": "Gentle decay (power-law) mapping function from sizes to arrival times",
      "topics": [
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    },
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      "page": "predict_marks",
      "title": "Predict values from the mark distribution",
      "topics": [
        "predict_marks"
      ]
    },
    {
      "page": "scale_rasters",
      "title": "Scale a set of rasters",
      "topics": [
        "scale_rasters"
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    {
      "page": "simulate_mpp",
      "title": "Simulate a realization of a location dependent marked point process",
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    {
      "page": "simulate_sc",
      "title": "Simulate from the self-correcting model",
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        "simulate_sc"
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      "title": "Small Example Data",
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      "page": "thin_st_fast",
      "title": "calculates acceptance for thinning mechanism during simulation",
      "topics": [
        "thin_st_fast"
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    {
      "page": "train_mark_model",
      "title": "Train a flexible model for the mark distribution",
      "topics": [
        "train_mark_model"
      ]
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        "Example workflow",
        "Step 1: Estimate the point process parameters",
        "Step 2: Train a mark model",
        "Step 3: Check model fit",
        "Step 4: Simulate from the fitted model",
        "Summary"
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