{
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  "Package": "FRK",
  "Type": "Package",
  "Title": "Fixed Rank Kriging",
  "Version": "2.3.2",
  "Date": "2026-01-15",
  "Authors@R": "c(\nperson(\"Andrew\", \"Zammit-Mangion\", , \"andrewzm@gmail.com\", c(\"aut\", \"cre\")),\nperson(\"Matthew\", \"Sainsbury-Dale\", , \"msdale@uow.edu.au\", c(\"aut\")))",
  "Maintainer": "Andrew Zammit-Mangion <andrewzm@gmail.com>",
  "VignetteBuilder": "knitr",
  "Description": "A tool for spatial/spatio-temporal modelling and\nprediction with large datasets. The approach models the field,\nand hence the covariance function, using a set of basis\nfunctions. This fixed-rank basis-function representation\nfacilitates the modelling of big data, and the method naturally\nallows for non-stationary, anisotropic covariance functions.\nDiscretisation of the spatial domain into so-called basic areal\nunits (BAUs) facilitates the use of observations with varying\nsupport (i.e., both point-referenced and areal supports,\npotentially simultaneously), and prediction over arbitrary\nuser-specified regions. `FRK` also supports inference over\nvarious manifolds, including the 2D plane and 3D sphere, and it\nprovides helper functions to model, fit, predict, and plot with\nrelative ease. Version 2.0.0 and above also supports the\nmodelling of non-Gaussian data (e.g., Poisson, binomial,\nnegative-binomial, gamma, and inverse-Gaussian) by employing a\ngeneralised linear mixed model (GLMM) framework. Zammit-Mangion\nand Cressie <doi:10.18637/jss.v098.i04> describe `FRK` in a\nGaussian setting, and detail its use of basis functions and\nBAUs, while Sainsbury-Dale, Zammit-Mangion, and Cressie\n<doi:10.18637/jss.v108.i10> describe `FRK` in a non-Gaussian\nsetting; two vignettes are available that summarise these\npapers and provide additional examples.",
  "URL": "https://andrewzm.github.io/FRK/, https://github.com/andrewzm/FRK/",
  "BugReports": "https://github.com/andrewzm/FRK/issues/",
  "Additional_repositories": "https://andrewzm.github.io/dggrids-repo/",
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  "Repository": "https://andrewzm.r-universe.dev",
  "Date/Publication": "2026-01-15 02:06:07 UTC",
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    "df_to_SpatialPolygons",
    "distance",
    "distR",
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    "eval_basis",
    "fitted",
    "FRK",
    "gc_dist",
    "gc_dist_time",
    "info_fit",
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    "logLik",
    "manifold",
    "measure",
    "nbasis",
    "nobs",
    "nres",
    "observed_BAUs",
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    "plane",
    "plot",
    "plot_spatial_or_ST",
    "predict",
    "radial_basis",
    "real_line",
    "remove_basis",
    "residuals",
    "show_basis",
    "simulate",
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    "SRE",
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    "SRE.predict",
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    "TensorP",
    "type",
    "unobserved_BAUs"
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      "title": "AIRS data for May 2003",
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      "class": [
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      "object": "Am_data",
      "class": [
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        "Am"
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      "table": true,
      "tojson": true
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        "res",
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      "table": true,
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        "y",
        "z",
        "z_unthresholded"
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      "table": true,
      "tojson": true
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      "class": [
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      "object": "worldmap",
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      ],
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        "lat",
        "group",
        "order",
        "region",
        "subregion"
      ],
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      "title": "AIRS data for May 2003",
      "topics": [
        "AIRS_05_2003"
      ]
    },
    {
      "page": "Am_data",
      "title": "Americium soil data",
      "topics": [
        "Am_data"
      ]
    },
    {
      "page": "auto_basis",
      "title": "Automatic basis-function placement",
      "topics": [
        "auto_basis"
      ]
    },
    {
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      "title": "Automatic BAU generation",
      "topics": [
        "auto_BAUs"
      ]
    },
    {
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      "title": "Generic basis-function constructor",
      "topics": [
        "Basis"
      ]
    },
    {
      "page": "Basis-class",
      "title": "Basis functions",
      "topics": [
        "Basis-class",
        "Basis_obj-class",
        "TensorP_Basis-class"
      ]
    },
    {
      "page": "BAUs_from_points",
      "title": "Creates pixels around points",
      "topics": [
        "BAUs_from_points",
        "BAUs_from_points,SpatialPoints-method",
        "BAUs_from_points,ST-method"
      ]
    },
    {
      "page": "coef_uncertainty",
      "title": "Uncertainty quantification of the fixed effects",
      "topics": [
        "coef_uncertainty"
      ]
    },
    {
      "page": "combine_basis",
      "title": "Combine basis functions",
      "topics": [
        "combine_basis",
        "combine_basis,Basis-method",
        "combine_basis,list-method"
      ]
    },
    {
      "page": "Basis_data.frame",
      "title": "Basis-function data frame object",
      "topics": [
        "$,Basis-method",
        "$<-,Basis-method",
        "as.data.frame.Basis",
        "as.data.frame.TensorP_Basis",
        "data.frame<-",
        "data.frame<-,Basis-method",
        "data.frame<-,TensorP_Basis-method",
        "data.frame_Basis,Basis-method"
      ]
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    {
      "page": "df_to_SpatialPolygons",
      "title": "Convert data frame to SpatialPolygons",
      "topics": [
        "df_to_SpatialPolygons"
      ]
    },
    {
      "page": "dist-matrix",
      "title": "Distance Matrix Computation from Two Matrices",
      "topics": [
        "dist-matrix",
        "distR"
      ]
    },
    {
      "page": "distance",
      "title": "Compute distance",
      "topics": [
        "distance",
        "distance,manifold-method",
        "distance,measure-method"
      ]
    },
    {
      "page": "distances",
      "title": "Pre-configured distances",
      "topics": [
        "distances",
        "Euclid_dist",
        "gc_dist",
        "gc_dist_time",
        "measure"
      ]
    },
    {
      "page": "draw_world",
      "title": "Draw a map of the world with country boundaries.",
      "topics": [
        "draw_world"
      ]
    },
    {
      "page": "eval_basis",
      "title": "Evaluate basis functions",
      "topics": [
        "eval_basis",
        "eval_basis,Basis,matrix-method",
        "eval_basis,Basis,SpatialPointsDataFrame-method",
        "eval_basis,Basis,SpatialPolygonsDataFrame-method",
        "eval_basis,Basis,STIDF-method",
        "eval_basis,Basis-matrix-method",
        "eval_basis,Basis-SpatialPointsDataFrame-method",
        "eval_basis,Basis-SpatialPolygonsDataFrame-method",
        "eval_basis,Basis-STIDF-method",
        "eval_basis,TensorP_Basis,matrix-method",
        "eval_basis,TensorP_Basis,STFDF-method",
        "eval_basis,TensorP_Basis,STIDF-method",
        "eval_basis,TensorP_Basis-matrix-method",
        "eval_basis,TensorP_Basis-STFDF-method",
        "eval_basis,TensorP_Basis-STIDF-method"
      ]
    },
    {
      "page": "SRE",
      "title": "Construct SRE object, fit and predict",
      "topics": [
        "AIC,SRE-method",
        "BIC,SRE-method",
        "coef,SRE-method",
        "coef_uncertainty,SRE-method",
        "fitted,SRE-method",
        "FRK",
        "logLik,SRE-method",
        "nobs,SRE-method",
        "predict,SRE-method",
        "residuals,SRE-method",
        "simulate",
        "SRE",
        "SRE.fit"
      ]
    },
    {
      "page": "info_fit",
      "title": "Retrieve fit information for SRE model",
      "topics": [
        "info_fit",
        "info_fit,SRE-method"
      ]
    },
    {
      "page": "initialize-manifold-method",
      "title": "manifold",
      "topics": [
        "initialize,manifold-method"
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