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  "Package": "BayesNSGP",
  "Title": "Bayesian Analysis of Non-Stationary Gaussian Process Models",
  "Description": "Enables off-the-shelf functionality for fully Bayesian,\nnonstationary Gaussian process modeling. The approach to\nnonstationary modeling involves a closed-form,\nconvolution-based covariance function with spatially-varying\nparameters; these parameter processes can be specified either\ndeterministically (using covariates or basis functions) or\nstochastically (using approximate Gaussian processes).\nStationary Gaussian processes are a special case of our\nmethodology, and we furthermore implement approximate Gaussian\nprocess inference to account for very large spatial data sets\n(Finley, et al (2017) <doi:10.48550/arXiv.1702.00434>).\nBayesian inference is carried out using Markov chain Monte\nCarlo methods via the \"nimble\" package, and posterior\nprediction for the Gaussian process at unobserved locations is\nprovided as a post-processing step. Also provided are\nnearest-neighbor Gaussian process components for use directly\nin user-written model code, where the spatial process is\nretained as a latent field: neighbor-structure construction, a\nlatent-field density and matching simulation function, a\npurpose-built Metropolis-Hastings sampler that updates the\nfield one node at a time, and posterior prediction at\nunobserved locations.",
  "Version": "0.3.1",
  "Date": "2026-09-10",
  "Maintainer": "Daniel Turek <danielturek@gmail.com>",
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  "Author": "Daniel Turek [aut, cre], Mark Risser [aut], Fabian Ketwaroo\n[aut]",
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  "Repository": "https://danielturek.r-universe.dev",
  "Date/Publication": "2026-09-10 14:31:03 UTC",
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    "calcQF",
    "calculateAD_ns",
    "calculateU_ns",
    "computeAD",
    "computeC",
    "computeNeighbors",
    "computeQF",
    "conditionLatentObs",
    "crossCy_sm",
    "Cy_sm",
    "determineNeighbors",
    "displayNeighbors",
    "dmnorm_gp2Scale",
    "dmnorm_NN_GP",
    "dmnorm_nngp",
    "dmnorm_sgv",
    "expcov",
    "get_single_reverse_neighbors",
    "inverseEigen",
    "matern_corr",
    "nimble_sparse_chol",
    "nimble_sparse_cholesky",
    "nimble_sparse_crossprod",
    "nimble_sparse_solve",
    "nimble_sparse_solveMat",
    "nimble_sparse_tcrossprod",
    "NNGP.pred",
    "nsCorr",
    "nsCrosscorr",
    "nsCrossdist",
    "nsCrossdist3d",
    "nsDist",
    "nsDist3d",
    "nsgpModel",
    "nsgpPredict",
    "orderCoordinatesMMD",
    "R_sparse_chol",
    "R_sparse_cholesky",
    "R_sparse_crossprod",
    "R_sparse_solve",
    "R_sparse_solveMat",
    "R_sparse_tcrossprod",
    "rmnorm_gp2Scale",
    "rmnorm_NN_GP",
    "rmnorm_nngp",
    "rmnorm_sgv",
    "RWNNGP_setup",
    "sampler_RW_NN_GP",
    "sgvSetup"
  ],
  "_help": [
    {
      "page": "calcQF",
      "title": "Calculate the Gaussian quadratic form for the NNGP approximation",
      "topics": [
        "calcQF"
      ]
    },
    {
      "page": "calculateAD_ns",
      "title": "Calculate A and D matrices for the NNGP approximation",
      "topics": [
        "calculateAD_ns"
      ]
    },
    {
      "page": "calculateU_ns",
      "title": "Calculate the (sparse) matrix U",
      "topics": [
        "calculateU_ns"
      ]
    },
    {
      "page": "computeAD",
      "title": "Compute NNGP regression coefficients and conditional variances",
      "topics": [
        "computeAD"
      ]
    },
    {
      "page": "computeC",
      "title": "Construct local covariance matrix for NNGP conditional distributions",
      "topics": [
        "computeC"
      ]
    },
    {
      "page": "computeNeighbors",
      "title": "Compute neighbor sets for a Nearest-Neighbor Gaussian Process (NNGP)",
      "topics": [
        "computeNeighbors"
      ]
    },
    {
      "page": "computeQF",
      "title": "Compute quadratic form for NNGP log-likelihood",
      "topics": [
        "computeQF"
      ]
    },
    {
      "page": "conditionLatentObs",
      "title": "Assign conditioning sets for the SGV approximation",
      "topics": [
        "conditionLatentObs"
      ]
    },
    {
      "page": "crossCy_sm",
      "title": "Calculate sparse kernel, core kernel, and determine nonzero entries",
      "topics": [
        "crossCy_sm"
      ]
    },
    {
      "page": "Cy_sm",
      "title": "Calculate sparse kernel, core kernel, and determine nonzero entries",
      "topics": [
        "Cy_sm"
      ]
    },
    {
      "page": "determineNeighbors",
      "title": "Determine the k-nearest neighbors for each spatial coordinate.",
      "topics": [
        "determineNeighbors"
      ]
    },
    {
      "page": "displayNeighbors",
      "title": "Plot neighbor sets for a Nearest-Neighbor Gaussian Process (NNGP)",
      "topics": [
        "displayNeighbors"
      ]
    },
    {
      "page": "dmnorm_gp2Scale",
      "title": "Function for the evaluating the Gaussian likelihood with gp2Scale sparse covariance.",
      "topics": [
        "dmnorm_gp2Scale"
      ]
    },
    {
      "page": "dmnorm_NN_GP",
      "title": "Compute Nearest-Neighbor Gaussian Process (NNGP) log-density",
      "topics": [
        "dmnorm_NN_GP"
      ]
    },
    {
      "page": "dmnorm_nngp",
      "title": "Function for the evaluating the NNGP approximate density.",
      "topics": [
        "dmnorm_nngp"
      ]
    },
    {
      "page": "dmnorm_sgv",
      "title": "Function for the evaluating the SGV approximate density.",
      "topics": [
        "dmnorm_sgv"
      ]
    },
    {
      "page": "expcov",
      "title": "Exponential covariance function for Gaussian processes",
      "topics": [
        "expcov"
      ]
    },
    {
      "page": "get_single_reverse_neighbors",
      "title": "Find reverse neighbors for a single NNGP node",
      "topics": [
        "get_single_reverse_neighbors"
      ]
    },
    {
      "page": "inverseEigen",
      "title": "Calculate covariance elements based on eigendecomposition components",
      "topics": [
        "inverseEigen"
      ]
    },
    {
      "page": "matern_corr",
      "title": "Calculate a stationary Matern correlation matrix",
      "topics": [
        "matern_corr"
      ]
    },
    {
      "page": "nimble_sparse_chol",
      "title": "nimble_sparse_chol",
      "topics": [
        "nimble_sparse_chol"
      ]
    },
    {
      "page": "nimble_sparse_cholesky",
      "title": "nimble_sparse_chol",
      "topics": [
        "nimble_sparse_cholesky"
      ]
    },
    {
      "page": "nimble_sparse_crossprod",
      "title": "nimble_sparse_crossprod",
      "topics": [
        "nimble_sparse_crossprod"
      ]
    },
    {
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      "title": "nimble_sparse_solve",
      "topics": [
        "nimble_sparse_solve"
      ]
    },
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      "title": "nimble_sparse_crossprod",
      "topics": [
        "nimble_sparse_solveMat"
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    },
    {
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      "title": "nimble_sparse_tcrossprod",
      "topics": [
        "nimble_sparse_tcrossprod"
      ]
    },
    {
      "page": "NNGP.pred",
      "title": "Posterior predictive sampling for NNGP",
      "topics": [
        "NNGP.pred"
      ]
    },
    {
      "page": "nsCorr",
      "title": "Calculate a nonstationary Matern correlation matrix",
      "topics": [
        "nsCorr"
      ]
    },
    {
      "page": "nsCrosscorr",
      "title": "Calculate a nonstationary Matern cross-correlation matrix",
      "topics": [
        "nsCrosscorr"
      ]
    },
    {
      "page": "nsCrossdist",
      "title": "Calculate coordinate-specific cross-distance matrices",
      "topics": [
        "nsCrossdist"
      ]
    },
    {
      "page": "nsCrossdist3d",
      "title": "Calculate coordinate-specific cross-distance matrices, only for nearest neighbors and store in an array",
      "topics": [
        "nsCrossdist3d"
      ]
    },
    {
      "page": "nsDist",
      "title": "Calculate coordinate-specific distance matrices",
      "topics": [
        "nsDist"
      ]
    },
    {
      "page": "nsDist3d",
      "title": "Calculate coordinate-specific distance matrices, only for nearest neighbors and store in an array",
      "topics": [
        "nsDist3d"
      ]
    },
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      "page": "nsgpModel",
      "title": "NIMBLE code for a generic nonstationary GP model",
      "topics": [
        "nsgpModel"
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    },
    {
      "page": "nsgpPredict",
      "title": "Posterior prediction for the NSGP",
      "topics": [
        "nsgpPredict"
      ]
    },
    {
      "page": "orderCoordinatesMMD",
      "title": "Order coordinates according to a maximum-minimum distance criterion.",
      "topics": [
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    },
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      "topics": [
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      "page": "R_sparse_crossprod",
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      "page": "R_sparse_tcrossprod",
      "title": "nimble_sparse_tcrossprod",
      "topics": [
        "R_sparse_tcrossprod"
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    },
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      "page": "rmnorm_gp2Scale",
      "title": "Function for the evaluating the SGV approximate density.",
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