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  "Type": "Package",
  "Title": "Tools for Gaussian Process Modeling in Uncertainty\nQuantification",
  "Version": "0.1.0-6",
  "Date": "2024-04-24",
  "Authors@R": "c(person(\"Pulong\", \"Ma\", role=c(\"aut\", \"cre\"), email=\"mpulong@gmail.com\"))",
  "Maintainer": "Pulong Ma <mpulong@gmail.com>",
  "Description": "Gaussian processes ('GPs') have been widely used to model\nspatial data, 'spatio'-temporal data, and computer experiments\nin diverse areas of statistics including spatial statistics,\n'spatio'-temporal statistics, uncertainty quantification, and\nmachine learning. This package creates basic tools for fitting\nand prediction based on 'GPs' with spatial data,\n'spatio'-temporal data, and computer experiments. Key\ncharacteristics for this GP tool include: (1) the comprehensive\nimplementation of various covariance functions including the\n'Matérn' family and the Confluent 'Hypergeometric' family with\nisotropic form, tensor form, and automatic relevance\ndetermination form, where the isotropic form is widely used in\nspatial statistics, the tensor form is widely used in design\nand analysis of computer experiments and uncertainty\nquantification, and the automatic relevance determination form\nis widely used in machine learning; (2) implementations via\nMarkov chain Monte Carlo ('MCMC') algorithms and optimization\nalgorithms for GP models with all the implemented covariance\nfunctions. The methods for fitting and prediction are mainly\nimplemented in a Bayesian framework; (3) model evaluation via\nFisher information and predictive metrics such as predictive\nscores; (4) built-in functionality for simulating 'GPs' with\nall the implemented covariance functions; (5) unified\nimplementation to allow easy specification of various 'GPs'.",
  "License": "GPL (>= 2)",
  "Encoding": "UTF-8",
  "BugReports": "https://github.com/pulongma/GPBayes/issues",
  "SystemRequirements": "GNU Scientific Library version >= 2.5",
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  "Packaged": {
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  "Author": "Pulong Ma [aut, cre]",
  "RoxygenNote": "7.3.1",
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  "Repository": "https://pulongma.r-universe.dev",
  "Date/Publication": "2024-04-26 02:33:52 UTC",
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    "ikernel",
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    "loglik",
    "matern",
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    "show"
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      "title": "Tools for Gaussian Stochastic Process Modeling in Uncertainty Quantification",
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        "GPBayes-package"
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    {
      "page": "BesselK",
      "title": "Modified Bessel function of the second kind",
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        "BesselK"
      ]
    },
    {
      "page": "cauchy",
      "title": "The generalized Cauchy correlation function",
      "topics": [
        "cauchy"
      ]
    },
    {
      "page": "CH",
      "title": "The Confluent Hypergeometric correlation function proposed by Ma and Bhadra (2023)",
      "topics": [
        "CH"
      ]
    },
    {
      "page": "cor.to.par",
      "title": "Find the correlation parameter given effective range",
      "topics": [
        "cor.to.par"
      ]
    },
    {
      "page": "deriv_kernel",
      "title": "A wraper to construct the derivative of correlation matrix with respect to correlation parameters",
      "topics": [
        "deriv_kernel"
      ]
    },
    {
      "page": "distance",
      "title": "Compute distances for two sets of inputs",
      "topics": [
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      ]
    },
    {
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      "title": "Building, fitting, predicting for a GaSP model",
      "topics": [
        "GaSP"
      ]
    },
    {
      "page": "gp",
      "title": "Construct the 'S4' object gp",
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        "gp"
      ]
    },
    {
      "page": "gp-class",
      "title": "The 'gp' class",
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      ]
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