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    "gradfuncarray",
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    "k_Matern52",
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    "kernel_exponential_dC",
    "kernel_gauss_dC",
    "kernel_latentFactor_dC",
    "kernel_matern32_dC",
    "kernel_matern52_dC",
    "kernel_orderedFactor_dC",
    "kernel_product",
    "kernel_sum",
    "LatentFactorKernel",
    "Matern32",
    "Matern52",
    "OrderedFactorKernel",
    "Periodic",
    "PowerExp",
    "RatQuad",
    "sqrt_matrix",
    "trend_0",
    "trend_c",
    "trend_LM",
    "Triangle",
    "White"
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      "title": "Kernel product",
      "topics": [
        "*.GauPro_kernel"
      ]
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      "title": "Kernel sum",
      "topics": [
        "+.GauPro_kernel"
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    },
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      "topics": [
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      "title": "Correlation Cubic matrix in C (symmetric)",
      "topics": [
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    },
    {
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    },
    {
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      "title": "Correlation Gaussian matrix in C (symmetric)",
      "topics": [
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    },
    {
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      "title": "Correlation Gaussian matrix in C using Rcpp",
      "topics": [
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      "topics": [
        "GauPro"
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    {
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      "topics": [
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    },
    {
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      "title": "Corr Gauss GP using inherited optim",
      "topics": [
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    },
    {
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      "title": "Kernel R6 class",
      "topics": [
        "GauPro_kernel"
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    },
    {
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    },
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      "title": "Gaussian process model with kernel",
      "topics": [
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    {
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      "title": "Corr Gauss GP using inherited optim",
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    {
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    },
    {
      "page": "Gaussian",
      "title": "Gaussian Kernel R6 class",
      "topics": [
        "Gaussian",
        "k_Gaussian"
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    {
      "page": "Gaussian_devianceC",
      "title": "Calculate the Gaussian deviance in C",
      "topics": [
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    },
    {
      "page": "Gaussian_hessianC",
      "title": "Calculate Hessian for a GP with Gaussian correlation",
      "topics": [
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      "topics": [
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    {
      "page": "Gaussian_hessianR",
      "title": "Calculate Hessian for a GP with Gaussian correlation",
      "topics": [
        "Gaussian_hessianR"
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    },
    {
      "page": "GowerFactorKernel",
      "title": "Gower factor Kernel R6 class",
      "topics": [
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        "k_GowerFactorKernel"
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    },
    {
      "page": "gpkm",
      "title": "Gaussian process regression model",
      "topics": [
        "gpkm"
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    },
    {
      "page": "gradfuncarray",
      "title": "Calculate gradfunc in optimization to speed up. NEEDS TO APERM dC_dparams Doesn't need to be exported, should only be useful in functions.",
      "topics": [
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    },
    {
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      "topics": [
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    {
      "page": "IgnoreIndsKernel",
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      "topics": [
        "IgnoreIndsKernel",
        "k_IgnoreIndsKernel"
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    },
    {
      "page": "kernel_cubic_dC",
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    {
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      "topics": [
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    },
    {
      "page": "kernel_gauss_dC",
      "title": "Derivative of Gaussian kernel covariance matrix in C",
      "topics": [
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      "topics": [
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    },
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      "page": "kernel_matern32_dC",
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      "topics": [
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      "page": "kernel_matern52_dC",
      "title": "Derivative of Matern 5/2 kernel covariance matrix in C",
      "topics": [
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    },
    {
      "page": "kernel_orderedFactor_dC",
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      "topics": [
        "kernel_orderedFactor_dC"
      ]
    },
    {
      "page": "kernel_product",
      "title": "Gaussian Kernel R6 class",
      "topics": [
        "kernel_product"
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    },
    {
      "page": "kernel_sum",
      "title": "Gaussian Kernel R6 class",
      "topics": [
        "kernel_sum"
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    },
    {
      "page": "LatentFactorKernel",
      "title": "Latent Factor Kernel R6 class",
      "topics": [
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        "LatentFactorKernel"
      ]
    },
    {
      "page": "Matern32",
      "title": "Matern 3/2 Kernel R6 class",
      "topics": [
        "k_Matern32",
        "Matern32"
      ]
    },
    {
      "page": "Matern52",
      "title": "Matern 5/2 Kernel R6 class",
      "topics": [
        "k_Matern52",
        "Matern52"
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    },
    {
      "page": "OrderedFactorKernel",
      "title": "Ordered Factor Kernel R6 class",
      "topics": [
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