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    "sample_gp_prior",
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        "coef.gaussianprocesses_model",
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      "topics": [
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      "title": "Evaluate a kernel specification",
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      "topics": [
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      "title": "Evaluate a Gaussian-process mean function",
      "topics": [
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      "page": "fit_gp",
      "title": "Fit an exact Gaussian-process regression model",
      "topics": [
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    },
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      "page": "fit_heteroscedastic_gp",
      "title": "Fit an approximate heteroscedastic Gaussian process",
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    },
    {
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      "title": "Fit a latent Gaussian-process model",
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    },
    {
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      "title": "Fit a sparse Gaussian process (FITC or VFE)",
      "topics": [
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    },
    {
      "page": "fit_time_series_gp",
      "title": "Fit a GP to ordered time-series observations",
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    },
    {
      "page": "forecast_gp",
      "title": "Forecast or interpolate from a time-aware GP model",
      "topics": [
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      ]
    },
    {
      "page": "forecast_metrics",
      "title": "Uncertainty-aware forecast metrics",
      "topics": [
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    },
    {
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      "title": "Fitted model objects",
      "topics": [
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        "gaussianprocesses_latent_model",
        "gaussianprocesses_model",
        "gaussianprocesses_sparse_model",
        "gaussianprocesses_state_space_model"
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    },
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      "title": "Summarize Gaussian-process uncertainty calibration",
      "topics": [
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    },
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      "title": "Scores of binary probability forecasts",
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      "title": "Comprehensive diagnostics for an exact Gaussian-process model",
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    {
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        "gaussian_likelihood",
        "gp_likelihoods",
        "poisson_likelihood"
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        "gp_mean_functions",
        "zero_mean"
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    {
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      "title": "Base R methods for fitted Gaussian-process models",
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        "fitted.gaussianprocesses_latent_model",
        "fitted.gaussianprocesses_model",
        "fitted.gaussianprocesses_sparse_model",
        "fitted.gaussianprocesses_state_space_model",
        "gp_model_methods",
        "logLik.gaussianprocesses_heteroscedastic_model",
        "logLik.gaussianprocesses_latent_model",
        "logLik.gaussianprocesses_model",
        "logLik.gaussianprocesses_sparse_model",
        "logLik.gaussianprocesses_state_space_model",
        "nobs.gaussianprocesses_heteroscedastic_model",
        "nobs.gaussianprocesses_latent_model",
        "nobs.gaussianprocesses_model",
        "nobs.gaussianprocesses_sparse_model",
        "nobs.gaussianprocesses_state_space_model",
        "predict.gaussianprocesses_heteroscedastic_model",
        "predict.gaussianprocesses_latent_model",
        "predict.gaussianprocesses_model",
        "predict.gaussianprocesses_sparse_model",
        "predict.gaussianprocesses_state_space_model",
        "print.summary.gaussianprocesses_model",
        "residuals.gaussianprocesses_heteroscedastic_model",
        "residuals.gaussianprocesses_model",
        "residuals.gaussianprocesses_sparse_model",
        "residuals.gaussianprocesses_state_space_model",
        "summary.gaussianprocesses_heteroscedastic_model",
        "summary.gaussianprocesses_latent_model",
        "summary.gaussianprocesses_model",
        "summary.gaussianprocesses_sparse_model",
        "summary.gaussianprocesses_state_space_model"
      ]
    },
    {
      "page": "gp_numerical_diagnostics",
      "title": "Summarize numerical conditioning of a fitted GP",
      "topics": [
        "gp_numerical_diagnostics"
      ]
    },
    {
      "page": "gp_profile_likelihood",
      "title": "Profile likelihood of one hyperparameter",
      "topics": [
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    },
    {
      "page": "gp_scores",
      "title": "Proper scoring rules for Gaussian predictions",
      "topics": [
        "gp_holdout_scores",
        "gp_loo_scores",
        "gp_scores"
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    },
    {
      "page": "gp_simulation_scenario",
      "title": "Generate standard GP simulation scenarios",
      "topics": [
        "gp_simulation_scenario"
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    },
    {
      "page": "gp_stack_outputs",
      "title": "Stack several outputs into one input matrix",
      "topics": [
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      ]
    },
    {
      "page": "gp_time_index",
      "title": "Build an explicit Gaussian-process time index",
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        "gp_time_index"
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    },
    {
      "page": "initialize_coregionalization",
      "title": "Initialize a coregionalization kernel from the data",
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    },
    {
      "page": "initialize_spectral_mixture",
      "title": "Initialise a spectral-mixture kernel from the data's spectrum",
      "topics": [
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    },
    {
      "page": "kernel_diagonal",
      "title": "Evaluate only the diagonal of a kernel matrix",
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      ]
    },
    {
      "page": "kernel_gradient",
      "title": "Derivatives of a covariance matrix with respect to log hyperparameters",
      "topics": [
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    },
    {
      "page": "kernel_input_gradient",
      "title": "Derivatives of a covariance matrix with respect to the inputs",
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    {
      "page": "kernel_linear",
      "title": "Linear covariance kernel",
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    },
    {
      "page": "kernel_matern",
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      "topics": [
        "kernel_matern",
        "kernel_matern12",
        "kernel_matern32",
        "kernel_matern52"
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    },
    {
      "page": "kernel_parameters",
      "title": "Extract kernel parameters",
      "topics": [
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      ]
    },
    {
      "page": "kernel_periodic",
      "title": "Periodic covariance kernel",
      "topics": [
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    },
    {
      "page": "kernel_rational_quadratic",
      "title": "Rational-quadratic covariance kernel",
      "topics": [
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    },
    {
      "page": "kernel_rbf",
      "title": "Squared-exponential covariance kernel",
      "topics": [
        "kernel_rbf"
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    },
    {
      "page": "kernel_white_noise",
      "title": "White-noise covariance kernel",
      "topics": [
        "kernel_white_noise"
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    },
    {
      "page": "likelihood_predictive",
      "title": "Predictive distribution of new responses",
      "topics": [
        "likelihood_predictive"
      ]
    },
    {
      "page": "linear_kernel",
      "title": "Create a linear kernel specification",
      "topics": [
        "linear_kernel"
      ]
    },
    {
      "page": "log_marginal_likelihood",
      "title": "Gaussian-process log marginal likelihood",
      "topics": [
        "log_marginal_likelihood"
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    },
    {
      "page": "log_marginal_likelihood_gradient",
      "title": "Gradient of the Gaussian-process log marginal likelihood",
      "topics": [
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    },
    {
      "page": "loo_gp",
      "title": "Exact leave-one-out Gaussian-process diagnostics",
      "topics": [
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    },
    {
      "page": "matern_kernel_specs",
      "title": "Create Matérn kernel specifications",
      "topics": [
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        "matern32_kernel",
        "matern52_kernel",
        "matern_kernel_specs"
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    },
    {
      "page": "mean_coefficients",
      "title": "Treatments of mean-function coefficients",
      "topics": [
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        "estimate_coefficients",
        "mean_coefficients"
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    },
    {
      "page": "optimize_gp",
      "title": "Optimize Gaussian-process hyperparameters",
      "topics": [
        "optimize_gp"
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    },
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      "page": "optimize_latent_gp",
      "title": "Optimize the hyperparameters of a latent Gaussian-process model",
      "topics": [
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      "title": "Optimize the hyperparameters of a sparse Gaussian process",
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      "title": "Optimize the hyperparameters of a time-series GP",
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      "page": "parametric_means",
      "title": "Parametric mean functions",
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        "linear_mean",
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        "polynomial_mean"
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    },
    {
      "page": "periodic_kernel",
      "title": "Create a periodic kernel specification",
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    {
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      "title": "Predict from an exact Gaussian-process regression model",
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    },
    {
      "page": "predict_gradient_gp",
      "title": "Predict the gradient of the latent function",
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