benchmark_exact_gp_scaling
                        Benchmark exact-GP scaling
benchmark_sparse_gp     Benchmark sparse FITC against exact GP
                        regression
changepoint_kernel      Changepoint kernels
coef.gaussianprocesses_latent_model
                        Coefficients of a Gaussian-process mean
compare_gp_predictions
                        Compare two GP posterior predictions
                        numerically
evaluate_kernel         Evaluate a kernel specification
evaluate_likelihood     Evaluate a likelihood and its derivatives
evaluate_mean           Evaluate a Gaussian-process mean function
fit_gp                  Fit an exact Gaussian-process regression model
fit_heteroscedastic_gp
                        Fit an approximate heteroscedastic Gaussian
                        process
fit_latent_gp           Fit a latent Gaussian-process model
fit_sparse_gp           Fit a sparse Gaussian process (FITC or VFE)
fit_time_series_gp      Fit a GP to ordered time-series observations
forecast_gp             Forecast or interpolate from a time-aware GP
                        model
forecast_metrics        Uncertainty-aware forecast metrics
gaussianprocesses_model
                        Fitted model objects
gp_calibration_diagnostics
                        Summarize Gaussian-process uncertainty
                        calibration
gp_classification_scores
                        Scores of binary probability forecasts
gp_count_scores         Scores of count forecasts
gp_diagnostics          Comprehensive diagnostics for an exact
                        Gaussian-process model
gp_hyperparameter_uncertainty
                        Uncertainty of estimated hyperparameters
gp_likelihoods          Likelihoods for Gaussian-process models
gp_mean_functions       Gaussian-process mean functions
gp_model_methods        Base R methods for fitted Gaussian-process
                        models
gp_numerical_diagnostics
                        Summarize numerical conditioning of a fitted GP
gp_profile_likelihood   Profile likelihood of one hyperparameter
gp_scores               Proper scoring rules for Gaussian predictions
gp_simulation_scenario
                        Generate standard GP simulation scenarios
gp_time_index           Build an explicit Gaussian-process time index
initialize_spectral_mixture
                        Initialise a spectral-mixture kernel from the
                        data's spectrum
kernel_diagonal         Evaluate only the diagonal of a kernel matrix
kernel_gradient         Derivatives of a covariance matrix with respect
                        to log hyperparameters
kernel_input_gradient   Derivatives of a covariance matrix with respect
                        to the inputs
kernel_linear           Linear covariance kernel
kernel_matern           Matérn covariance kernels
kernel_parameters       Extract kernel parameters
kernel_periodic         Periodic covariance kernel
kernel_rational_quadratic
                        Rational-quadratic covariance kernel
kernel_rbf              Squared-exponential covariance kernel
kernel_white_noise      White-noise covariance kernel
likelihood_predictive   Predictive distribution of new responses
linear_kernel           Create a linear kernel specification
log_marginal_likelihood
                        Gaussian-process log marginal likelihood
log_marginal_likelihood_gradient
                        Gradient of the Gaussian-process log marginal
                        likelihood
loo_gp                  Exact leave-one-out Gaussian-process
                        diagnostics
matern_kernel_specs     Create Matérn kernel specifications
mean_coefficients       Treatments of mean-function coefficients
optimize_gp             Optimize Gaussian-process hyperparameters
optimize_latent_gp      Optimize the hyperparameters of a latent
                        Gaussian-process model
optimize_sparse_gp      Optimize the hyperparameters of a sparse
                        Gaussian process
optimize_time_series_gp
                        Optimize the hyperparameters of a time-series
                        GP
parametric_means        Parametric mean functions
periodic_kernel         Create a periodic kernel specification
predict_gp              Predict from an exact Gaussian-process
                        regression model
predict_gradient_gp     Predict the gradient of the latent function
predict_heteroscedastic_gp
                        Predict from an approximate heteroscedastic GP
predict_latent_gp       Predict from a latent Gaussian-process model
predict_sparse_gp       Predict from a sparse Gaussian-process model
product_kernel          Multiply covariance kernels
rational_quadratic_kernel
                        Create a rational-quadratic kernel
                        specification
rbf_kernel              Create an RBF kernel specification
rolling_origin_gp       Rolling-origin evaluation for time-aware
                        Gaussian processes
run_gp_benchmark_suite
                        Run the standard GP benchmark suite
sample_gp_posterior.gaussianprocesses_latent_model
                        Draw functions from a Gaussian-process
                        posterior
sample_gp_prior         Draw functions from a Gaussian-process prior
scale_kernel            Scale a covariance kernel
select_dimensions       Restrict a kernel to selected input columns
select_inducing_points
                        Select inducing points deterministically
simulate_gp_data        Simulate observations from a Gaussian process
spectral_mixture_kernel
                        Spectral-mixture kernels
sum_kernel              Add covariance kernels
time_series_kernel      Create a trend-periodic-local time-series
                        kernel
update_kernel_parameters
                        Update parameters in a kernel specification
white_noise_kernel      Create a white-noise kernel specification
