Man pages for mattdneal/gaussianProcess
Gaussian Process Fitting and Prediction
| add.kernel | Add a Kernel Class to a Model Tree |
| as.character.ModelTree | Convert a Model Tree to a String |
| bayesian.information.criterion | Calculate BIC of a GP |
| bayesian.information.criterion.EDoF | Calculate BIC of a GP (Effective DoF) |
| bayesian.information.criterion.EDoF.eigen | Calculate BIC of a GP (Effective DoF, eigenvalue... |
| bootify | Bootify a Statistic |
| callKernelByString | Select Built-in C++ Kernels by Name |
| create.ard.kernel | Create an ARD kernel object |
| create.gaussian.process | Create a gaussianProcess object |
| create.kernel.object | Create a kernel object |
| create.kernel.object.from.model.tree | Create Kernel object from model tree |
| create.model.tree | Create an Empty Model Tree |
| create.model.tree.builtin | Create a Model Tree Containing All Built-in Kernels |
| create.model.tree.builtin.scaled | Create a Model Tree Containing All Built-in Kernels (scaled) |
| create.model.tree.from.list | Create a Model Tree from a list of kernel objects |
| create.numeric.grad | Create a numerical gradient function for a kernel using the... |
| create.numeric.hessian | Create a numerical Hessian function for a kernel using the... |
| create.numeric.hessian.from.grad | Create a numerical Hessian function from a kernel grad using... |
| create.rf.additional.params | Create Additional Params List for Random Forest Kernel |
| delete.node | Delete a Node in a Model Tree |
| eval.cov.mat.grad.node | Evaluate Covariance Matrix Grad of a Model Tree Node |
| eval.cov.mat.hess.node | Evaluate Covariance Matrix Hessian of a Model Tree Node |
| eval.cov.mat.node | Evaluate Covariance Matrix of a Model Tree Node |
| eval.node.kstar.mat | K* matrix of a model tree node |
| fit.hyperparams | Fit hyperparameters for a GP using maximum likelihood... |
| gaussianProcess-package | Utilities for training Gaussian processes. |
| get.alpha | Get alpha for a GP |
| get_covariance_matrix | Get the Covariance Matrix for a Kernel |
| get_covariance_matrix.character | Get the Covariance Matrix for a built-in kernel |
| get_covariance_matrix.function | Get the Covariance Matrix for a kernel function |
| get_covariance_matrix_grad.character | Get the Covariance Matrix grad for a built-in kernel |
| get_covariance_matrix_grad.function | Get the Covariance Matrix grad for a kernel function |
| get_covariance_matrix_grad.Kernel | Get the Covariance Matrix Grad for a Kernel |
| get_covariance_matrix_grad.ModelTree | Get the Covariance Matrix Grad for a Kernel ModelTree |
| get_covariance_matrix_hess.character | Get the Covariance Matrix Hessian for a built-in kernel |
| get_covariance_matrix_hess.function | Get the Covariance Matrix Hessian for a kernel function |
| get_covariance_matrix_hess.Kernel | Get the Covariance Matrix Hessian for a Kernel |
| get_covariance_matrix_hess.ModelTree | Get the Covariance Matrix Hessian for a Kernel ModelTree |
| get_covariance_matrix.Kernel | Get the Covariance Matrix for a Kernel |
| get_covariance_matrix.ModelTree | Get the Covariance Matrix for a Kernel ModelTree |
| get.cov.mat.chol | Get the Cholesky decomposition of a covariance matrix |
| get.inst.cov.mats | Get Kernel Instance Covariance Matrices, Grads or Hessians |
| get_kstar_matrix | Get predictive K* matrix |
| get_kstar_matrix.character | Get predictive K* matrix |
| get_kstar_matrix.function | Get predictive K* matrix |
| get_kstar_matrix.Kernel | Get predictive K* matrix |
| get_kstar_matrix.ModelTree | K* Matrix of a Model Tree |
| get.marginal.likelihood | Get Log Marginal Likelihood of a GP |
| get.marginal.likelihood.grad | Get the Grad of the Log Marginal Likelihood of a GP |
| get.marginal.likelihood.hessian | Get the Hessian of the Log Marginal Likelihood of a GP |
| get.normal.prior | Return an object representing a normal prior over... |
| get.uniform.prior | Return an object representing a uniform prior over... |
| insert.kernel.instance | Insert Kernel Instance into Model Tree |
| list_built_in_kernels | Return a list containing built in kernels. |
| list_built_in_kernels_scaled | Return a list of built-in kernels scaled with a constant... |
| list_polynomial_kernels | Create a list of polynomial kernels |
| model.search | Perform Model Selection Using BIC |
| plot.GaussianProcess | Plot a Gaussian Process |
| posterior.laplace.approximation | Calculate Laplace Approximation of a GP |
| predict.GaussianProcess | Create Predictions Using a Gaussian Process |
| print.Kernel | Print a kernel |
| random.forest.partition.function.generator | Random forest kernel |
| random.partition.kernel | Random Partition Kernel |
| rmse | Regression fit metrics |
| sample.functions.from.kernel | Sample a function from a Gaussian Process Prior |
| scale_kernel | Scale a kernel |
| scale_kernel_list | Scale a list of kernel objects |
| summary.ModelTree | Print a Summary of a Model Tree |
| test.kernel.grad | Test an Analytic Gradient Against a Numeric Gradient |
| test.structure.recreation | Test the ability of the BIC search to identify the data... |
