# Tools for Gaussian Process Modeling in Uncertainty Quantification

## Documentation for package ‘GPBayes’ version 0.1.0-6

- DESCRIPTION file
- Package NEWS

## Help Pages

GPBayes-package | Tools for Gaussian Stochastic Process Modeling in Uncertainty Quantification
BesselK | Modified Bessel function of the second kind
cauchy | The generalized Cauchy correlation function
CH | The Confluent Hypergeometric correlation function proposed by Ma and Bhadra (2023)
cor.to.par | Find the correlation parameter given effective range
deriv_kernel | A wraper to construct the derivative of correlation matrix with respect to correlation parameters
distance | Compute distances for two sets of inputs
GaSP | Building, fitting, predicting for a GaSP model
gp | Construct the 'S4' object gp
gp-class | The 'gp' class
gp.condsim | Perform conditional simulation from a Gaussian process
gp.fisher | Fisher information matrix
gp.get.mcmc | get posterior summary for MCMC samples
gp.mcmc | A wraper to fit a Gaussian stochastic process model with MCMC algorithms
gp.model.adequacy | Model assessment based on Deviance information criterion (DIC), logarithmic pointwise predictive density (lppd), and logarithmic joint predictive density (ljpd).
gp.optim | A wraper to fit a Gaussian stochastic process model with optimization methods
gp.predict | Prediction at new inputs based on a Gaussian stochastic process model
gp.sim | Simulate from a Gaussian stochastic process model
HypergU | Confluent hypergeometric function of the second kind
ikernel | A wraper to build different kinds of correlation matrices between two sets of inputs
kernel | A wraper to build different kinds of correlation matrices with distance as arguments
loglik | A wraper to compute the natural logarithm of the integrated likelihood function
matern | The Matérn correlation function proposed by Matérn (1960)
powexp | The powered-exponential correlation function
show,gp-methods | Print the information an object of the 'gp' class
show-method | Print the information an object of the 'gp' class