R: Fast Bayesian Gaussian Process Regression Fitting

# Fast Bayesian Gaussian Process Regression Fitting

## Documentation for package ‘BayesGPfit’ version 1.1.0

- DESCRIPTION file.

## Help Pages

GP.Bayes.fit | Regular Bayesian fitting of Gaussian process regression on regular grid points with the modified exponential sqaured kernel.
GP.create.cols | Create 256 colors gradually transitioning from Blue to Yellow to Red.
GP.eigen.funcs.fast | Compute eigen functions
GP.eigen.funcs.fast.orth | Create orthogonal eigen functions
GP.eigen.value | Compute eigen values for the standard modified exponential squared correlation kernel.
GP.fast.Bayes.fit | Fast Bayesian fitting of Gaussian process
GP.generate.grids | Create spatial grids.
GP.plot.curve | Graphical representation of one, two, three-dimensional curves
GP.plot.curves | Graphical representation of multiple curves in one and two-dimensional curves
GP.predict | Gaussian process predictions
GP.simulate.curve.fast | Simulate curve on d-dimensional Euclidean space based on Gaussian processes via modified exponential squared kernel.
GP.simulate.curves.fast | Simulate multiple curves on d-dimensional Euclidean space based on Gaussian processes via modified exponential squared kernel.
GP.std.grids | Compute the standardized grids
GP.summary | Summary of posterior inference on the Bayesian Gaussian process regression model