Package: BayesGPfit 1.1.0

BayesGPfit: Fast Bayesian Gaussian Process Regression Fitting

Bayesian inferences on nonparametric regression via Gaussian Processes with a modified exponential square kernel using a basis expansion approach.

Authors:Jian Kang [aut, cre], John Burkardt [ctb]

# Install 'BayesGPfit' in R:

install.packages('BayesGPfit', repos = c('https://kangjian2016.r-universe.dev', 'https://cloud.r-project.org'))

Uses libs:

On CRAN:

Last updated 2 years agofrom:8cb69a5b8c. Checks:OK: 1 ERROR: 8. Indexed: yes.

Dependencies:lattice

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