tgp-package |
The Treed Gaussian Process Model Package |
bgp |
One of Six Bayesian Nonparametric & Nonstationary Regression Models |
bgpllm |
One of Six Bayesian Nonparametric & Nonstationary Regression Models |
blm |
One of Six Bayesian Nonparametric & Nonstationary Regression Models |
btgp |
One of Six Bayesian Nonparametric & Nonstationary Regression Models |
btgpllm |
One of Six Bayesian Nonparametric & Nonstationary Regression Models |
btlm |
One of Six Bayesian Nonparametric & Nonstationary Regression Models |
dopt.gp |
Sequential D-Optimal Design for a Stationary Gaussian Process |
exp2d |
2-d Exponential Data |
exp2d.rand |
Random 2-d Exponential Data |
exp2d.Z |
Random Z-values for 2-d Exponential Data |
friedman.1.data |
First Friedman Dataset |
interp.loess |
Lowess 2-d interpolation onto a uniform grid |
lhs |
Latin Hypercube sampling |
mapT |
Plot the MAP partition, or add one to an existing plot |
partition |
Partition data according to the MAP tree |
plot.tgp |
Plotting for Treed Gaussian Process Models |
tgp |
Generic interface to treed Gaussian process models |
tgp.default.params |
Default Treed Gaussian Process Model Parameters |
tgp.design |
Sequential Treed D-Optimal Design for Treed Gaussian Process Models |
tgp.get.partitions |
Get partition of data from maximum a' posteriori tree |
tgp.trees |
Plot the MAP Tree for each height encountered by the Markov Chain |