Bayesian Treed Gaussian Process Models

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Documentation for package ‘tgp’ version 2.4-22

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tgp-package The Treed Gaussian Process Model Package
bcart Bayesian Nonparametric & Nonstationary Regression Models
bgp Bayesian Nonparametric & Nonstationary Regression Models
bgpllm Bayesian Nonparametric & Nonstationary Regression Models
blm Bayesian Nonparametric & Nonstationary Regression Models
btgp Bayesian Nonparametric & Nonstationary Regression Models
btgpllm Bayesian Nonparametric & Nonstationary Regression Models
btlm Bayesian Nonparametric & Nonstationary Regression Models
default.itemps Default Sigmoidal, Harmonic and Geometric Temperature Ladders
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
fried.bool First Friedman Dataset and a variation
friedman.1.data First Friedman Dataset and a variation
hist2bar Functions to plot summary information about the sampled inverse temperatures, tree heights, etc., stored in the traces of a "tgp"-class object
interp.loess Lowess 2-d interpolation onto a uniform grid
itemps.barplot Functions to plot summary information about the sampled inverse temperatures, tree heights, etc., stored in the traces of a "tgp"-class object
lhs Latin Hypercube sampling
mapT Plot the MAP partition, or add one to an existing plot
optim.ptgpf Surrogate-based optimization of noisy black-box function
optim.step.tgp Surrogate-based optimization of noisy black-box function
partition Partition data according to the MAP tree
plot.tgp Plotting for Treed Gaussian Process Models
predict.tgp Predict method for Treed Gaussian process models
sens Monte Carlo Bayesian Sensitivity Analysis
tgp.default.params Default Treed Gaussian Process Model Parameters
tgp.design Sequential Treed D-Optimal Design for Treed Gaussian Process Models
tgp.trees Plot the MAP Tree for each height encountered by the Markov Chain