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tgp (version 2.4-23)

Bayesian Treed Gaussian Process Models

Description

Bayesian nonstationary, semiparametric nonlinear regression and design by treed Gaussian processes (GPs) with jumps to the limiting linear model (LLM). Special cases also implemented include Bayesian linear models, CART, treed linear models, stationary separable and isotropic GPs, and GP single-index models. Provides 1-d and 2-d plotting functions (with projection and slice capabilities) and tree drawing, designed for visualization of tgp-class output. Sensitivity analysis and multi-resolution models are supported. Sequential experimental design and adaptive sampling functions are also provided, including ALM, ALC, and expected improvement. The latter supports derivative-free optimization of noisy black-box functions. For details and tutorials, see Gramacy (2007) and Gramacy & Taddy (2010) .

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Version

Install

install.packages('tgp')

Monthly Downloads

3,403

Version

2.4-23

License

LGPL

Maintainer

Last Published

September 3rd, 2024

Functions in tgp (2.4-23)

mapT

Plot the MAP partition, or add one to an existing plot
partition

Partition data according to the MAP tree
tgp-internal

Internal Treed Gaussian Process Model Functions
tgp-package

The Treed Gaussian Process Model Package
tgp.default.params

Default Treed Gaussian Process Model Parameters
predict.tgp

Predict method for Treed Gaussian process models
plot.tgp

Plotting for Treed Gaussian Process Models
sens

Monte Carlo Bayesian Sensitivity Analysis
optim.tgp

Surrogate-based optimization of noisy black-box function
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
exp2d.Z

Random Z-values for 2-d Exponential Data
default.itemps

Default Sigmoidal, Harmonic and Geometric Temperature Ladders
exp2d.rand

Random 2-d Exponential Data
lhs

Latin Hypercube sampling
interp.loess

Lowess 2-d interpolation onto a uniform grid
exp2d

2-d Exponential Data
btgp

Bayesian Nonparametric & Nonstationary Regression Models
dopt.gp

Sequential D-Optimal Design for a Stationary Gaussian Process
itemps

Functions to plot summary information about the sampled inverse temperatures, tree heights, etc., stored in the traces of a "tgp"-class object
friedman.1.data

First Friedman Dataset and a variation