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tgp (version 1.0-1)

Bayesian treed Gaussian process models

Description

Bayesian Nonparametric and nonstationary regression by treed Gaussian processes with jumps to the limiting linear model (LLM). Special cases also implememted include Bayesian linear models, linear CART, stationary separable and isotropic Gaussian process regression. Includes 1d and 2d plotting functions (with higher dimension projection and slice capabilities), and tree drawing, designed for visualization of tgp class output. (2d plotting requires akima; tree plotting requires maptree and combinat.)

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Version

Install

install.packages('tgp')

Monthly Downloads

2,390

Version

1.0-1

License

LGPL (Lesser GNU Public License)

Maintainer

Robert B Gramacy

Last Published

September 3rd, 2024

Functions in tgp (1.0-1)

dopt.gp

Sequential D-Optimal Design for a Stationary Gaussian Process
exp2d

2-d Exponential Data
tgp.get.partitions

Get partition of data from maximum a' posteriori tree
tgp.design

Sequential Treed D-Optimal Design for Treed Gaussian Process Models
btgp

One of Six Bayesian Nonparametric & Nonstationary Regression Models
tgp-internal

Internal Treed Gaussian Process Model Functions
tgp.trees

Plot MAP Treed Gaussian Process Models
tgp-package

The Treed Gaussian Process Model Package
tgp.default.params

Default Treed Gaussian Process Model Parameters
tgp

Generic interface to treed Gaussian process models
exp2d.rand

Randomly subsampled 2-d Exponential Data
plot.tgp

Plotting for Treed Gaussian Process Models
friedman.1.data

First Friedman Dataset