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tgp (version 1.1-6)

Bayesian treed Gaussian process models

Description

Bayesian semiparametric 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. Provides 1-d and 2-d plotting functions (with projection and slice capabilities) and tree drawing, designed for visualization of tgp-class output.

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Version

Install

install.packages('tgp')

Monthly Downloads

1,853

Version

1.1-6

License

LGPL (Lesser GNU Public License)

Maintainer

Robert B Gramacy

Last Published

September 3rd, 2024

Functions in tgp (1.1-6)

plot.tgp

Plotting for Treed Gaussian Process Models
partition

Partition data according to the MAP tree
mapT

Plot the MAP partition, or add one to an existing plot
friedman.1.data

First Friedman Dataset
exp2d

2-d Exponential Data
tgp.design

Sequential Treed D-Optimal Design for Treed Gaussian Process Models
interp.loess

Lowess 2-d interpolation onto a uniform grid
exp2d.rand

Randomly subsampled 2-d Exponential Data
tgp.default.params

Default Treed Gaussian Process Model Parameters
tgp-package

The Treed Gaussian Process Model Package
lhs

Latin Hypercube sampling
tgp.get.partitions

Get partition of data from maximum a' posteriori tree
tgp-internal

Internal Treed Gaussian Process Model Functions
tgp.trees

Plot the MAP Tree for each height encountered by the Markov Chain
tgp

Generic interface to treed Gaussian process models
btgp

One of Six Bayesian Nonparametric & Nonstationary Regression Models
dopt.gp

Sequential D-Optimal Design for a Stationary Gaussian Process