GPTreeO: Dividing Local Gaussian Processes for Online Learning Regression

We implement and extend the Dividing Local Gaussian Process algorithm by Lederer et al. (2020) <doi:10.48550/arXiv.2006.09446>. Its main use case is in online learning where it is used to train a network of local GPs (referred to as tree) by cleverly partitioning the input space. In contrast to a single GP, 'GPTreeO' is able to deal with larger amounts of data. The package includes methods to create the tree and set its parameter, incorporating data points from a data stream as well as making joint predictions based on all relevant local GPs.

Version:	1.0.0
Imports:	R6, hash, DiceKriging, mlegp
Suggests:	knitr, rmarkdown, spelling, testthat
Published:	2024-09-23
DOI:	10.32614/CRAN.package.GPTreeO
Author:	Timo Braun [aut, cre], Anders Kvellestad [aut], Riccardo De Bin [ctb]
Maintainer:	Timo Braun <gptreeo.timo.braun at gmail.com>
License:	MIT + file LICENSE
NeedsCompilation:	no
Language:	en-US
Materials:	NEWS
CRAN checks:	GPTreeO results

Documentation:

Reference manual:	GPTreeO.pdf
Vignettes:	GPTreeO-Vignette (source, R code)

Downloads:

Package source:	GPTreeO_1.0.0.tar.gz
Windows binaries:	r-devel: GPTreeO_1.0.0.zip, r-release: GPTreeO_1.0.0.zip, r-oldrel: GPTreeO_1.0.0.zip
macOS binaries:	r-release (arm64): GPTreeO_1.0.0.tgz, r-oldrel (arm64): GPTreeO_1.0.0.tgz, r-release (x86_64): GPTreeO_1.0.0.tgz, r-oldrel (x86_64): GPTreeO_1.0.0.tgz

Linking:

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