Dividing Local Gaussian Processes for Online Learning Regression R package GPTreeO version 1.0.1 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. Software Imports: R6, hash, DiceKriging, mlegp Suggests: knitr, rmarkdown, spelling, testthat Timo Braun <gptreeo.timo.braun@gmail.com> Comprehensive R Archive Network (CRAN) Timo Braun [aut, cre] (ORCID: <https://orcid.org/0009-0001-0965-8285>), Anders Kvellestad [aut] (ORCID: <https://orcid.org/0000-0002-5267-7705>), Riccardo De Bin [ctb] (ORCID: <https://orcid.org/0000-0002-7441-6880>) MIT + file LICENSE (https://CRAN.R-project.org/package=GPTreeO/LICENSE) 2024-10-16 application/tgz https://CRAN.R-project.org/package=GPTreeO doi:10.32614/CRAN.package.GPTreeO en-US