Dividing Local Gaussian Processes for Online Learning Regression

We implement and extend the Dividing Local Gaussian Process algorithm by Lederer et al. (2020) . 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.