Gaussian process regression (GPR) is a non-parametric kernel-based machine learning method. GPR is based on Bayesian formalism, which enables the estimation of prediction uncertainty of the response variables. We propose an R package that provides an easy-to-use interface for multivariate GPR. The mgpr package was originally developed for remote sensing-based forest inventories that require multivariate prediction of forest attributes. The mgpr package supports both univariate and multivariate responses using a separable kernel and includes a robust hyperparameter estimation algorithm. The mgpr package is suitable for various regression problems with single response or multiple responses and provides good prediction performance.
CITATION STYLE
Varvia, P., Räty, J., & Packalen, P. (2023). mgpr: An R package for multivariate Gaussian process regression. SoftwareX, 24. https://doi.org/10.1016/j.softx.2023.101563
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