Lightweight Implementation for the Most Common Gaussian Process Models

Implements the most common Gaussian process (GP) models using Laplace and expectation propagation (EP) approximations, maximum marginal likelihood (or posterior) inference for the hyperparameters, and sparse approximations for larger datasets.


Reference manual

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0.11.1 by Juho Piironen, a month ago

Browse source code at

Authors: Juho Piironen [cre, aut]

Documentation:   PDF Manual  

GPL-3 license

Imports Matrix, methods, Rcpp

Suggests testthat, knitr, rmarkdown, ggplot2

Linking to Rcpp, RcppArmadillo

See at CRAN