Scalable Gaussian-Process Approximations

Fast scalable Gaussian process approximations, particularly well suited to spatial (aerial, remote-sensed) and environmental data, described in more detail in Katzfuss and Guinness (2017) . Package also contains a fast implementation of the incomplete Cholesky decomposition (IC0), based on Schaefer et al. (2019) and MaxMin ordering proposed in Guinness (2018) .


Reference manual

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0.1.3 by Marcin Jurek, a year ago

Browse source code at

Authors: Matthias Katzfuss [aut] , Marcin Jurek [aut, cre] , Daniel Zilber [aut] , Wenlong Gong [aut] , Joe Guinness [ctb] , Jingjie Zhang [ctb] , Florian Schaefer [ctb]

Documentation:   PDF Manual  

GPL (>= 2) license

Imports Rcpp, methods, stats, sparseinv, fields, Matrix, parallel, GpGp, FNN

Suggests mvtnorm, knitr, rmarkdown, testthat

Linking to Rcpp, RcppArmadillo, BH

See at CRAN