GPvecchia: 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) <arXiv:1708.06302>. Package also contains a fast implementation of the incomplete Cholesky decomposition (IC0), based on Schaefer et al. (2019) <arXiv:1706.02205> and MaxMin ordering proposed in Guinness (2018) <arXiv:1609.05372>.

Version: 0.1.3
Imports: Rcpp (≥ 0.12.16), methods, stats, sparseinv, fields, Matrix (≥ 1.2.14), parallel, GpGp, FNN
LinkingTo: Rcpp, RcppArmadillo, BH
Suggests: mvtnorm, knitr, rmarkdown, testthat
Published: 2020-04-22
Author: Matthias Katzfuss [aut], Marcin Jurek [aut, cre], Daniel Zilber [aut], Wenlong Gong [aut], Joe Guinness [ctb], Jingjie Zhang [ctb], Florian Schaefer [ctb]
Maintainer: Marcin Jurek <marcinjurek1988 at gmail.com>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
Materials: README NEWS
CRAN checks: GPvecchia results

Downloads:

Reference manual: GPvecchia.pdf
Vignettes: GPvecchia
Package source: GPvecchia_0.1.3.tar.gz
Windows binaries: r-prerelease: GPvecchia_0.1.2.zip, r-release: GPvecchia_0.1.2.zip, r-oldrel: GPvecchia_0.1.2.zip
macOS binaries: r-prerelease: not available, r-release: GPvecchia_0.1.3.tgz, r-oldrel: not available
Old sources: GPvecchia archive

Linking:

Please use the canonical form https://CRAN.R-project.org/package=GPvecchia to link to this page.