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

Documentation:

Reference manual:	GPvecchia.pdf
Vignettes:	GPvecchia

Downloads:

Package source:	GPvecchia_0.1.3.tar.gz
Windows binaries:	r-devel: GPvecchia_0.1.3.zip, r-release: GPvecchia_0.1.3.zip, r-oldrel: GPvecchia_0.1.3.zip
macOS binaries:	r-release (arm64): GPvecchia_0.1.3.tgz, r-oldrel (arm64): GPvecchia_0.1.3.tgz, r-release (x86_64): GPvecchia_0.1.3.tgz, r-oldrel (x86_64): GPvecchia_0.1.3.tgz
Old sources:	GPvecchia archive

Reverse dependencies:

Reverse depends:

GeoModels

Linking:

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