GpGp: Fast Gaussian Process Computation Using Vecchia's Approximation

Functions for fitting and doing predictions with Gaussian process models using Vecchia's (1988) approximation. Package also includes functions for reordering input locations, finding ordered nearest neighbors (with help from 'FNN' package), grouping operations, and conditional simulations. Covariance functions for spatial and spatial-temporal data on Euclidean domains and spheres are provided. The original approximation is due to Vecchia (1988) <http://www.jstor.org/stable/2345768>, and the reordering and grouping methods are from Guinness (2018) <doi:10.1080/00401706.2018.1437476>. Model fitting employs a Fisher scoring algorithm described in Guinness (2019) <arXiv:1905.08374>.

Version: 0.2.2
Depends: R (≥ 2.10)
Imports: Rcpp (≥ 0.12.13), FNN
LinkingTo: Rcpp, RcppArmadillo
Suggests: fields, knitr, rmarkdown, testthat, maps, maptools
Published: 2020-07-12
Author: Joseph Guinness [aut, cre], Matthias Katzfuss [aut]
Maintainer: Joseph Guinness <joeguinness at gmail.com>
License: MIT + file LICENSE
NeedsCompilation: yes
Materials: README NEWS
CRAN checks: GpGp results

Downloads:

Reference manual: GpGp.pdf
Package source: GpGp_0.2.2.tar.gz
Windows binaries: r-devel: GpGp_0.2.2.zip, r-release: GpGp_0.2.2.zip, r-oldrel: GpGp_0.2.2.zip
macOS binaries: r-release: GpGp_0.2.2.tgz, r-oldrel: GpGp_0.2.2.tgz
Old sources: GpGp archive

Reverse dependencies:

Reverse imports: GPvecchia

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