GpGp: Fast Gaussian Process Computation Using Vecchia's Approximation

Functions for reordering input locations, finding ordered nearest neighbors (with help from 'FNN' package), grouping operations, approximate likelihood evaluations, profile likelihoods, Gaussian process predictions, 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>.

Version: 0.1.0
Depends: R (≥ 2.10)
Imports: Rcpp (≥ 0.12.13), FNN
LinkingTo: Rcpp
Suggests: fields, knitr, rmarkdown, testthat
Published: 2018-02-19
Author: Joseph Guinness [aut, cre], Matthias Katzfuss [aut]
Maintainer: Joseph Guinness <joeguinness at gmail.com>
License: MIT + file LICENSE
NeedsCompilation: yes
Materials: README
CRAN checks: GpGp results

Downloads:

Reference manual: GpGp.pdf
Vignettes: Analysis of Jason-3 Windspeed Data with GpGp
Package source: GpGp_0.1.0.tar.gz
Windows binaries: r-devel: GpGp_0.1.0.zip, r-release: GpGp_0.1.0.zip, r-oldrel: GpGp_0.1.0.zip
OS X binaries: r-release: GpGp_0.1.0.tgz, r-oldrel: GpGp_0.1.0.tgz

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