Package: GPvecchia 0.1.8

Marcin Jurek

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) <doi:10.48550/arXiv.1708.06302>. Package also contains a fast implementation of the incomplete Cholesky decomposition (IC0), based on Schaefer et al. (2019) <doi:10.48550/arXiv.1706.02205> and MaxMin ordering proposed in Guinness (2018) <doi:10.48550/arXiv.1609.05372>.

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

GPvecchia_0.1.8.tar.gz
GPvecchia_0.1.8.tar.gz(r-4.7-arm64)GPvecchia_0.1.8.tar.gz(r-4.7-x86_64)GPvecchia_0.1.8.tar.gz(r-4.6-arm64)GPvecchia_0.1.8.tar.gz(r-4.6-x86_64)
GPvecchia_0.1.8.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
GPvecchia/json (API)
NEWS

# Install 'GPvecchia' in R:
install.packages('GPvecchia', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))
Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

openblascppopenmp

4.88 score 4 packages 125 scripts 434 downloads 25 exports 14 dependencies

Last updated from:438f274c21. Checks:6 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-arm64OK199
linux-devel-x86_64OK177
source / vignettesOK279
linux-release-arm64OK171
linux-release-x86_64OK179
wasm-releaseOK162

Exports:calculate_posterior_VLcreateLcreateUgetMatCovgetMatCovFromFactorCppic0icholMaternFunorder_coordinateorder_dist_to_pointorder_maxmin_exactorder_maxmin_exact_obs_predorder_middleoutorder_outsideinSelInvV2covmatvecchia_estimatevecchia_laplace_likelihoodvecchia_laplace_likelihood_from_posteriorvecchia_laplace_predictionvecchia_likelihoodvecchia_lincombvecchia_predvecchia_predictionvecchia_specify

Dependencies:BHdotCall64fieldsFNNGpGplatticemapsMatrixRColorBrewerRcppRcppArmadillospamsparseinvviridisLite

GPvecchia tutorial

Marcin Jurek, Daniel Zilber, Matthias Katzfuss

Rendered fromGPvecchia_vignette.Rmdusingknitr::rmarkdownon May 31 2026.

Last update: 2020-04-14
Started: 2019-10-02

Readme and manuals

Help Manual

Help pageTopics
Vecchia Laplace extension of GPVecchia for non-Gaussian datacalculate_posterior_VL
create the sparse triangular L matrix for specific parameterscreateL
create the sparse triangular U matrix for specific parameterscreateU
extract the required elements from the covariance matrixgetMatCov
Calculate the covariance values required by HV for matrix factors passed as sparse matricesgetMatCovFromFactorCpp
GPvecchia: fast, scalable Gaussian process approximationsGPvecchia-package GPvecchia
Incomplete Cholesky decomposition of a sparse matrix passed in the compressed sparse row formatic0
Wrapper for incomplete Cholesky decompositionichol
Calculate Matern covariance functionMaternFun
Sorted coordinate orderingorder_coordinate
Distance to specified point orderingorder_dist_to_point
Maximum minimum distance orderingorder_maxmin_exact
Maximum minimum distance ordering for predictionorder_maxmin_exact_obs_pred
Middle-out orderingorder_middleout
Outside-in orderingorder_outsidein
selected inverse of a sparse matrixSelInv
compute covariance matrix from V.ord Do not run this function for large n or n.p!!!V2covmat
estimate mean and covariance parameters of a Matern covariance function using Vecchiavecchia_estimate
Wrapper for VL version of vecchia_likelihoodvecchia_laplace_likelihood
Wrapper for VL version of vecchia_likelihoodvecchia_laplace_likelihood_from_posterior
Wrapper for VL version of vecchia_predictionvecchia_laplace_prediction
evaluation of the likelihoodvecchia_likelihood
linear combination of predictions compute the distribution of a linear combination Hyvecchia_lincomb
make spatial predictions using Vecchia based on estimated parametersvecchia_pred
Vecchia predictionvecchia_prediction
specify a general vecchia approximationvecchia_specify