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BayesNSGP
View on CRAN: Click
here
Download and install BayesNSGP package within the R console
Install from CRAN:
install.packages("BayesNSGP")
Install from Github:
library("remotes")
install_github("cran/BayesNSGP")
Install by package version:
library("remotes")
install_version("BayesNSGP", "0.1.2")
Attach the package and use:
library("BayesNSGP")
Maintained by
Daniel Turek
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
10.32614/CRAN.package.BayesNSGP . BayesNSGP results . BayesNSGP.pdf . BayesNSGP_0.1.2.tar.gz . BayesNSGP_0.1.2.zip . BayesNSGP_0.1.2.zip . BayesNSGP_0.1.2.zip . BayesNSGP_0.1.2.tgz . BayesNSGP_0.1.2.tgz . BayesNSGP_0.1.2.tgz . BayesNSGP_0.1.2.tgz . BayesNSGP_0.1.2.tgz . BayesNSGP_0.1.2.tgz . BayesNSGP archive . https://CRAN.R-project.org/package=BayesNSGP .
First Published: 2019-08-29
Latest Update: 2022-01-09
Description:
Enables off-the-shelf functionality for fully Bayesian, nonstationary Gaussian process modeling. The approach to nonstationary modeling involves a closed-form, convolution-based covariance function with spatially-varying parameters; these parameter processes can be specified either deterministically (using covariates or basis functions) or stochastically (using approximate Gaussian processes). Stationary Gaussian processes are a special case of our methodology, and we furthermore implement approximate Gaussian process inference to account for very large spatial data sets (Finley, et al (2017) ). Bayesian inference is carried out using Markov chain Monte Carlo methods via the 'nimble' package, and posterior prediction for the Gaussian process at unobserved locations is provided as a post-processing step.
How to cite:
Daniel Turek (2019). BayesNSGP: Bayesian Analysis of Non-Stationary Gaussian Process Models. R package version 0.1.2, https://cran.r-project.org/web/packages/BayesNSGP. Accessed 11 Apr. 2025.
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Other R packages that BayesNSGP depends,
imports, suggests or enhances
Complete documentation for BayesNSGP
Functions, R codes and Examples using
the BayesNSGP R package
Some associated functions: R_sparse_chol . R_sparse_crossprod . R_sparse_solve . R_sparse_tcrossprod . calcQF . calculateAD_ns . calculateU_ns . conditionLatentObs . determineNeighbors . dmnorm_nngp . dmnorm_sgv . inverseEigen . matern_corr . nimble_sparse_chol . nimble_sparse_crossprod . nimble_sparse_solve . nimble_sparse_tcrossprod . nsCorr . nsCrosscorr . nsCrossdist . nsCrossdist3d . nsDist . nsDist3d . nsgpModel . nsgpPredict . orderCoordinatesMMD . rmnorm_nngp . rmnorm_sgv . sgvSetup .
Some associated R codes: NNGP.R . SGV.R . core.R . Full BayesNSGP package functions and examples
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