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GPBayes
View on CRAN: Click
here
Download and install GPBayes package within the R console
Install from CRAN:
install.packages("GPBayes")
Install from Github:
library("remotes")
install_github("cran/GPBayes")
Install by package version:
library("remotes")
install_version("GPBayes", "0.1.0-5.1")
Attach the package and use:
library("GPBayes")
Maintained by
Pulong Ma
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2021-10-08
Latest Update: 2023-02-01
Description:
Gaussian processes ('GPs') have been widely used to model spatial data, 'spatio'-temporal data, and computer experiments in diverse areas of statistics including spatial statistics, 'spatio'-temporal statistics, uncertainty quantification, and machine learning. This package creates basic tools for fitting and prediction based on 'GPs' with spatial data, 'spatio'-temporal data, and computer experiments. Key characteristics for this GP tool include: (1) the comprehensive implementation of various covariance functions including the 'Mat
How to cite:
Pulong Ma (2021). GPBayes: Tools for Gaussian Process Modeling in Uncertainty Quantification. R package version 0.1.0-5.1, https://cran.r-project.org/web/packages/GPBayes
Previous versions and publish date:
Other packages that cited GPBayes R package
View GPBayes citation profile
Other R packages that GPBayes depends,
imports, suggests or enhances
Functions, R codes and Examples using
the GPBayes R package
Some associated functions: BesselK . CH . GPBayes-package . GaSP . HypergU . cauchy . cor.to.par . deriv_kernel . distance . gp-class . gp-method . gp.fisher . gp.get.mcmc . gp.mcmc . gp.model.adequacy . gp . gp.optim . gp.predict . gp.sim . ikernel . kernel . loglik . matern . powexp .
Some associated R codes: AllClass.R . GaSPUtils.R . RcppExports.R . startup.R . utils.R . Full GPBayes package functions and examples
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