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This repository is just a wrapper to emulate a "standard" R package from https://github.com/libKriging/bindings/R/rlibkriging content, so you can install it just using:

install.packages('devtools')
devtools::install_github("libKriging/rlibkriging")

The stable version is available from CRAN :

install.packages('rlibkriging')

Requirements

Note: this repository mainly contains modified Makefiles, inspired by https://github.com/astamm/nloptr wrapper.

CRAN

When submitting to CRAN, ./tools/setup.sh should be run before R CMD build rlibkriging to fit CRAN policy.

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Version

Install

install.packages('rlibkriging')

Monthly Downloads

243

Version

0.9-2.2

License

Apache License (>= 2)

Maintainer

Yann Richet

Last Published

August 27th, 2025

Functions in rlibkriging (0.9-2.2)

as.list.NoiseKriging

Coerce a NoiseKriging Object into a List
as.list.NuggetKriging

Coerce a NuggetKriging Object into a List
classKriging

Shortcut to provide functions to the S3 class "Kriging"
as.km.NuggetKriging

Coerce a NuggetKriging object into the "km" class of the DiceKriging package.
covMat

covariance function
copy

Duplicate object.
covMat.NuggetKriging

Compute Covariance Matrix of NuggetKriging Model
covMat.NoiseKriging

Compute Covariance Matrix of NoiseKriging Model
covMat.Kriging

Compute Covariance Matrix of Kriging Model
leaveOneOut.Kriging

Get leaveOneOut of Kriging Model
load.NuggetKriging

Load a NuggetKriging Model from a file storage
fit.NuggetKriging

Fit NuggetKriging object on given data.
load.NoiseKriging

Load a NoiseKriging Model from a file storage
fit.NoiseKriging

Fit NoiseKriging object on given data.
logLikelihood

Compute Log-Likelihood
fit

Fit model on data.
fit.Kriging

Fit Kriging object on given data.
predict.Kriging

Predict from a Kriging object.
logMargPost.Kriging

Get logMargPost of Kriging Model
leaveOneOutFun

Leave-One-Out function
leaveOneOutFun.Kriging

Compute Leave-One-Out (LOO) error for an object with S3 class "Kriging" representing a kriging model.
leaveOneOut

Compute Leave-One-Out
logLikelihood.NuggetKriging

Get logLikelihood of NuggetKriging Model
logLikelihood.NoiseKriging

Get logLikelihood of NoiseKriging Model
logLikelihoodFun.Kriging

Compute Log-Likelihood of Kriging Model
predict.NoiseKriging

Predict from a NoiseKriging object.
load

Load any Kriging Model from a file storage. Back to base::load if not a Kriging object.
logLikelihoodFun

Log-Likelihood function
leaveOneOutVec.Kriging

Compute Leave-One-Out (LOO) vector error for an object with S3 class "Kriging" representing a kriging model.
logMargPost.NuggetKriging

Get logMargPost of NuggetKriging Model
logMargPost

Compute log-Marginal Posterior
logMargPostFun

log-Marginal Posterior function
logLikelihood.Kriging

Get Log-Likelihood of Kriging Model
predict,NoiseKM-method

Prediction Method for a NoiseKM Object
classNoiseKriging

Shortcut to provide functions to the S3 class "NoiseKriging"
predict,KM-method

Prediction Method for a KM Object
predict.NuggetKriging

Predict from a NuggetKriging object.
print.Kriging

Print the content of a Kriging object.
print.NoiseKriging

Print the content of a NoiseKriging object.
save.Kriging

Save a Kriging Model to a file storage
save.NoiseKriging

Save a NoiseKriging Model to a file storage
predict,NuggetKM-method

Prediction Method for a NuggetKM Object
simulate.NuggetKriging

Simulation from a NuggetKriging model object.
simulate.NoiseKriging

Simulation from a NoiseKriging model object.
print.NuggetKriging

Print the content of a NuggetKriging object.
update_simulate

Update simulation of model on data.
update.NuggetKriging

Update a NuggetKriging model object with new points
update,NoiseKM-method

Update a NoiseKM Object with New Points
update,KM-method

Update a KM Object with New Points
update_simulate.NuggetKriging

Update previous simulation of a NuggetKriging model object.
update.NoiseKriging

Update a NoiseKriging model object with new points
logMargPostFun.Kriging

Compute the log-marginal posterior of a kriging model, using the prior XXXY.
copy.NuggetKriging

Duplicate a NuggetKriging Model
copy.NoiseKriging

Duplicate a NoiseKriging Model
leaveOneOutVec

Leave-One-Out vector
logMargPostFun.NuggetKriging

Compute the log-marginal posterior of a kriging model, using the prior XXXY.
save

Save a Kriging Model inside a file. Back to base::save if argument is not a Kriging object.
save.NuggetKriging

Save a NuggetKriging Model to a file storage
logLikelihoodFun.NoiseKriging

Compute Log-Likelihood of NoiseKriging Model
load.Kriging

Load a Kriging Model from a file storage
update,NuggetKM-method

Update a NuggetKM Object with New Points
update_simulate.Kriging

Update previous simulation of a Kriging model object.
simulate.Kriging

Simulation from a Kriging model object.
simulate,NuggetKM-method

Simulation from a NuggetKM Object
simulate,NoiseKM-method

Simulation from a NoiseKM Object
logLikelihoodFun.NuggetKriging

Compute Log-Likelihood of NuggetKriging Model
simulate,KM-method

Simulation from a KM Object
update.Kriging

Update a Kriging model object with new points
update_simulate.NoiseKriging

Update previous simulation of a NoiseKriging model object.
NoiseKriging

Create an object with S3 class "NoiseKriging" using the libKriging library.
as.km.Kriging

Coerce a Kriging object into the "km" class of the DiceKriging package.
NuggetKriging

Create an object with S3 class "NuggetKriging" using the libKriging library.
NoiseKM

Create an NoiseKM Object
as.km

Coerce an Object into a km Object
as.km.NoiseKriging

Coerce a NoiseKriging object into the "km" class of the DiceKriging package.
as.list.Kriging

Coerce a Kriging Object into a List
KM-class

S4 class for Kriging Models Extending the "km" Class
KM

Create an KM Object
classNuggetKriging

Shortcut to provide functions to the S3 class "NuggetKriging"
copy.Kriging

Duplicate a Kriging Model
NuggetKM

Create an NuggetKM Object
NuggetKM-class

S4 class for NuggetKriging Models Extending the "km" Class
Kriging

Create an object with S3 class "Kriging" using the libKriging library.
NoiseKM-class

S4 class for NoiseKriging Models Extending the "km" Class