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DiceKriging: Kriging methods for computer experiments

This repository is the regular DiceKriging package repository (available at http://cran.r-project.org/web/packages/DiceKriging). It contains the latest sources, possibly some supplement of stable CRAN release.

Installation

You can install the standard (CRAN) version of the code: install.packages("DiceKriging")

You can install the latest (this repository) version x.y.z of the code:

  • using pre-built packages:
    • Windows: install.packages("https://github.com/DiceKrigingClub/DiceKriging/releases/download/windows/DiceKriging_x.y.z.zip")
    • Linux: install.packages("https://github.com/DiceKrigingClub/DiceKriging/releases/download/osx-linux/DiceKriging_x.y.z.tar.gz")
    • OSX: install.packages("https://github.com/DiceKrigingClub/DiceKriging/releases/download/osx-linux/DiceKriging_x.y.z.tgz")
  • or using the devtools R package (assuming you have C compiler installed):
install.packages("devtools") # Install devtools, if you haven't already.
devtools::install_github("DiceKriging", "DiceKrigingClub")

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Version

Install

install.packages('DiceKriging')

Monthly Downloads

9,252

Version

1.5.8

License

GPL-2 | GPL-3

Maintainer

Olivier Roustant

Last Published

November 5th, 2020

Functions in DiceKriging (1.5.8)

SCAD

Penalty function
DiceKriging-package

Kriging Methods for Computer Experiments
covIso-class

Class of tensor-product spatial covariances with isotropic range
checkNames

Consistency test between the column names of two matrices
camelback

2D test function
SCAD.derivative

Penalty function derivative
covTensorProduct-class

Class of tensor-product spatial covariances
covKernel-class

Class "covKernel"
coef

Get coefficients values
km1Nugget.init

Fitting Kriging Models
logLik

log-likelihood of a km object
covUser-class

Class "covUser"
kmData

Fit and/or create kriging models
computeAuxVariables

Auxiliary variables for kriging
scalingGrad

Gradient of the dimensional Scaling function
covparam2vect

Auxiliary function
covVector.dx

Spatial covariance - Derivatives
logLikFun

Concentrated log-likelihood of a km object
drop.response

Trend model formula operation
covScaling-class

Class "covScaling"
branin

2D test function
covStruct.create

Spatial covariance - Class constructor
covMatrix

Covariance matrix
covMat1Mat2

Cross covariance matrix
ninput

Get the spatial dimension
hartman3

3D test function
logLikGrad

Concentrated log-Likelihood of a km object - Analytical gradient
covParametersBounds

Boundaries for covariance parameters
show

Print values of a km object
goldsteinPrice

2D test function
covMatrixDerivative

Covariance matrix derivatives
nuggetflag

Get the nugget flag
inputnames

Get the input variables names
leaveOneOut.km

Leave-one-out for a km object
kmNuggets.init

Fitting Kriging Models
nuggetvalue

Get or set the nugget value
leaveOneOutFun

Leave-one-out least square criterion of a km object
hartman6

6D test function
km

Fit and/or create kriging models
plot

Diagnostic plot for the validation of a km object
km-class

Kriging models class
simulate

Simulate GP values at any given set of points for a km object
scalingFun

Scaling function
update

Update of a kriging model
scalingFun1d

Scaling 1-dimensional function
trendMatrix.update

Trend model matrix operation
leaveOneOutGrad

Leave-one-out least square criterion - Analytical gradient
vect2covparam

Auxiliary function
predict

Predict values and confidence intervals at newdata for a km object
kmEstimate

Fitting Kriging Models
kernelname

Get the kernel name
kmNoNugget.init

Fitting Kriging Models
trend.deltax

Trend derivatives