# Kriging Models using the 'libKriging' Library

## Documentation for package ‘rlibkriging’ version 0.8-0.1

- DESCRIPTION file.

## Help Pages

A C F K L N P S T U V X Y misc

## -- A --

abline(v | Compute Log-Likelihood of NoiseKriging Model
as.km | Coerce an Object into a 'km' Object
as.km-method | Coerce a 'Kriging' object into the '"km"' class of the 'DiceKriging' package.
as.km.Kriging | Coerce a 'Kriging' object into the '"km"' class of the 'DiceKriging' package.
as.km.NoiseKriging | Coerce a 'NoiseKriging' object into the '"km"' class of the 'DiceKriging' package.
as.km.NuggetKriging | Coerce a 'NuggetKriging' object into the '"km"' class of the 'DiceKriging' package.
as.list-method | Coerce a 'Kriging' Object into a List
as.list-method | Coerce a 'NoiseKriging' Object into a List
as.list-method | Coerce a 'NuggetKriging' Object into a List
as.list.Kriging | Coerce a 'Kriging' Object into a List
as.list.NoiseKriging | Coerce a 'NoiseKriging' Object into a List
as.list.NuggetKriging | Coerce a 'NuggetKriging' Object into a List
as.matrix(runif(10)) | Compute Log-Likelihood of NoiseKriging Model

## -- C --

cbind(theta,sigma20))$logLikelihood | Compute Log-Likelihood of NoiseKriging Model
cbind(theta0,sigma2))$logLikelihood | Compute Log-Likelihood of NoiseKriging Model
col | Compute Log-Likelihood of NoiseKriging Model
contour(t,s2,matrix(ncol=length(s2),ll(expand.grid(t,s2))),xlab="theta",ylab="sigma2") | Compute Log-Likelihood of NoiseKriging Model
copy | Duplicate object.
copy-method | Duplicate a Kriging Model
copy-method | Duplicate a NoiseKriging Model
copy-method | Duplicate a NuggetKriging Model
copy.Kriging | Duplicate a Kriging Model
copy.NoiseKriging | Duplicate a NoiseKriging Model
copy.NuggetKriging | Duplicate a NuggetKriging Model
cos(7 | Compute Log-Likelihood of NoiseKriging Model

## -- F --

f | Compute Log-Likelihood of NoiseKriging Model
f(X) | Compute Log-Likelihood of NoiseKriging Model
fit | Fit model on data.
fit.Kriging | Fit 'Kriging' object on given data.
fit.NoiseKriging | Fit 'NoiseKriging' object on given data.
fit.NuggetKriging | Fit 'NuggetKriging' object on given data.
function(sigma2) | Compute Log-Likelihood of NoiseKriging Model
function(theta) | Compute Log-Likelihood of NoiseKriging Model
function(theta_sigma2) | Compute Log-Likelihood of NoiseKriging Model
function(x) | Compute Log-Likelihood of NoiseKriging Model

## -- K --

k | Compute Log-Likelihood of NoiseKriging Model
k$sigma2() | Compute Log-Likelihood of NoiseKriging Model
k$sigma2(), | Compute Log-Likelihood of NoiseKriging Model
k$theta() | Compute Log-Likelihood of NoiseKriging Model
k$theta(), | Compute Log-Likelihood of NoiseKriging Model
kernel | Compute Log-Likelihood of NoiseKriging Model
KM | Create an 'KM' Object
KM-class | S4 class for Kriging Models Extending the '"km"' Class
Kriging | Create an object with S3 class '"Kriging"' using the 'libKriging' library.

## -- L --

leaveOneOut | Compute Leave-One-Out
leaveOneOut-method | Get leaveOneOut of Kriging Model
leaveOneOut.Kriging | Get leaveOneOut of Kriging Model
leaveOneOutFun | Leave-One-Out function
leaveOneOutFun-method | Compute Leave-One-Out (LOO) error for an object with S3 class '"Kriging"' representing a kriging model.
leaveOneOutFun.Kriging | Compute Leave-One-Out (LOO) error for an object with S3 class '"Kriging"' representing a kriging model.
leaveOneOutVec | Leave-One-Out vector
leaveOneOutVec-method | Compute Leave-One-Out (LOO) vector error for an object with S3 class '"Kriging"' representing a kriging model.
leaveOneOutVec.Kriging | Compute Leave-One-Out (LOO) vector error for an object with S3 class '"Kriging"' representing a kriging model.
length.out | Compute Log-Likelihood of NoiseKriging Model
ll | Compute Log-Likelihood of NoiseKriging Model
ll_sigma2 | Compute Log-Likelihood of NoiseKriging Model
ll_theta | Compute Log-Likelihood of NoiseKriging Model
load | Load any Kriging Model from a file storage.
load.Kriging | Load a Kriging Model from a file storage
load.NoiseKriging | Load a NoiseKriging Model from a file storage
load.NuggetKriging | Load a NuggetKriging Model from a file storage
logLikelihood | Compute Log-Likelihood
logLikelihood-method | Get Log-Likelihood of Kriging Model
logLikelihood-method | Get logLikelihood of NoiseKriging Model
logLikelihood-method | Get logLikelihood of NuggetKriging Model
logLikelihood.Kriging | Get Log-Likelihood of Kriging Model
logLikelihood.NoiseKriging | Get logLikelihood of NoiseKriging Model
logLikelihood.NuggetKriging | Get logLikelihood of NuggetKriging Model
logLikelihoodFun | Log-Likelihood function
logLikelihoodFun(k, | Compute Log-Likelihood of NoiseKriging Model
logLikelihoodFun-method | Compute Log-Likelihood of Kriging Model
logLikelihoodFun-method | Compute Log-Likelihood of NoiseKriging Model
logLikelihoodFun-method | Compute Log-Likelihood of NuggetKriging Model
logLikelihoodFun.Kriging | Compute Log-Likelihood of Kriging Model
logLikelihoodFun.NoiseKriging | Compute Log-Likelihood of NoiseKriging Model
logLikelihoodFun.NuggetKriging | Compute Log-Likelihood of NuggetKriging Model
logMargPost | Compute log-Marginal Posterior
logMargPost-method | Get logMargPost of Kriging Model
logMargPost-method | Get logMargPost of NuggetKriging Model
logMargPost.Kriging | Get logMargPost of Kriging Model
logMargPost.NuggetKriging | Get logMargPost of NuggetKriging Model
logMargPostFun | log-Marginal Posterior function
logMargPostFun-method | Compute the log-marginal posterior of a kriging model, using the prior XXXY.
logMargPostFun-method | Compute the log-marginal posterior of a kriging model, using the prior XXXY.
logMargPostFun.Kriging | Compute the log-marginal posterior of a kriging model, using the prior XXXY.
logMargPostFun.NuggetKriging | Compute the log-marginal posterior of a kriging model, using the prior XXXY.

## -- N --

NoiseKM | Create an 'NoiseKM' Object
NoiseKM-class | S4 class for NoiseKriging Models Extending the '"km"' Class
NoiseKriging | Create an object with S3 class '"NoiseKriging"' using the 'libKriging' library.
NoiseKriging(y, | Compute Log-Likelihood of NoiseKriging Model
NuggetKM | Create an 'NuggetKM' Object
NuggetKM-class | S4 class for NuggetKriging Models Extending the '"km"' Class
NuggetKriging | Create an object with S3 class '"NuggetKriging"' using the 'libKriging' library.

## -- P --

plot(s2, | Compute Log-Likelihood of NoiseKriging Model
plot(t, | Compute Log-Likelihood of NoiseKriging Model
points(k$theta(),k$sigma2(),col='blue') | Compute Log-Likelihood of NoiseKriging Model
predict-method | Prediction Method for a 'KM' Object
predict-method | Prediction Method for a 'NoiseKM' Object
predict-method | Prediction Method for a 'NuggetKM' Object
predict.Kriging | Predict from a 'Kriging' object.
predict.NoiseKriging | Predict from a 'NoiseKriging' object.
predict.NuggetKriging | Predict from a 'NuggetKriging' object.
print(k) | Compute Log-Likelihood of NoiseKriging Model
print.Kriging | Print the content of a 'Kriging' object.
print.NoiseKriging | Print the content of a 'NoiseKriging' object.
print.NuggetKriging | Print the content of a 'NuggetKriging' object.

## -- S --

s2 | Compute Log-Likelihood of NoiseKriging Model
save | Save object.
save-method | Save a Kriging Model to a file storage
save-method | Save a NoiseKriging Model to a file storage
save-method | Save a NuggetKriging Model to a file storage
save.Kriging | Save a Kriging Model to a file storage
save.NoiseKriging | Save a NoiseKriging Model to a file storage
save.NuggetKriging | Save a NuggetKriging Model to a file storage
seq(from | Compute Log-Likelihood of NoiseKriging Model
set.seed(123) | Compute Log-Likelihood of NoiseKriging Model
sigma20 | Compute Log-Likelihood of NoiseKriging Model
simulate-method | Simulation from a 'KM' Object
simulate-method | Simulation from a 'NoiseKM' Object
simulate-method | Simulation from a 'NuggetKM' Object
simulate.Kriging | Simulation from a 'Kriging' model object.
simulate.NoiseKriging | Simulation from a 'NoiseKriging' model object.
simulate.NuggetKriging | Simulation from a 'NuggetKriging' model object.

## -- T --

t | Compute Log-Likelihood of NoiseKriging Model
theta0 | Compute Log-Likelihood of NoiseKriging Model
theta_sigma2)$logLikelihood | Compute Log-Likelihood of NoiseKriging Model
to | Compute Log-Likelihood of NoiseKriging Model
type | Compute Log-Likelihood of NoiseKriging Model

## -- U --

update-method | Update a 'KM' Object with New Points
update-method | Update a 'NoiseKM' Object with New Points
update-method | Update a 'NuggetKM' Object with New Points
update.Kriging | Update a 'Kriging' model object with new points
update.NoiseKriging | Update a 'NoiseKriging' model object with new points
update.NuggetKriging | Update a 'NuggetKriging' model object with new points

## -- V --

Vectorize(ll_sigma2)(s2), | Compute Log-Likelihood of NoiseKriging Model
Vectorize(ll_theta)(t), | Compute Log-Likelihood of NoiseKriging Model

## -- X --

X | Compute Log-Likelihood of NoiseKriging Model
x) | Compute Log-Likelihood of NoiseKriging Model
X, | Compute Log-Likelihood of NoiseKriging Model
X/10 | Compute Log-Likelihood of NoiseKriging Model
x^5 | Compute Log-Likelihood of NoiseKriging Model

## -- Y --

y | Compute Log-Likelihood of NoiseKriging Model

## -- misc --

"blue") | Compute Log-Likelihood of NoiseKriging Model
"matern3_2") | Compute Log-Likelihood of NoiseKriging Model
'l') | Compute Log-Likelihood of NoiseKriging Model
(1 | Compute Log-Likelihood of NoiseKriging Model
(sin(12 | Compute Log-Likelihood of NoiseKriging Model
(X/10)^2, | Compute Log-Likelihood of NoiseKriging Model
* | Compute Log-Likelihood of NoiseKriging Model
*rnorm(nrow(X)) | Compute Log-Likelihood of NoiseKriging Model
+ | Compute Log-Likelihood of NoiseKriging Model
- | Compute Log-Likelihood of NoiseKriging Model
/ | Compute Log-Likelihood of NoiseKriging Model
0.001, | Compute Log-Likelihood of NoiseKriging Model
0.7) | Compute Log-Likelihood of NoiseKriging Model
1 | Compute Log-Likelihood of NoiseKriging Model
1, | Compute Log-Likelihood of NoiseKriging Model
101) | Compute Log-Likelihood of NoiseKriging Model
2 | Compute Log-Likelihood of NoiseKriging Model
2, | Compute Log-Likelihood of NoiseKriging Model
31) | Compute Log-Likelihood of NoiseKriging Model
<- | Compute Log-Likelihood of NoiseKriging Model
= | Compute Log-Likelihood of NoiseKriging Model