# Heteroskedastic Gaussian Process Modeling and Design under Replication

## Documentation for package ‘hetGP’ version 1.1.2

- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- Package NEWS.

## Help Pages

hetGP-package | Package hetGP
allocate_mult | Allocation of replicates on existing designs
ato | Assemble To Order (ATO) Data and Fits
ato.a | Assemble To Order (ATO) Data and Fits
bfs | Bayes Factor Data
bfs.exp | Bayes Factor Data
bfs.gamma | Bayes Factor Data
compareGP | Likelihood-based comparison of models
cov_gen | Correlation function of selected type, supporting both isotropic and product forms
crit_cSUR | Contour Stepwise Uncertainty Reduction criterion
crit_EI | Expected Improvement criterion
crit_ICU | Integrated Contour Uncertainty criterion
crit_IMSPE | Sequential IMSPE criterion
crit_MCU | Maximum Contour Uncertainty criterion
crit_MEE | Maximum Empirical Error criterion
crit_optim | Criterion optimization
crit_tMSE | t-MSE criterion
deriv_crit_EI | Derivative of EI criterion for GP models
deriv_crit_IMSPE | Derivative of crit_IMSPE
f1d | 1d test function (1)
f1d2 | 1d test function (2)
f1d2_n | Noisy 1d test function (2) Add Gaussian noise with variance r(x) = scale * (exp(sin(2 pi x)))^2 to 'f1d2'
f1d_n | Noisy 1d test function (1) Add Gaussian noise with variance r(x) = scale * (1.1 + sin(2 pi x))^2 to 'f1d'
find_reps | Data preprocessing
horizon | Adapt horizon
IMSPE | Integrated Mean Square Prediction Error
IMSPE_optim | IMSPE optimization
kill | Assemble To Order (ATO) Data and Fits
LOO_preds | Leave one out predictions
mleHetGP | Gaussian process modeling with heteroskedastic noise
mleHetTP | Student-t process modeling with heteroskedastic noise
mleHomGP | Gaussian process modeling with homoskedastic noise
mleHomTP | Student-T process modeling with homoskedastic noise
mult | Assemble To Order (ATO) Data and Fits
nc | Assemble To Order (ATO) Data and Fits
out | Assemble To Order (ATO) Data and Fits
out.a | Assemble To Order (ATO) Data and Fits
predict.hetGP | Gaussian process predictions using a heterogeneous noise GP object (of class 'hetGP')
predict.hetTP | Student-t process predictions using a heterogeneous noise TP object (of class 'hetTP')
predict.homGP | Gaussian process predictions using a homoskedastic noise GP object (of class 'homGP')
predict.homTP | Student-t process predictions using a homoskedastic noise GP object (of class 'homGP')
rebuild | Import and export of hetGP objects
rebuild.hetGP | Import and export of hetGP objects
rebuild.hetTP | Import and export of hetGP objects
rebuild.homGP | Import and export of hetGP objects
rebuild.homTP | Import and export of hetGP objects
reps | Assemble To Order (ATO) Data and Fits
scores | Score and RMSE function To asses the performance of the prediction, this function computes the root mean squared error and proper score function (also known as negative log-probability density).
sirEval | SIR test problem
sirSimulate | SIR test problem
strip | Import and export of hetGP objects
train | Assemble To Order (ATO) Data and Fits
update.hetGP | Update '"hetGP"'-class model fit with new observations
update.hetTP | Update '"hetTP"'-class model fit with new observations
update.homGP | Fast 'homGP'-update
update.homTP | Fast 'homTP'-update
Wij | Compute double integral of the covariance kernel over a [0,1]^d domain
X | Assemble To Order (ATO) Data and Fits
Xa | Assemble To Order (ATO) Data and Fits
Xtest | Assemble To Order (ATO) Data and Fits
Xtrain | Assemble To Order (ATO) Data and Fits
Xtrain.out | Assemble To Order (ATO) Data and Fits
Z | Assemble To Order (ATO) Data and Fits
Za | Assemble To Order (ATO) Data and Fits
Zm | Assemble To Order (ATO) Data and Fits
Ztest | Assemble To Order (ATO) Data and Fits
Ztrain | Assemble To Order (ATO) Data and Fits
Ztrain.out | Assemble To Order (ATO) Data and Fits
Zv | Assemble To Order (ATO) Data and Fits