Heteroskedastic Gaussian Process Modeling and Design under Replication

[Up] [Top]

Documentation for package ‘hetGP’ version 1.0.2

Help Pages

hetGP-package Package hetGP
allocate_mult Allocation of replicates on existing designs
compareGP Likelihood-based comparison of models
cov_gen Correlation function of selected type, supporting both isotropic and product forms
crit_IMSE Sequential IMSPE criterion
deriv_crit_IMSE Derivative of crit_IMSE
f1d 1d test function
find_reps Data preprocessing
IMSE.search IMSE minimization
IMSE_nsteps_ahead h-IMSE with replication
IMSPE Integrated Mean Square Prediction Error
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
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
sirEval SIR test problem
sirSimulate SIR test problem
strip Import and export of hetGP objects
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
update_horizon Adapt horizon
Wij Compute double integral of the covariance kernel over a [0,1]^d domain