IMSPE                   Integrated Mean Square Prediction Error
IMSPE_optim             IMSPE optimization
LOO_preds               Leave one out predictions
Wij                     Compute double integral of the covariance
                        kernel over a [0,1]^d domain
allocate_mult           Allocation of replicates on existing designs
allocq                  Allocation
allocq_c                Allocation under maximum replication
                        constraints
ato                     Assemble To Order (ATO) Data and Fits
bfs                     Bayes Factor Data
compareGP               Likelihood-based comparison of models
cov_gen                 Correlation function of selected type,
                        supporting both isotropic and product forms
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_cSUR               Contour Stepwise Uncertainty Reduction
                        criterion
crit_logEI              Logarithm of Expected Improvement criterion
crit_optim              Criterion optimization
crit_qEI                Parallel Expected improvement
crit_tMSE               t-MSE criterion
deriv_crit_EI           Derivative of EI criterion for GP models
deriv_crit_IMSPE        Derivative of crit_IMSPE
deriv_crit_logEI        Derivative of logEI criterion for GP models
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
hyperSharpeMax          Hypervolume Sharpe ratio maximization
hyperSharperQ           Hypervolume Sharpe ratio return and covariance
mleCRNGP                Gaussian process modeling with correlated noise
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
pred_noisy_input        Gaussian process prediction prediction at a
                        noisy input 'x', with centered Gaussian noise
                        of variance 'sigma_x'.  Several options are
                        available, with different efficiency/accuracy
                        tradeoffs.
predict.CRNGP           Gaussian process predictions using a GP object
                        for correlated noise (of class 'CRNGP')
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')
qEI_loop                BO loop with qEI
qHSRI_loop              BO loop with massive batches
rebuild                 Import and export of hetGP objects
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).
simul                   Conditional simulation for CRNGP
simul.CRNGP             Fast conditional simulation for a CRNGP model
sirEval                 SIR test problem
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_pred             Prediction update with new designs and
                        observations
