AEI                     Augmented Expected Improvement
AEI.grad                AEI's Gradient
AKG                     Approximate Knowledge Gradient (AKG)
AKG.grad                AKG's Gradient
DiceOptim-package       Kriging-based optimization methods for computer
                        experiments
EGO.cst                 Sequential constrained Expected Improvement
                        maximization and model re-estimation, with a
                        number of iterations fixed in advance by the
                        user
EGO.nsteps              Sequential EI maximization and model
                        re-estimation, with a number of iterations
                        fixed in advance by the user
EI                      Analytical expression of the Expected
                        Improvement criterion
EI.grad                 Analytical gradient of the Expected Improvement
                        criterion
EQI                     Expected Quantile Improvement
EQI.grad                EQI's Gradient
ParrConstraint          2D constraint function
TREGO.nsteps            Trust-region based EGO algorithm.
checkPredict            Prevention of numerical instability for a new
                        observation
crit_AL                 Expected Augmented Lagrangian Improvement
crit_EFI                Expected Feasible Improvement
crit_SUR_cst            Stepwise Uncertainty Reduction criterion
critcst_optimizer       Maximization of constrained Expected
                        Improvement criteria
easyEGO                 User-friendly wrapper of the functions
                        'fastEGO.nsteps' and 'TREGO.nsteps'. Generates
                        initial DOEs and kriging models (objects of
                        class 'km'), and executes 'nsteps' iterations
                        of either EGO or TREGO.
easyEGO.cst             EGO algorithm with constraints
fastEGO.nsteps          Sequential EI maximization and model
                        re-estimation, with a number of iterations
                        fixed in advance by the user
fastfun                 Fastfun function
integration_design_cst
                        Generic function to build integration points
                        (for the SUR criterion)
kriging.quantile        Kriging quantile
kriging.quantile.grad   Analytical gradient of the Kriging quantile of
                        level beta
max_AEI                 Maximizer of the Augmented Expected Improvement
                        criterion function
max_AKG                 Maximizer of the Expected Quantile Improvement
                        criterion function
max_EI                  Maximization of the Expected Improvement
                        criterion
max_EQI                 Maximizer of the Expected Quantile Improvement
                        criterion function
max_crit                Maximization of the Expected Improvement
                        criterion
max_qEI                 Maximization of multipoint expected improvement
                        criterion (qEI)
min_quantile            Minimization of the Kriging quantile.
noisy.optimizer         Optimization of homogenously noisy functions
                        based on Kriging
qEGO.nsteps             Sequential multipoint Expected improvement
                        (qEI) maximizations and model re-estimation
qEI                     Analytical expression of the multipoint
                        expected improvement (qEI) criterion
qEI.grad                Gradient of the multipoint expected improvement
                        (qEI) criterion
sampleFromEI            Sampling points according to the expected
                        improvement criterion
test_feas_vec           Test constraints violation (vectorized)
update_km_noisyEGO      Update of one or two Kriging models when adding
                        new observation
