| 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_logEI | Logarithm of Expected Improvement criterion |
| crit_MCU | Maximum Contour Uncertainty criterion |
| crit_MEE | Maximum Empirical Error 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 |
| 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 |
| 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 |
| 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.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') |
| 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. |
| 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). |
| simul | Conditional simulation for CRNGP |
| simul.CRNGP | Fast conditional simulation for a CRNGP model |
| 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 |