| magp2d_fit | Fit a MaGP model with a two-dimensional sequence map |
| magp2d_rmse | Calculate root-mean-squared prediction error |
| magpfull_fit | Fit a MaGP model with a full sequence map |
| magp_bayes_optimize | Continue Bayesian optimization from completed experiments |
| magp_bayes_optimize_from_scratch | Start Bayesian optimization before any experiments have been run |
| magp_expected_improvement | Score candidate experiments with expected improvement |
| magp_initial_design | Construct a quantitative-sequence initial design |
| magp_joint_criterion | Evaluate a complete quantitative-sequence initial design |
| magp_next_point | Select the next quantitative-sequence experiment |
| magp_quantitative_criterion | Evaluate a quantitative Latin hypercube |
| magp_quantitative_design | Construct the quantitative portion of an initial design |
| magp_sequence_criterion | Evaluate a sequence initial design |
| magp_sequence_design | Construct the sequence portion of an initial design |
| predict.magp | Predict outcomes from a fitted MaGP model |
| predict.magp2d | Predict outcomes from a fitted MaGP model |
| predict.magpfull | Predict outcomes from a fitted MaGP model |
| print.magp2d | Summarize a fitted two-dimensional MaGP model |
| print.magpfull | Summarize a fitted full-mapping MaGP model |
| print.magp_bayes_opt | Print a MaGP Bayesian optimization result |
| print.magp_initial_design | Print a quantitative-sequence initial design |
| print.magp_next_point | Print a MaGP acquisition-search result |
| print.magp_quantitative_design | Print a quantitative initial design |
| print.magp_sequence_design | Print a sequence initial design |