Mapping-Based Additive Gaussian Process Models


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Documentation for package ‘magp’ version 0.12.0

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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