magp - Mapping-Based Additive Gaussian Process Models

Fits mapping-based additive Gaussian process models for experiments in which each component has both a quantitative level and a position in an ordered sequence. Two model structures are available: a compact two-dimensional mapping and a full mapping with one fewer dimension than the number of components. Both models support parameter estimation, point prediction, and plug-in predictive uncertainty. Input checks validate the sequence data and apply consistent scaling to the quantitative inputs. Computationally intensive covariance and gradient calculations are implemented in C++ with 'Rcpp'. Initial-design functions combine a space-filling Latin hypercube with sequence permutations. The sequence portion can be generated randomly or optimized with simulated annealing or space-filling threshold accepting. Expected improvement can be optimized over both parts of the input, and a sequential interface supports Bayesian optimization of an expensive user-supplied objective. An integrated workflow can generate the initial design, evaluate the objective, and continue the sequential search in one call. The model was introduced by Xiao et al. (2024) <doi:10.1080/01621459.2022.2123335>.