The GPareto package for R provides multi-objective optimization algorithms for expensive black-box functions and uncertainty quantification methods. Popular methods such as EGO in the mono-objective case relies on Gaussian processes or Kriging to build surrogate models.
Driven by the prediction uncertainty given by these models, several infill criteria have also been proposed in a multi-objective setup to select new points sequentially and efficiently cope with severely limited evaluation budgets.
They are implemented in the package, in addition with estimation of the whole Pareto front location and uncertainty quantification visualization in the design and objective spaces. Finally, it attempts to fill the gap between expert use of the corresponding methods and simple usage, where many efforts have been put on providing graphical visualization, standard tuning and interactivity.