Gaussian process regression models, a.k.a. Kriging models, are
    applied to global multi-objective optimization of black-box functions.
    Multi-objective Expected Improvement and Step-wise Uncertainty Reduction
    sequential infill criteria are available. A quantification of uncertainty
    on Pareto fronts is provided using conditional simulations.

Derivative Free Gradient Projection Algorithms for Factor Rotation.
    For more details see ?GPArotateDF. Theory for these functions can be found in
    the following publications:
    Jennrich (2004) <doi:10.1007/BF02295647>.
    Bernaards and Jennrich (2005) <doi:10.1177/0013164404272507>.

Gradient Projection Algorithms for Factor Rotation. 
    For details see ?GPArotation. When using this package, please cite
    Bernaards and Jennrich (2005) <doi:10.1177/0013164404272507>
    'Gradient Projection Algorithms and Software 
    for Arbitrary Rotation Criteria in Factor Analysis'.

Gaussian processes ('GPs') have been widely used to model spatial data, 'spatio'-temporal data, and computer experiments in diverse areas of statistics including spatial statistics, 'spatio'-temporal statistics, uncertainty quantification, and machine learning. This package creates basic tools for fitting and prediction based on 'GPs' with spatial data, 'spatio'-temporal data, and computer experiments. Key characteristics for this GP tool include: (1) the comprehensive implementation of various covariance functions including the 'Matérn' family and the Confluent 'Hypergeometric' family with isotropic form, tensor form, and automatic relevance determination form, where the isotropic form is widely used in spatial statistics, the tensor form is widely used in design and analysis of computer experiments and uncertainty quantification, and the automatic relevance determination form is widely used in machine learning; (2) implementations via Markov chain Monte Carlo ('MCMC') algorithms and optimization algorithms for GP models with all the implemented covariance functions. The methods for fitting and prediction are mainly implemented in a Bayesian framework; (3) model evaluation via Fisher information and predictive metrics such as predictive scores; (4) built-in functionality for simulating 'GPs' with all the implemented covariance functions; (5) unified implementation to allow easy specification of various 'GPs'.