  The CompModels package for R provides a suite of computer model test
functions that can be used for computer model prediction/emulation, uncertainty
quantification, and calibration, but in particular, the sequential optimization
of computer models. The package is a mix of real-world physics problems, known
mathematical functions, and black-box functions that have been converted into
computer models with the goal of Bayesian (i.e., sequential) optimization in
mind. Likewise, the package contains computer models that represent either the
constrained or unconstrained optimization case, each with varying levels of
difficulty. In this paper, we illustrate the use of the package with both
real-world examples and black-box functions by solving constrained optimization
problems via Bayesian optimization. Ultimately, the package is shown to provide
users with a source of computer model test functions that are reproducible,
shareable, and that can be used for benchmarking of novel optimization methods.
