  Gaussian stochastic process emulation is a powerful tool for approximating
computationally intensive computer models. However, estimation of parameters in
the GaSP emulator is a challenging task. No closed-form estimator is available
and many numerical problems arise with standard estimates, e.g., the maximum
likelihood estimator. In this package, we implement a marginal posterior mode
estimator, for special priors and parameterizations, an estimation method that
meets the robust parameter estimation criteria discussed in
\cite{Gu2018robustness}; mathematical reasons are provided therein to explain
why robust parameter estimation can greatly improve predictive performance of
the emulator. In addition, inert inputs (inputs that almost have no effect on
the variability of a function) can be identified from the marginal posterior
mode estimation, at no extra computational cost. The package also implements
the parallel partial Gaussian stochastic process (PP GaSP) emulator
(\cite{gu2016parallel}) for the scenario where the computer model has multiple
outputs on e.g. spatial-temporal coordinates. The package can be operated in a
default mode, but also allows numerous user specifications, such as the
capability of specifying trend functions and noise terms. Examples are studied
herein to highlight the performance of the package in terms of out-of-sample
prediction.}
