This paper introduces two R packages available on the Comprehensive R Archive network. The main application concerns the study of computer code output. Package DiceDesign is dedicated to numerical design of experiments, from the construction to the study of the design properties. Package DiceEval deals with the fit, the validation and the comparison of metamodels.After a brief presentation of the context, we focus on the architecture of these two packages. A two-dimensional test function will be a running example to illustrate the main functionalities of these packages and an industrial case study in five dimensions will also be detailed.

A kinetic model for the dehydration of lithium sulfate monohydrate is proposed in order to account for experimental data obtained on single crystals by thermogravimetry at 80 1C under fixed water vapour pressure, and by optical microscopy. This model is based on the assumptions of Mampel's model, the nucleation takes place randomly at the surface of the solid and is followed by isotropic growth toward the centre of the crystal. Calculated rates da/dt are obtained by means of Monte-Carlo simulations and compared to the experimental ones, which leads to the determination of two kinetic constants: the areic frequency of nucleation (in number of nuclei m À2 s À1 ) and the areic reactivity of growth (in mol m À2 s À1 ).

We consider the optimization of a computer model where each simulation either fails or returns a valid output performance. We first propose a new joint Gaussian process model for classification of the inputs (computation failure or success) and for regression of the performance function. We provide results that allow for a computationally efficient maximum likelihood estimation of the covariance parameters, with a stochastic approximation of the likelihood gradient. We then extend the classical improvement criterion to our setting of joint classification and regression. We provide an efficient computation procedure for the extended criterion and its gradient. We prove the almost sure convergence of the global optimization algorithm following from this extended criterion. We also study the practical performances of this algorithm, both on simulated data and on a real computer model in the context of automotive fan design.