Two recently released R packages for the approximation and the optimization of expensive-to-evaluate deterministic functions are presented. After a motivation of the DiceKriging and DiceOptim packages relying on an overview of existing needs and softwares in the field of Kriging for Computer Experiments, a self-contained mini-tutorial on Kriging-based approximation and optimization techniques is proposed. The functionalities of both packages are then detailed and demonstrated in two disctinct sections. In particular, the versatility of DiceKriging with respect to trend and noise specications, covariance parameter estimation, as well as conditional and unconditional simulations are illustrated on the basis of several reproducible numerical experiments. The implementation of sequential and parallel optimization strategies relying on the Expected Improvement criterion are then put to the fore on the occasion of DiceOptim's presentation. An appendix is finally dedicated to complementary mathematical and computational details. N.B. This paper is a follow up of Roustant, Ginsbourger, and Deville (2012), compiled with the versions of DiceKriging and DiceOptim mentioned in the left margin. Some di fferences may exist, and the most important ones are precised in Appendix E.