It is often interesting to see how the distribution of performance measures changes as the current design changes due to the variability in the input variables. For efficiency, the performance measures are approximated by using local window surrogate models. Dynamic Kriging (DKG), which is an improved version of Kriging, is used for the surrogate modeling method. Input distributions can be represented using seven marginal distribution types (Normal, Lognormal, Weibull, Gumbel, Gamma, Extreme and Extreme type-II). Eight types of copulas are provided to depict statistical correlation of input random variables (Clayton, Frank, FGM, Gaussian, AMH, Gumbel, A12 and A14). RAMDO can generate the output distributions, which is the probability distributions of the performance measures, using Monte Carlo with the Dynamic Kriging surrogate models. The output distributions can used to calculate various statistics about the performance measures.