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It is shown how to set up, conduct, and analyze large simulation studies withthe new R package simsalapar = simulations simplified and launched parallel. Asimulation study typically starts with determining a collection of inputvariables and their values on which the study depends, such as sample sizes,dimensions, types and degrees of dependence, estimation methods, etc.Computations are desired for all com- binations of these variables. Ifconducting these computations sequentially is too time- consuming, parallelcomputing can be applied over all combinations of select variables. The finalresult object of a simulation study is typically an array. From this array,sum- mary statistics can be derived and presented in terms of (flat contingencyor LATEX) tables or visualized in terms of (matrix-like) figures. The R packagesimsalapar provides several tools to achieve the above tasks. Warnings anderrors are dealt with correctly, various seeding methods are available, and runtime is measured. Furthermore, tools for analyzing the results via tables orgraphics are pro- vided. In contrast to rather minimal examples typically foundin R packages or vignettes, an end-to-end, not-so-minimal simulation problemfrom the realm of quantitative risk management is given. The concepts presentedand solutions provided by simsalapar may be of interest to students,researchers, and practitioners as a how-to for conducting real- istic,large-scale simulation studies in R. Also, the development of the packagerevealed useful improvements to R itself, which are available in R 3.0.0.

Parallel and other simulations in R made easy: An end-to-end study