It is shown how to set up, conduct, and analyze large simulation studies with the new R package simsalapar = simulations simplified and launched parallel. A simulation study typically starts with determining a collection of input variables 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. If conducting these computations sequentially is too time- consuming, parallel computing can be applied over all combinations of select variables. The final result object of a simulation study is typically an array. From this array, sum- mary statistics can be derived and presented in terms of (flat contingency or LATEX) tables or visualized in terms of (matrix-like) figures. The R package simsalapar provides several tools to achieve the above tasks. Warnings and errors are dealt with correctly, various seeding methods are available, and run time is measured. Furthermore, tools for analyzing the results via tables or graphics are pro- vided. In contrast to rather minimal examples typically found in R packages or vignettes, an end-to-end, not-so-minimal simulation problem from the realm of quantitative risk management is given. The concepts presented and 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 package revealed useful improvements to R itself, which are available in R 3.0.0.
Hofert, M., & Mächler, M. (2016). Parallel and Other Simulations in R Made Easy: An End-to-End Study . Journal of Statistical Software, 69(4). https://doi.org/10.18637/jss.v069.i04