  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.
