Structure for Organizing Monte Carlo Simulation Designs
Details
Provides tools to help organize Monte Carlo simulations in R. The package
controls the structure and back-end of Monte Carlo simulations
by utilizing a general generate-analyse-summarise strategy. The functions provided control common
simulation issues such as re-simulating non-convergent results, support parallel
back-end computations with proper random number generation within each simulation
condition,
save and restore temporary files,
aggregate results across independent nodes, and provide native support for debugging.
The primary function for organizing the simulations is runSimulation, while
for array jobs submitting to HPC clusters (e.g., SLURM) see runArraySimulation
and the associated package vignettes.
For an in-depth tutorial of the package please refer to
Chalmers and Adkins (2020; doi:10.20982/tqmp.16.4.p248
).
For an earlier didactic presentation of the package users can refer to Sigal and Chalmers
(2016; doi:10.1080/10691898.2016.1246953
). Finally, see the associated
wiki on Github (https://github.com/philchalmers/SimDesign/wiki)
for other tutorial material, examples, and applications of SimDesign to real-world simulations.
References
Chalmers, R. P., & Adkins, M. C. (2020). Writing Effective and Reliable Monte Carlo Simulations
with the SimDesign Package. The Quantitative Methods for Psychology, 16(4), 248-280.
doi:10.20982/tqmp.16.4.p248
Sigal, M. J., & Chalmers, R. P. (2016). Play it again: Teaching statistics with Monte
Carlo simulation. Journal of Statistics Education, 24(3), 136-156.
doi:10.1080/10691898.2016.1246953
Author
Phil Chalmers rphilip.chalmers@gmail.com