The simssd R package uses simulation to do sample size determination (SSD), the process of estimating the sample size needed for a statistical study, and power computation for fixed effects in multilevel linear regression models. It has a focus on improving computational speed.

The predecessor to simssd was developed to support my PhD research, A faster simulation approach to sample size determination for random effect models, at the Centre for Multilevel Modelling (University of Bristol).

It extended ideas arising from the MLPowSim software written by William Browne and Mousa Golalizadeh.

I gratefully acknowledge funding provided for my PhD via UK Economic and Social Research Council (ESRC) grant number ES/H044094/1.

My thanks to the late Professor Jon Rasbash for getting the original project off the ground as well as Professor William Browne, Professor Fiona Steele, CBE, Professor Debora Price and the late Professor Harvey Goldstein for their invaluable guidance and support.

The MLPowSim manual by William Browne, Mousa Golalizadeh and Richard Parker contains a number of motivating examples.

The software design of simssd draws on some ideas from Chalmers & Adkins:
Chalmers RP, and Adkins, MC (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.

In an ongoing way, tools provided by Hadley Wickham and his colleagues at Posit Software, PBC (formerly RStudio, PBC) enable me to develop much higher quality software in R than I otherwise would have been able to. Thank you Hadley & others at Posit   🙂