Pilot a study before you run it. Describe the design you plan to collect, with its groups, conditions, sample sizes, effect sizes and outcome family, and pilotr generates the data that design would produce, then estimates the power and the precision it would buy. Twin, feature-parity packages read one portable JSON specification and draw from one random-number stream, so a given specification and seed produce the same data in either language, bit for bit apart from a documented tolerance of a few units in the last place where an unrounded response family applies exp() to the linear predictor.
remotes::install_github("pablobernabeu/pilotr", subdir = "r/pilotr").
pip install pilotr.
The same workflow without a line of code and without an installation. The app runs the R package itself through webR, entirely on your machine, so no design and no data leave the browser. Build a specification with the controls, look at the simulated data, estimate power over a range of sample sizes, and download the specification, the data and the R script that reproduces the run.
The portable specification and the shared random-number contract are written up in spec/SPEC.md, which is what the two packages and the app are all built against.