pilotr ships a point-and-click application over the same design specification that the R and Python packages consume. It is a thin client. Every control writes into the portable JSON specification, which you can download and run unchanged in either package to obtain identical data.
Two ways to run it
One way is to run it on your own machine. With the package installed, launch the app locally:
pilotr::run_app()Each user runs their own R process, so a heavy power run never blocks anyone else, and simulations use all of your local cores.
The other way needs nothing installed. A serverless build runs entirely in the browser through WebAssembly, with no server and nothing uploaded. Try it at the no-code app. It covers the light path: build a design, simulate, inspect the data, estimate two-group Gaussian power with the sample size its curve solves to, and export.
What the app does
Describe the design in the left panel: the sample sizes, the factor and its two levels, the fixed intercept and effect, a response family, and for crossed designs the random-effect standard deviations. Then read the tabs. ‘Design spec’ holds the exact JSON specification, the portable source of truth, and ‘Data’ the simulated data set. ‘Summary & plot’ gives group summaries and a plot. ‘Power’ reports simulation-based two-group Gaussian power with the Type S and Type M errors, together with a power curve over sample size and the sample size at which that curve reaches 0.80, with a confidence interval on it. ‘R script’ emits a self-contained, reproducible R script, which the installed app can also verify by running it in a clean R session and confirming bit-for-bit reproduction.
Changing the response family resets the intercept and effect to sensible values for that family’s scale, so a point-and-click design stays valid. The advanced ‘paste a JSON spec’ box accepts designs beyond the point-and-click controls, such as continuous predictors, interactions and nesting.
One specification, three interfaces
The app does not own the design. The specification does. Download the
specification (.json) or the data (.csv), and
run the specification unchanged in R with simulate_design()
or in Python with pilotr.simulate() for identical data. The
heavier analyses stay with the installed packages: crossed mixed-effects
power in R, through lme4 as the reference backend, and in
Python, and precision/ROPE design analysis in R. Whichever interface you
use, the specification you build in the app drives all three.