pilotr (Python)

Simulate experimental and behavioural data from a portable JSON design specification.

This is the twin of the R package of the same name, carrying its generative core. The two share the design specification and the random-number generator, so the same specification and seed produce identical data in either language wherever the two share a maths library, as on Linux. Elsewhere, a value that passes through exp(), log() or pow() can differ in its last bits, as Cross-language reproducibility sets out. Simulation, validation, the analysis model a specification implies, simulation-based power and the curve solvers are here. Precision/ROPE design analysis, the lme4 reference backend for mixed-effects power, the brms bridge, the analysis-script emitters, the helpers that build specifications, and the app launcher are in the R package alone.

Get started Try the no-code app

A design specification names the units, the factors and their contrasts, the fixed effect sizes, any random effects and a response family. From that one file pilotr draws a data set, estimates power and precision by simulation, then emits the analysis code the design implies. The same file drives the R package, the browser-based no-code app and this Python package, so a design built by pointing and clicking can be run unchanged in a script, and a design written in a script can be handed to a collaborator who has installed nothing.

The generative core is pure Python and dependency-free. scipy, statsmodels and pandas are optional extras, needed only by the power tools. The Get started guide covers installing pilotr and simulating a first data set.

Where to go next

The guides follow the workflow, from choosing a response family to sizing a study and checking that the two languages agree.

• Response families: Gaussian, lognormal, reaction times, accuracy, counts, ordinal, Beta and the ex-Gaussian.
• Worked examples: one ready-to-run design per family.
• Power and design analysis: power, power_curve and power_mixed, with solve_curve and target_n for the sample size a curve implies, and power_report for the sample-size justification.
• Cross-language reproducibility: how the same spec gives identical data in R and Python, and where the last bits can differ.
• Specification format: the JSON spec and the cross-language RNG contract.
• API reference: every public function and class.

If pilotr contributes to published work, please cite it. Its citation gives the concept DOI https://doi.org/10.5281/zenodo.21266313, which resolves to the latest release archived on Zenodo.