Changelog • pilotr

pilotr 0.2.1

Released 2026-07-23.

A package-level help topic, ?pilotr, introduces the workflow and groups every export by the stage it belongs to.
The reference examples that cut the replicate count so they run quickly now say so, and each n_sims argument documents the Monte Carlo error and a usable recommendation for real planning.
The precision_curve() example passes a region of practical equivalence clearly inside the effect it declares. It previously fell back to a default equal to that effect, the configuration the vignette warns against.

model_formula() binds the global environment, so a formula prints without the environment tag that varied between builds and leaked into the rendered reference pages.

Released 2026-07-15.

pilotr_example() lists the design specifications shipped with the package, one per design family, and returns the path to each for load_spec(). They are the same JSON files that drive the Python package and the no-code app, and the Python package gains the same function.
The eight per-family specifications now travel inside the package itself, so an installed copy can load them without a checkout of the repository.

Initial release.

simulate_design() generates a data set from a portable JSON design specification shared with the Python package of the same name, with build_spec() composing a specification from a flat list of design inputs, load_spec() reading one authored elsewhere (such as one downloaded from the no-code app) and spec_json() serialising it back to JSON.
Response families: Gaussian, lognormal, shifted lognormal, Bernoulli, Poisson, ordinal and Beta.
A shared cross-language random-number generator (make_rng(), with the AS 241 inverse normal in as241()) makes the simulated data bit-identical to the Python package’s given the same specification and seed.
A per_subject value below 1 or above the number of items is rejected with a clear error; the Python package raises on the same inputs.

Simulation-based power and design analysis with power_design(), power_mixed() and power_curve_mixed(), reporting power alongside the Type S and Type M errors of Gelman and Carlin (2014). power_curve_mixed() returns an n_converged column alongside power and type_m, so the Monte Carlo standard error can be computed over the converged replicates.
Precision and region-of-practical-equivalence (ROPE) design analysis with precision_design() and precision_curve().
Every analysis seeds the shared RNG as seed + (replicate - 1), the same indexed-seed rule across all power and precision functions, the HPC array script and both languages.
power_mixed() and power_curve_mixed() reject a specification without an item unit with a clear error instead of returning NaN power.
All simulation-based power and precision analyses take a workers argument that spreads the Monte Carlo replicates across local cores with base R’s parallel package. Because every replicate seeds the shared RNG from its own index, any worker count returns results identical to a serial run, and the sweep functions create their worker pool once and reuse it across all sample sizes.

model_formula() and model_data() derive the lmer formula and modelling data frame implied by a specification, and brms_bridge() derives a brms formula, family and priors, mapping every response family to its brms counterpart (including Beta()).
generate_r_script() writes a self-contained, reproducible R script from a specification.
A no-code Shiny application over the same specification, launched with run_app().

citation("pilotr") builds its version note from the package metadata, so it cannot drift from DESCRIPTION.