The challenge | A shared generator | Determinism in R | The same design in Python | The analysis layer | Why this matters
Package: pilotr 0.3.1
pilotr: Simulate Experimental and Behavioural Data from a Portable Design Specification
Generative simulation of experimental and behavioural data sets from a portable JavaScript Object Notation (JSON) design specification shared with the 'Python' package of the same name. Supports user-specified fixed effect sizes, crossed by-subject and by-item random intercepts and slopes, predictors measured with error, realistic response families (Gaussian, lognormal, shifted lognormal, ex-Gaussian, Bernoulli, Poisson, ordinal and Beta), and simulation-based power and precision-based design analysis, including the Type S and Type M errors of Gelman and Carlin (2014) <doi:10.1177/1745691614551642> and a region of practical equivalence. A shared cross-language random-number generator means that, given the same specification and seed, the R and 'Python' implementations produce identical data: exactly for the Gaussian family and for any family with rounding set, and to within the last unit in the last place for families applying a transcendental function to the linear predictor, whose rounding the IEEE-754 standard does not fix.
Authors:
pilotr_0.3.1.tar.gz
pilotr_0.3.1.zip(r-4.7-any)pilotr_0.3.1.zip(r-4.6-any)pilotr_0.3.1.zip(r-4.5-any)
pilotr_0.3.1.tgz(r-4.6-any)pilotr_0.3.1.tgz(r-4.5-any)
pilotr_0.3.1.tar.gz(r-4.7-any)pilotr_0.3.1.tar.gz(r-4.6-any)
pilotr_0.3.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
pilotr/json (API)
| # Install 'pilotr' in R: |
| install.packages('pilotr', repos = c('https://pablobernabeu.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/pablobernabeu/pilotr/issues
Pkgdown/docs site:https://pablobernabeu.github.io
Last updated from:6e838c4fdf. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel | OK | 184 | ||
| source / vignettes | OK | 223 | ||
| linux-release | OK | 147 | ||
| macos-release | OK | 156 | ||
| macos-oldrel | OK | 144 | ||
| windows-devel | OK | 115 | ||
| windows-release | OK | 82 | ||
| windows-oldrel | OK | 99 | ||
| wasm-release | OK | 141 |
Exports:as241brms_bridgebuild_speccalibrate_responsedefault_response_namedesign_conditionsgenerate_design_analysisgenerate_r_scriptload_specmake_rngmodel_datamodel_formulapilotr_examplepower_curve_mixedpower_designpower_mixedprecision_curveprecision_designreplicate_seedsresponse_variancerun_appsimulate_designsolve_curvespec_from_modelspec_jsonsweep_spectarget_nvalidate_spec
Dependencies:jsonlite
Cross-language reproducibility
Last update: 2026-09-12
Started: 2026-09-12
Getting started with pilotr
One specification, three interfaces | Describe a design | Simulate | Reproducible by construction | Export a self-contained script | Where to go next
Last update: 2026-09-12
Started: 2026-09-12
Power and design analysis
Two-group Gaussian | Crossed mixed-effects designs | A power curve | The sample size the curve implies | Parallel execution | A bridge to the Bayesian workflow | See also
Last update: 2026-09-12
Started: 2026-09-12
Precision and ROPE design analysis
The idea | A worked design | Precision at a fixed N | Sweeping sample size | See also
Last update: 2026-09-12
Started: 2026-09-12
Response families
Gaussian | Shifted lognormal (reaction times) | Lognormal (positive continuous) | Bernoulli (accuracy) | Poisson (counts) | Ordinal (Likert) | Beta (proportions) | Choosing a family | Writing the specification directly
Last update: 2026-09-12
Started: 2026-09-12
The no-code app
Two ways to run it | What the app does | One specification, three interfaces
Last update: 2026-09-12
Started: 2026-09-12
Worked examples
Last update: 2026-09-12
Started: 2026-09-12
