powerape: Power Analysis for Average Partial Effects

Simulation-based power analysis in which the effect size of interest is the average partial effect (APE) or the average interaction effect (AIE) from a nonlinear model such as probit or logit, including smallest-effect-size-of-interest (SESOI) powering via confidence-interval criteria for detection, minimum-effect, and equivalence claims (Riesthuis, 2024) and robustness sweeps over contextual assumptions (Hancock and Feng, 2026).

See also

Useful links:

• https://jespernwulff.github.io/powerape/

• https://github.com/jespernwulff/powerape

• Report bugs at https://github.com/jespernwulff/powerape/issues

Author

Maintainer: Jesper Wulff jwulff@econ.au.dk