Package index

Specify the world

Describe the data-generating process the study will sample from.

pa_var()

Declare a variable of the data-generating process

ape_dgp()

Specify the data-generating process for an APE/AIE power analysis

ape_dgp_empirical()

Specify a DGP with covariates resampled from pilot data

ape_dgp_from_fit()

Specify a DGP from a fitted pilot model

ape_dgp_panel()

Specify a panel DGP: correlated random effects probit

ape_dgp_iv()

Specify an IV DGP: endogenous focal, control-function probit

Pin the effect

Solve coefficients from targets in APE/AIE units.

set_ape()

Pin the true APE of a DGP (inversion step)

set_aie()

Pin the true AIE of a moderated DGP (inversion step)

true_ape()

Recompute the true APE implied by a pinned DGP

true_aie()

Recompute the true AIE implied by a pinned DGP

Power

Simulated power, curves, required sample sizes, detectable effects.

ape_power()

Simulated power for an APE/AIE claim at a given sample size

ape_curve() plot(<powerape_curve>)

Power curve over a grid of sample sizes

ape_n()

Required sample size for a target power

ape_mde()

Minimum detectable APE/AIE at a given sample size

Justified error rates

Alpha by sample-size rule or by error costs, from one stored simulation.

power_at()

Power at other error rates from one stored simulation

ape_alpha() plot(<powerape_alpha>)

Justify the error rate: minimize or balance weighted Type I and Type II errors

alpha_frontier()

Closed-form justified alpha at the detectability frontier

Robustness and reporting

ape_robust()

Robustness sweep over contextual assumptions (worst-case power and n_max)

power_statement()

Render a power analysis as a citable methods paragraph