This R package allows the determination of some distributions of the voters' power when passing laws in weighted voting situations.

We extend 'dplyr' and 'fuzzyjoin' join functions with
    features to preprocess the data, apply various data checks, and deal with
    conflicting columns.

An implementation of the generalized power analysis for the local average treatment effect (LATE), proposed by Bansak (2020) <doi:10.1214/19-STS732>. Power analysis is in the context of estimating the LATE (also known as the complier average causal effect, or CACE), with calculations based on a test of the null hypothesis that the LATE equals 0 with a two-sided alternative. The method uses standardized effect sizes to place a conservative bound on the power under minimal assumptions. Package allows users to recover power, sample size requirements, or minimum detectable effect sizes. Package also allows users to work with absolute effects rather than effect sizes, to specify an additional assumption to narrow the bounds, and to incorporate covariate adjustment.

An implementation of maximum likelihood estimators for a
    variety of heavy tailed distributions, including both the discrete and
    continuous power law distributions. Additionally, a goodness-of-fit
    based approach is used to estimate the lower cut-off for the scaling
    region.

An implementation of the sample size computation method for network
    models proposed by Constantin et al. (2023) <doi:10.1037/met0000555>.
    The implementation takes the form of a three-step recursive algorithm
    designed to find an optimal sample size given a model specification and a
    performance measure of interest. It starts with a Monte Carlo simulation
    step for computing the performance measure and a statistic at various sample
    sizes selected from an initial sample size range. It continues with a
    monotone curve-fitting step for interpolating the statistic across the entire
    sample size range. The final step employs stratified bootstrapping to quantify
    the uncertainty around the fitted curve.