powerly: Sample Size Analysis for Psychological Networks and More

An implementation of the sample size computation method for network models proposed by Constantin et al. (2021) <doi:10.31234/osf.io/j5v7u>. 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.

Package details

AuthorMihai Constantin [aut, cre] (<https://orcid.org/0000-0002-6460-0107>)
MaintainerMihai Constantin <mihai@mihaiconstantin.com>
LicenseMIT + file LICENSE
URL https://powerly.dev
Package repositoryView on CRAN
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powerly documentation built on May 1, 2022, 5:07 p.m.