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. (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.
| Version: | 1.10.0 |
| Imports: | R6,splines2,quadprog,bootnet,qgraph,parabar,ggplot2,rlang,mvtnorm,patchwork |
| Suggests: | testthat (≥ 3.0.0) |
| Published: | 2025-09-01 |
| DOI: | 10.32614/CRAN.package.powerly |
| Author: | Mihai Constantin [aut, cre] |
| Maintainer: | Mihai Constantin <mihai at mihaiconstantin.com> |
| BugReports: | https://github.com/mihaiconstantin/powerly/issues |
| License: | MIT + fileLICENSE |
| URL: | https://powerly.dev |
| NeedsCompilation: | no |
| Citation: | powerly citation info |
| Materials: | README,NEWS |
| CRAN checks: | powerly results |
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