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.
Version: 1.8.6 Imports: R6, progress, parallel, splines2, quadprog, osqp, bootnet, qgraph, ggplot2, rlang, mvtnorm, patchwork Suggests: testthat (≥ 3.0.0) Published: 2022-09-09 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 + file LICENSE URL: https://powerly.dev NeedsCompilation: no Citation: powerly citation info Materials: README NEWS CRAN checks: powerly results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=powerly to link to this page.
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