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.">

powerly: Sample Size Analysis for Psychological Networks and More (original) (raw)

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 ORCID iD [aut, cre]
Maintainer: Mihai Constantin
BugReports: https://github.com/mihaiconstantin/powerly/issues
License: MIT + file
URL: https://powerly.dev
NeedsCompilation: no
Citation: powerly citation info
Materials: README, NEWS
CRAN checks: powerly results

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