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.

Version: 1.7.2
Imports: R6, progress, parallel, splines2, quadprog, osqp, bootnet, qgraph, ggplot2, rlang, patchwork
Suggests: testthat (≥ 3.0.0)
Published: 2021-11-17
Author: Mihai Constantin ORCID iD [aut, cre]
Maintainer: Mihai Constantin <mihai at mihaiconstantin.com>
BugReports: https://github.com/mihaiconstantin/powerly/issues
License: MIT + file LICENSE
URL: https://github.com/mihaiconstantin/powerly
NeedsCompilation: no
Citation: powerly citation info
Materials: README NEWS
CRAN checks: powerly results

Documentation:

Reference manual: powerly.pdf

Downloads:

Package source: powerly_1.7.2.tar.gz
Windows binaries: r-devel: powerly_1.7.2.zip, r-release: powerly_1.7.2.zip, r-oldrel: powerly_1.7.2.zip
macOS binaries: r-release (arm64): powerly_1.7.2.tgz, r-release (x86_64): powerly_1.7.2.tgz, r-oldrel: powerly_1.7.2.tgz
Old sources: powerly archive

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