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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 |
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 |
Reference manual: | powerly.pdf |
Package source: | powerly_1.8.6.tar.gz |
Windows binaries: | r-devel: powerly_1.8.6.zip, r-release: powerly_1.8.6.zip, r-oldrel: powerly_1.8.6.zip |
macOS binaries: | r-release (arm64): powerly_1.8.6.tgz, r-oldrel (arm64): powerly_1.8.6.tgz, r-release (x86_64): powerly_1.8.6.tgz, r-oldrel (x86_64): powerly_1.8.6.tgz |
Old sources: | powerly archive |
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