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: |
1.8.6 |
| 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:
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