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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:
Downloads:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=powerly
to link to this page.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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