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:

Reference manual:

powerly.pdf

Downloads:

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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