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) 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.


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install.packages("powerly")

1.5.2 by Mihai Constantin, 21 days ago


https://github.com/mihaiconstantin/powerly


Report a bug at https://github.com/mihaiconstantin/powerly/issues


Browse source code at https://github.com/cran/powerly


Authors: Mihai Constantin [aut, cre]


Documentation:   PDF Manual  


MIT + file LICENSE license


Imports R6, progress, parallel, splines2, quadprog, osqp, bootnet, qgraph, ggplot2, patchwork

Suggests testthat


See at CRAN