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.
Package details |
|
---|---|
Author | Mihai Constantin [aut, cre] (<https://orcid.org/0000-0002-6460-0107>) |
Maintainer | Mihai Constantin <mihai@mihaiconstantin.com> |
License | MIT + file LICENSE |
Version | 1.8.6 |
URL | https://powerly.dev |
Package repository | View on CRAN |
Installation |
Install the latest version of this package by entering the following in R:
|
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.