Publications

Below you can find the main publications pertaining to powerly:

• Constantin, M. A., Schuurman, N. K., & Vermunt, J. K. (2023). A General Monte Carlo Method for Sample Size Analysis in the Context of Network Models. Psychological Methods. https://doi.org/10.1037/met0000555

  The network approach to psychology is an increasingly popular framework for studying interactions among variables. As the field matures and psychological network modeling becomes more prevalent, there is an increasing need to aid researchers with a network approach in mind that plan to collect data. In this paper, we introduce a general method for performing sample size analysis in the context of network models. The method takes the form of a three-step algorithm designed to find an optimal sample size value given a hypothesized network, an outcome measure (e.g., sensitivity), and a statistic of interest (e.g., power). It starts with a Monte Carlo simulation step for computing the outcome measure and the statistic at various sample sizes. It continues with a curve-fitting step for interpolating the statistic. The final step employs bootstrapping to account for the uncertainty around the interpolated curve. The method is implemented in the form of an R package called powerly, freely available on GitHub and CRAN.

• Constantin, M. A., Schuurman, N. K., & Vermunt, J. K. (2025). A General Framework for Sample Size Analysis [Manuscript in preparation]. Methodology and Statistics, Tilburg University.

  With the release of powerly v2.0.0 we go beyond just applying the method in Constantin et al. (2023) to network models. We provide a sophisticated system that can be intuitively extended to any statistical model and research question.

You may also be interested in:

• Constantin, M. (2025). boterview: A Large Language Model Interview Toolkit for Social Science Experiments [Manuscript in preparation]. Methodology and Statistics, Tilburg University. https://boterview.dev

  boterview is a Python package that enables social science researchers to easily deploy chatbot-based interviews with customizable protocols and randomized condition assignment. Complex study designs can be easily set up without programming knowledge beyond editing a TOML (i.e., plain text) configuration file and providing interview content in Markdown files.

• Constantin, M. A. (2025). parabar: A Lightweight Framework for Parallelizing R Code With Asynchronous and Decoration Semantics [Manuscript in preparation]. Methodology and Statistics, Tilburg University. https://parabar.mihaiconstantin.com

  parabar is a package designed to provide a simple interface for executing tasks in parallel, while also providing functionality for tracking and displaying the progress of the tasks. This package is aimed at two audiences: (1) end-users who want to execute a task in parallel in an interactive R session and track the execution progress, and (2) R package developers who want to use parabar as a solution for parallel processing in their packages. The parabar can also be used in conjunction with the accompanying doParabar R package to enable foreach semantics.