The methods are widely applicable to other DNA sequence clustering problems. Someone may obtain contradicting results with a new algorithm. In such a case, rerunning our scripts on the same or new data may help elucidate the source of the differences between the results.
In this paper, an R package was used to improve the reproducibility of the analyses. Therefore, it would be good to know to what extent this works. The R package includes the following analyses: (1) data trimming and preparation, (2) descriptive statistics, (3) reliability and correlations, (4) t-tests and Bayesian t-tests, (5) latent-change models (structural equation modeling approach), and (6) multiverse analyses. Furthermore, all deidentified data, experiment codes, research materials, and results are publicly accessible on the Open Science Framework (OSF) at https://osf.io/ngfxv. The study’s design and the analyses were pre-registered on OSF. The preregistration can be accessed at https://osf.io/ tywu7.