References
- Balocchi, C., Deshpande, S. K., George, E. I., and Jensen, S. T. (2023), “Crime in Philadelphia: Bayesian Clustering with Particle Optimization,” Journal of the American Statistical Association, 118, 818–829. DOI: 10.1080/01621459.2022.2156348.
- Begley, C. G., and Ioannidis, J. P. (2015), “Reproducibility in Science: Improving the Standard for Basic and Preclinical Research,” Circulation Research, 116, 116–126. DOI: 10.1161/CIRCRESAHA.114.303819.
- Cacioppo, J. T., Kaplan, R. M., Krosnick, J. A., Olds, J. L., and Dean, H. (2015), “Social, Behavioral, and Economic Sciences Perspectives on Robust and Reliable Science,” Report of the Subcommittee on Replicability in Science Advisory Committee to the National Science Foundation Directorate for Social, Behavioral, and Economic Sciences, 1.
- Gao, L. L., Bien, J., and Witten, D. (2022), “Selective Inference for Hierarchical Clustering,” Journal of the American Statistical Association, 119, 332–342. DOI: 10.1080/01621459.2022.2116331.
- Goodman, S. N., Fanelli, D., and Ioannidis, J. P. (2016), “What Does Research Reproducibility Mean?,” Science Translational Medicine, 8, 341ps12. DOI: 10.1126/scitranslmed.aaf5027.
- Gundersen, O. E. (2021), “The Fundamental Principles of Reproducibility,” Philosophical Transactions of the Royal Society A, 379, 20200210.
- Open Science Collaboration. (2015), “Estimating the Reproducibility of Psychological Science,” Science, 349, aac4716. DOI: 10.1126/science.aac4716.
- Willis, C., and Stodden, V. (2020), “Trust but Verify: How to Leverage Policies, Workflows, and Infrastructure to Ensure Computational Reproducibility in Publication,” Harvard Data Science Review, 2. Available at https://hdsr.mitpress.mit.edu/pub/f0obb31j. DOI: 10.1162/99608f92.25982dcf.