Description
Augmented Modelling of Relational Events.
Description
Utilities for simulating and prototyping relational event models, including helpers to generate dynamic event sequences and covariate processes for sender and receiver sets. The endogenous-effect and case-control estimation machinery follows Juozaitiene and Wit (2024) <doi:10.1093/jrsssa/qnae132>.
README.md
amorem
amorem is an R package for simulation and inference in relational event models (REMs) and relational hyper event models (RHEMs) — dynamic network data in continuous time.
📖 Documentation
Everything lives on the documentation site:
→ franciscorichter.github.io/amorem
Installation, the quick-start tutorial, the full guide set (simulation, endogenous catalogue, estimation, hyperedge models, datasets, real-data analysis, validation experiments), and the complete function reference are all there.
References
Methodological background for the models implemented in amorem:
- Bianchi, F., Filippi-Mazzola, E., Lomi, A., & Wit, E. C. (2024). Relational Event Modeling. Annual Review of Statistics and Its Application, 11, 297–319. https://doi.org/10.1146/annurev-statistics-040722-060248
- Boschi, M., & Wit, E. C. (2026). Introduction to Relational Event Modelling. arXiv:2604.07063. https://arxiv.org/abs/2604.07063
- Juozaitienė, R., & Wit, E. C. (2024). Relational event modelling with timing, closure and actor-heterogeneity effects. Journal of the Royal Statistical Society Series A, 188(4). https://doi.org/10.1093/jrsssa/qnae132
- Boschi, M., Lerner, J., & Wit, E. C. (2025). Relational hyper event models with time-varying non-linear effects. arXiv:2509.05289. https://arxiv.org/abs/2509.05289
License
MIT, see LICENSE.