Augmented Modelling of Relational Events

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) .

amorem

amorem logo

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