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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, analyses of bundled event logs, validation experiments), and the complete function reference are all there.

What the package covers

• Simulation — simulate_relational_events() with an exact Gillespie kernel or an approximate tau-leap kernel; simulators for undirected and directed hyper-events.
• Data intake — standardize_event_log(), sample_non_events() (case-control sampling), attach_static_covariates(), simulate_actor_covariates().
• Statistics — endogenous_features() (timing, closure and balance effects of Juozaitienė & Wit) and hyperedge_features().
• Estimation — rem() with three backends: clogit (conditional-logistic partial likelihood), gam (one-control binomial form with smooth, time-varying and random effects: tv(), nl(), tvnl(), re()), and nn (neural or additive-spline scorer; nn_uncertainty() gives bootstrap bands, and nn_control(engine = "torch") is optional).
• Model comparison and fit — compare_models() and its _global / _smooth variants; martingale-residual goodness of fit (gof_*(), martingale_residuals()).
• Data — nine documented event logs (data/, sources in data-raw/).

Development

devtools::document()        # regenerate man/ and NAMESPACE
devtools::test()            # tests/testthat
devtools::check(cran = TRUE)
pkgdown::build_site()       # local preview of the docs site

CI runs R CMD check on GitHub Actions (.github/workflows/R-CMD-check.yaml) and, on every push to main, builds the pkgdown site and deploys it to the gh-pages branch, so the site needs no manual deploy. Web-only guides live in vignettes/articles/ and are excluded from the CRAN build.

Remotes. origin is GitHub (public; CRAN URL, issues, the docs site). forge is the private Forgejo working copy (forge:pancho/amorem).

Status

• CRAN: 1.1.0, published 2026-10-17. The main branch is 1.2.0 (new gof_rem(), thirteen new datasets, the data catalogue), not yet submitted.
• Planned: a hyper-event case-control sampler with history-aware masking, so that hyper-event data can be fitted with rem(); resolving the S3 class clash on "rem" with relevent::rem() and redeem::rem().
• Open check: the coxme path with two random-effect axes (random_effects = c("sender", "receiver")) has not been re-validated since the stratification fix.
• Companion paper: the software paper describing version 1.0.0 is being revised outside this repository (host air, ~/System/Research/amorem-revision-20260812/). The local paper/ folder is gitignored.

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.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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