amorem

amorem — end-to-end simulation, sampling, feature engineering, model selection, and inference for relational event models (REMs) in R, in one consistent API.

The package consolidates four lines of recent methodological work:

• Dyadic REMs with timing / closure variants and one- or two-axis actor frailty, following Juozaitienė & Wit (2024, JRSS-A).
• Relational Hyper Event Models (RHEMs) with set-valued sender/receiver hyperedges, subset-repetition covariates, and smooth effects (linear / tv / nl / tvnl), following Boschi, Lerner & Wit (2025).
• Global covariate effects — covariates varying in time but constant across pairs — via the time-shifted partial likelihood of Lembo, Juozaitienė, Vinciotti & Wit (2025, JRSS-C).
• Cumulative martingale-residual GOF tests, following Boschi & Wit (2025, Stat & Comp 36:4).

Installation

# install.packages("remotes")
remotes::install_github("franciscorichter/amorem")

30-second tour

library(amorem)

# 1. Bundled datasets, ready to load
data(classroom_events)        # 691 events,    20 actors  (McFarland 2001)
data(social_evolution_calls)  # 439 events,    54 actors  (Madan et al. 2011)
data(radoslaw_email)          # 82,927 emails, 167 actors (Michalski et al. 2014)

# 2. Compute endogenous features post-hoc from any (sender, receiver, time) log
feat <- compute_endogenous_features(
  classroom_events, stats = c("reciprocity", "recency"))

# 3. Simulate a ready-to-fit case-1-control table with known structure ...
w <- simulate_relational_events(
  n_events = 1500, senders = LETTERS[1:8], receivers = LETTERS[1:8],
  n_controls = 1, endogenous_stats = "reciprocity_count",
  endogenous_effects = c(reciprocity_count = 0.4), wide = TRUE)

# 4. ... and fit it with the default "gam" backend
fit <- rem(~ reciprocity_count, data = w, method = "gam")
summary(fit)

What’s inside

• rem() — the unified fitter for preprocessed case-control data, with a conditional-logit backend (case-k-control) and a gam backend (case-1-control) supporting linear / tv / nl / tvnl smooth effects and re() random effects, plus summary() / coef() / plot(). widen_case_control() reshapes a long case-control log into the wide form rem() expects.
• simulate_relational_events() — exact Gillespie or τ-leap; composes exogenous, endogenous, and time-varying global covariates; with wide = TRUE emits a ready-to-fit case-1-control table.
• compute_endogenous_features() — the endogenous catalogue computed post-hoc from any (sender, receiver, time) log; via history_log it scores sampled non-events without polluting the event history.
• sample_non_events() — nested case-control sampling with appearance / citation / remove risk-set rules.
• Goodness of fit — gof_global(), gof_multivariate(), gof_auxiliary(), and pointwise martingale_residuals().
• The earlier compare_models(), compare_models_smooth() and compare_models_global() remain available (superseded by rem()).

Guides

Guide	What you’ll find
Quick start	install + simulate-and-recover in a few lines
Simulation	the dyadic mechanisms, Gillespie vs τ-leap
Endogenous catalogue	the statistic catalogue and its variant axes
Estimation	case-control sampling, the three rem() backends — clogit (linear), gam (smooth tv/nl/tvnl/re effects), nn (neural conditional logit) — model comparison, GOF
Hyperedge models	the (I, J, time) data model and RHEM simulators
Datasets	the bundled REM datasets
Real-data analysis	sender-frailty flip, smooth effect curves
Validation experiments	recovery, smooth, scaling, parity, and the neural backend (gradient check + interaction recovery)

Full per-function documentation is under Reference.

References

• Juozaitienė R., Wit E.C. (2024). It’s about time: revisiting reciprocity and triadicity in relational event analysis. JRSS-A 188(4), 1246–1262. doi:10.1093/jrsssa/qnae132.
• Boschi M., Lerner J., Wit E.C. (2025). Beyond Linearity and Time-Homogeneity: Relational Hyper Event Models with Time-Varying Non-Linear Effects. arXiv:2509.05289.
• Lembo M., Juozaitienė R., Vinciotti V., Wit E.C. (2025). Relational Event Models with Global Covariates. JRSS-C.
• Boschi M., Wit E.C. (2025). Goodness of fit in relational event models. Statistics and Computing 36(4).