Tools for fitting, diagnosing, and analyzing tie-oriented and
    actor-oriented relational event models, under both frequentist and Bayesian
    approaches. The package supports tie-oriented modeling (Butts, 2008,
    <doi:10.1111/j.1467-9531.2008.00203.x>) and an actor-oriented modeling
    framework (Stadtfeld et al., 2017, <doi:10.15195/v4.a14>),
    with additional model diagnostics and goodness-of-fit tools. 
    Interfaces to estimation backends provide a range of extensions: random-effects (frailty)
    relational event models capturing sender, receiver, and dyadic heterogeneity
    (Juozaitiene & Wit 2024, <doi:10.1007/s11336-024-09952-x>;
    Mulder & Hoff, 2024, <doi:10.1214/24-AOAS1885>), finite mixture
    and dyadic latent class models for unobserved dyadic heterogeneity
    (Lakdawala et al., 2026, <doi:10.1016/j.socnet.2026.06.006>), penalized estimation via the lasso,
    ridge, and elastic net (Tibshirani, R., 1996,
    <doi:10.1111/j.2517-6161.1996.tb02080.x>; Karimova et al., 2023, <doi:10.1016/j.socnet.2023.02.006>),
    and approximate Bayesian regularization (Karimova et al., 2025, <doi:10.1016/j.jmp.2025.102925>). Modeling
    of events with a duration is also supported (Lakdawala et al., 2026, <doi:10.48550/arXiv.2602.21000>) and
    moving window relational event models (Mulder & Leenders, 2019,
    <doi:10.1016/j.chaos.2018.11.027>; Meijerink et al., 2023, <doi:10.1371/journal.pone.0272309>).

Model based simulation of dynamic networks under tie-oriented (Butts, C., 2008, <doi:10.1111/j.1467-9531.2008.00203.x>) and actor-oriented (Stadtfeld, C., & Block, P., 2017, <doi:10.15195/v4.a14>) relational event models. Supports simulation from a variety of relational event model extensions, including temporal variability in effects, heterogeneity through dyadic latent class relational event models (DLC-REM), random effects, blockmodels, and memory decay in relational event models (Lakdawala, R., 2024 <doi:10.48550/arXiv.2403.19329>). The development of this package was supported by a Vidi Grant (452-17-006) awarded by the Netherlands Organization for Scientific Research (NWO) Grant and an ERC Starting Grant  (758791).