Package: amorem 1.2.0

amorem: Augmented Modelling of Relational Events

An integrated workflow for relational event models, in which timestamped interactions among actors arrive in continuous time and the event rate depends on the history of the network itself. Event streams are simulated by an exact Gillespie kernel or an approximate tau-leap kernel; non-events are drawn by case-control sampling in the manner of Lerner and Lomi (2020) <doi:10.1017/nws.2019.57>, or from a time-shifted copy of the process so that covariates shared by all dyads at a given time remain estimable (Lembo et al. 2025, <doi:10.1093/jrsssc/qlaf058>); the timing- and closure-based endogenous statistics of Juozaitiene and Wit (2024) <doi:10.1093/jrsssa/qnae132> are computed from that history; and one front-end fits the resulting case-control data by conditional-logistic partial likelihood, by penalised regression on its degenerate one-control binomial form (which admits smooth, time-varying and actor random effects), or by stochastic gradient descent, including the additive-spline construction of Filippi-Mazzola and Wit (2024) <doi:10.1093/jrsssc/qlae023>. Martingale-residual goodness-of-fit diagnostics are provided for the linear fit, together with simulation and statistic computation for relational hyper-events, a collection of documented event logs from published interaction networks, and a reconstructed El Nino index as a global covariate.

Authors:Francisco Richter [aut, cre], Martina Boschi [aut], Ernst C. Wit [aut], Melania Lembo [aut]