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.
