Polyadic, or "multicast" social interaction networks arise when one sender addresses multiple receivers simultaneously. Currently available relational event models (REM) are not well suited to the analysis of polyadic because they specify rates for sets as functions dyadic covariates associated with and receiver at a time. Relational hyperevent (RHEM) address this problem by specifying hyperedg...

Natural resources models serve as important tools to support decision making by predicting environmental indicators. All model predictions have uncertainty associated with them. Model predictive uncertainty, often expressed as the confidence interval around a model prediction value, may serve as important supplementary information for assisting decision making processes. In this article, we des...

1 Immunology Group, IGMP and Centre of Excellence for Translational Medicine, University of Tartu, 50411 Tartu, Estonia 2Competence Centre of Reproductive Medicine and Biology, Tiigi 61b, 50410 Tartu, Estonia 3Department of Obstetrics and Gynecology and IGMP, University of Tartu, 51014 Tartu, Estonia 4Laboratory of Immunology, Faculty of Medicine, Université Henri Poincaré and Nancytomique, CHU...

Abstract Objectives Approaches to the study of Outlaw Motorcycle Gangs OMCGs tend focus on offending at individual level, with limited nature and extent co-offending among these affiliates. We aim examine by using relational hyper event models (RHEM) determine what additional insights can be discerned above beyond more traditional network approaches. Methods Using de-identified police recorded ...

Analyzing two-mode networks linking actors to events they attend may help uncover the structure and evolution of social networks. This classic network insight is particularly valuable in analysis data extracted from contact diaries where produce — at same time are product relations among participants. Contact comprise any number meeting a specific point time. In this paper we recall corresponde...