Modelling human and non-human animal network data in R using STRAND

Abstract

AbstractThere have been recent calls for wider application of generative modelling approaches in applied social network analysis. These calls have been motivated by the limitations of contemporary empirical frameworks, which have generally relied on post hoc permutation methods that do not actively account for interdependence in network data. At present, however, it remains difficult for typical end-users—e.g., field researchers—to apply generative network models, as there is a dearth of openly available software packages that make application of such methods as simple as other, permutation-based methods.Here, we outline the STRAND R package, which provides a suite of generative models for Bayesian analysis of human and non-human animal social network data that can be implemented using simple, base R syntax.To facilitate ease-of-use, we provide a tutorial demonstrating how STRAND can be used to model binary, count, or proportion data using stochastic blockmodels, social relations models, or a combination of the two modelling frameworks.