Chimento, M.; Hoppitt, W.

A critical consequence of joining social groups is the possibility of social transmission of information from conspecifics related to novel behaviours or resources. Mathematical models of spreading have been developed leading to several established methods of inferring whether information spreads via social or individual learning. Network-based diffusion analysis (NBDA) has emerged as a leading frequentist framework for inferring and quantifying social transmission, particularly in non-human animal populations. NBDA has been extended several times to account for multiple diffusions, multiple networks, individual-level variables, and complex transmission functions. Bayesian implementations of NBDA were introduced in two prior studies. However, there is not yet a user-friendly package to implement a Bayesian NBDA, and the approach has seen limited usage by other researchers. Here, we present a unified framework for performing Bayesian analysis of social transmission using NBDA-type models, implemented in the widely used Stan programming language. We provide a user-friendly R package \"STbayes\" (ST: social transmission) for other researchers to easily use this framework. STbayes accepts user-formatted data, but can also import data directly from the existing NBDA R package. Based on the data users provide, STbayes automatically generates multi-network, multi-diffusion models that allow for individual covariates that may influence transmission, as well as individual-level varying (random) effects for any parameter. Using simulated data, we demonstrate that this model can accurately differentiate the relative contribution of individual and social learning in the spread of information through networked populations. We illustrate how incorporating upstream uncertainty about the relationships between individuals in the social group can improve model fit. We show that our framework can be used to infer complex transmission rules and describe a new parameterization of frequency-dependent transmission that is more numerically stable than prior parameterizations. Finally, we introduce support for dynamic transmission weights and a \"high-resolution\" data mode, which allows users to make use of fine-scale data collected by contemporary automated tracking methods. These extensions increase the set of contexts that this type of model may be used for.