## Introduction

In recent years, there has been a proliferation of software packages that provide functionality for analysis of social network data. These have largely been driven by the computational needs to analyse and interpret affiliation data in sociology, where data sets can be collected with high resolution and certainty. However, studying social behaviour in non-human animals entails much greater uncertainty in the probability that a dyad exists, and the measured strength of that connection. This has spawned extensive literature, in particular when testing for statistical significance and non-randomness (Whitehead 1997; Bejder *et al*. 1998; Croft *et al*. 2008; Whitehead 2008; Croft *et al*. 2011). Yet, there remains a general lack of simple to use tools in R (R Development Core Team 2012) that implement methods to perform the specialized analyses on sets of observed co-occurrences of individuals in animal groups.

The jump from analysing high-resolution networks, as typically achievable in human social networks, to networks comprising high levels of uncertainty is one of the largest barriers to robust application of social networks in animal behaviour. Croft *et al*. (2011) provide a comprehensive review outlining the reasons why standard methods, particularly those based on node-based permutations, are not suitable. The need for specialized methods for analyses in this subject was rapidly addressed by statisticians and biologists, culminating in the package *SOCPROG* (Whitehead 2009) that provides routines for many complex analyses. However, numerous studies are still published using packages such as *UCINET* (Borgatti *et al*. 2002) that provide out-of-the-box analyses but typically violate many of the underlying assumptions from data sampling when calculating significance in animal social networks (Croft *et al*. 2011). For example, social networks from human data generally assume that all individuals are equally likely to be observed at all times. Here, I describe a package that provides routines for several specialized tests based on data describing individual membership in groups. The *asnipe* package provides these routines in the statistical environment R that enables the results of these routines to be directly integrated with a wide range of social network packages for generating statistics on the inferred social network. By providing these routines in the R environment, I hope to bridge an existing gap in statistical tools and enable more robust use of social networks in animal behaviour research.