How animals interact and develop social relationships in face of sociodemographic and ecological pressures is of great interest. New methodologies, in particular Social Network Analysis (SNA), allow us to elucidate these types of questions. However, the different methodologies developed to that end and the speed at which they emerge make their use difficult. Moreover, the lack of communication between the different software developed to provide an answer to the same/different research questions is a source of confusion. The R package 'Animal Network Toolkit' (ANT) was developed with the aim of implementing in one package the different social network analysis techniques currently used in the study of animal social networks. Hence, ANT is a toolkit for animal research allowing among other things to: 1) measure global, dyadic and nodal networks metrics; 2) perform data randomization: pre- and post-network (node and link permutations); 3) perform statistical permutation tests as correlation test, t-test, General Linear Model, General Linear Mixed Model, deletion simulation, Matrix TauKr correlations , MRQAP. The package is partially coded in C++ using the R package Rcpp for an optimal coding speed. The package gives researchers a workflow from the raw data to the achievement of statistical analyses, allowing for a multilevel approach: from the individual's position and role within the network, to the identification of interactional patterns, and the study of the overall network properties. Furthermore, ANT also provides a guideline on the SNA techniques used: 1) from the appropriate randomization technique according to the data collected; 2) to the choice, the meaning, the limitations and advantages of the network metrics to apply, 3) and the type of statistical tests to run. The ANT project is multi-collaborative, aiming to provide access to advanced social network analysis techniques and to create new ones that meet researchers' needs in future versions. The ANT project is multi-collaborative, aiming to provide access to advanced social network analysis techniques and to create new ones that meet researchers' needs in future versions.
|Author||Sosa Sebastian, Puga-Gonzalez Ivan, Hu Feng He, Peng Zhang, Xiaohua Xie, Cédric Sueur|
|Maintainer||Sosa Sebastian <[email protected]>, Puga-Gonzalez Ivan <[email protected]>|
|License||GPL (>= 3)|
|URL||www.s-sosa.com/softwares or https://github.com/SebastianSosa/Animal-Network-Toolkit|
|Package repository||View on GitHub|
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