Contains statistical methods to analyze graphs, such as graph parameter estimation, model selection based on the GIC (Graph Information Criterion), statistical tests to discriminate two or more populations of graphs (ANOGVA - Analysis of Graph Variability), correlation between graphs, and clustering of graphs. References: Takahashi et al. (2012) <doi:10.1371/journal.pone.0049949>, Futija et al. (2017) <doi:10.3389/fnins.2017.00066>, Fujita et al. (2017) <doi:10.1016/j.csda.2016.11.016>, Tang et al. (2017) <doi:10.3150/15-BEJ789>, Tang et al. (2017) <doi:10.1080/10618600.2016.1193505>, Ghoshdastidar et al. (2017) <arXiv:1705.06168>, Ghoshdastidar et al. (2017) <arXiv:1707.00833>, Cerqueira et al. (2017) <doi:10.1109/TNSE.2017.2674026>, Fraiman and Fraiman (2018) <doi:10.1038/s41598-018-23152-5>, Fujita et al. (2019) <doi:10.1093/comnet/cnz028>.
