  Complex networks are used to describe a broad range of disparate social
systems and natural phenomena, from power grids to customer segmentation to
human brain connectome. Challenges of parametric model specification and
validation inspire a search for more data-driven and flexible nonparametric
approaches for inference of complex networks. In this paper we discuss
methodology and R implementation of two bootstrap procedures on random
networks, that is, patchwork bootstrap of Thompson et al. (2016) and Gel et al.
(2017) and vertex bootstrap of Snijders and Borgatti (1999). To our knowledge,
the new R package snowboot is the first implementation of the vertex and
patchwork bootstrap inference on networks in R. Our new package is accompanied
with a detailed user's manual, and is compatible with the popular R package on
network studies igraph. We evaluate the patchwork bootstrap and vertex
bootstrap with extensive simulation studies and illustrate their utility in
application to analysis of real world networks.
