# Snowboot: Bootstrap Methods for Network Inference

## TL;DR

Snowboot introduces R implementations of bootstrap methods for network inference, providing flexible, data-driven tools for analyzing complex networks across various fields, validated through simulations and real-world applications.

## Contribution

First R package implementing vertex and patchwork bootstrap methods for network inference, enhancing nonparametric analysis capabilities.

## Key findings

- Effective in diverse network types
- Validated through extensive simulations
- Useful for real-world network analysis

## Abstract

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.