Performs network-based source estimation. Different approaches are available: effective distance median, recursive backtracking, and centrality-based source estimation. Additionally, we provide public transportation network data as well as methods for data preparation, source estimation performance analysis and visualization.

A graph visualization engine that emphasizes on 
  aesthetics at the same time providing default parameters that yield
  out-of-the-box-nice visualizations. The package is built on top of
  'The Grid Graphics Package' and seamlessly work with 'igraph' and 
  'network' objects.

Network Pre-Processing and normalization. Methods for normalizing graphs, including Chua normalization, Laplacian normalization, Binary magnification, min-max normalization and others. Methods to sparsify adjacency matrices. Methods for graph pre-processing and for filtering edges of the graph.

Implements methods for centrality related analyses of networks. 
    While the package includes the possibility to build more than 20 indices, 
    its main focus lies on index-free assessment of centrality via partial 
    rankings obtained by neighborhood-inclusion or positional dominance. These 
    partial rankings can be analyzed with different methods, including 
    probabilistic methods like computing expected node ranks and relative 
    rank probabilities (how likely is it that a node is more central than another?).
    The methodology is described in depth in the vignettes and in
    Schoch (2018) <doi:10.1016/j.socnet.2017.12.003>.

Regress network responses (both directed and undirected) onto covariates of interest that may be actor-, relation-, or network-valued. In addition, compute principled variance estimates of the coefficients assuming that the errors are jointly exchangeable. Missing data is accommodated. Additionally implements building and inversion of covariance matrices under joint exchangeability, and  generates random covariance matrices from this class. For more detail on methods, see Marrs, Fosdick, and McCormick (2017) <arXiv:1701.05530>.