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 set of maximum likelihood estimation routines for panel network autocorrelation models. The package contains functionality for fixed and random effects panel network autocorrelation model, in addition to dynamic panel network autocorrelation that contains time lags of the response and networks. Moreover, the package allows for multiple networks to be fitted. For information on the types of models estimated in this package, please see Anselin (1988) <doi:10.1007/978-94-015-7799-1>, Cook et al. (2023) <doi:10.1017/S0003055422000272>, Hays et al. (2010) <doi:10.1016/j.stamet.2009.11.005>, Lee and Yu (2012) <doi:10.1111/j.1468-2354.2012.00724.x>, Millo (2014) <doi:10.1016/j.csda.2013.07.024>, Millo and Piras (2012) <doi:10.18637/jss.v047.i01>, and Wang and Yu (2015) <doi:10.1016/j.econlet.2015.01.021>.

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>.