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>.

Functions for assessing the replication/preservation of a network 
  module's topology across datasets through permutation testing; Ritchie et al. 
  (2015) <doi: 10.1016/j.cels.2016.06.012>.

Statistical entropy analysis of network data as introduced by Frank and Shafie (2016) <doi:10.1177/0759106315615511>, and in a forthcoming book by Nowicki, Shafie and Frank (2022).

Predicting the structure of a graph including new nodes and edges using
    a time series of graphs. Flux balance analysis, a linear and integer programming 
    technique used in biochemistry is used with time series prediction methods to 
    predict the graph structure at a future time point 
    Kandanaarachchi (2024) <doi:10.48550/arXiv.2401.04280>.