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 a in textbook which is in progress.

Calculates network measures commonly used in Network Medicine. Measures such as the Largest Connected Component, the Relative Largest Connected Component, Proximity and Separation are calculated along with their statistical significance. Significance can be computed both using a degree-preserving randomization and non-degree preserving.

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 (2025) <doi:10.48550/arXiv.2507.05806>.

Segregation is a network-level property such that edges between 
  predefined groups of vertices are relatively less likely. Network homophily 
  is a individual-level tendency to form relations with people who are similar 
  on some attribute (e.g. gender, music taste, social status, etc.). In general
  homophily leads to segregation, but segregation might arise without 
  homophily. This package implements descriptive indices measuring 
  homophily/segregation. It is a computational companion 
  to Bojanowski & Corten (2014) <doi:10.1016/j.socnet.2014.04.001>.

The network structural equation modeling conducts a network 
    statistical analysis on a data frame of coincident observations of 
    multiple continuous variables [1]. 
    It builds a pathway model by exploring a pool of domain knowledge guided 
    candidate statistical relationships between each of the variable pairs, 
    selecting the 'best fit' on the basis of a specific criteria such as 
    adjusted r-squared value.
    This material is based upon work supported by the U.S. National Science 
    Foundation Award EEC-2052776 and EEC-2052662 for the MDS-Rely IUCRC Center, 
    under the NSF Solicitation: 
    NSF 20-570 Industry-University Cooperative Research Centers Program 
    [1] Bruckman, Laura S., Nicholas R. Wheeler, Junheng Ma, Ethan Wang, 
    Carl K. Wang, Ivan Chou, Jiayang Sun, and Roger H. French. (2013) 
    <doi:10.1109/ACCESS.2013.2267611>.