Statistical Modeling with Network Cohesion
Model fitting procedures for regression with network cohesion effects, when a network connecting sample individuals is available in a regression problem. In the future, other commonly used statistical models will be added, such as gaussian graphical model.

c(
person("Ryan", "Langendorf", role = c("aut", "cre"), email = "ryan.langendorf@colorado.edu"),
person("Debra", "Goldberg", role = "ctb", email = "debra@colorado.edu")
)
Functions to take two networks stored as matrices and return
a node-level injection between them (bijection if the input networks are of the same size). The alignment
is made by giving the Hungarian algorithm a symmetric cost matrix where entries are the numerical difference in
the distributions over time of diffusion within each network originating from each node.
clue, expm, igraph, Matrix, pdist, pracma, vegan
Ryan Langendorf [aut, cre],
Debra Goldberg [ctb]

Functions to take two networks stored as matrices and return a node-level injection between them (bijection if the input networks are of the same size). The alignment is made by comparing diffusion kernels originating from each node in one network to those originating from each node in the other network. This creates a cost matrix where rows are nodes from one network and columns are nodes from the other network. Optimal node pairings are then found using the Hungarian algorithm.

Infer system functioning with empirical NETwork COMparisons.
c(
person("Ryan", "Langendorf", role = c("aut", "cre"), email = "ryan.langendorf@colorado.edu"),
person("Debra", "Goldberg", role = "ctb", email = "debra@colorado.edu"),
person("Matthew", "Burgess", role = "ctb", email = "Matthew.G.Burgess@colorado.edu")
)
stats, dplyr, tibble, clue, expm, igraph, Matrix, pdist,
pracma, vegan, magrittr, foreach, parallel, doParallel, optimx,
GenSA, rlang, ggfortify, ggplot2, ggraph, reshape2
Ryan Langendorf [aut, cre],
Debra Goldberg [ctb],
Matthew Burgess [ctb]

Infer system functioning with empirical NETwork COMparisons. These methods are part of a growing paradigm in network science that uses relative comparisons of networks to infer mechanistic classifications and predict systemic interventions. They have been developed and applied in Langendorf and Burgess (2021) <doi:10.1038/s41598-021-99251-7>, Langendorf (2020) <doi:10.1201/9781351190831-6>, and Langendorf and Goldberg (2019) <arXiv:1912.12551>.