Title: Spatio-Network Generalised Linear Mixed Models for Areal Unit
Author: George Gerogiannis, Mark Tranmer, Duncan Lee
Description: Implements a class of univariate and multivariate spatio-network generalised linear mixed models for areal unit and network data, with inference in a Bayesian setting using Markov chain Monte Carlo (MCMC) simulation. The response variable can be binomial, Gaussian, or Poisson. Spatial autocorrelation is modelled by a set of random effects that are assigned a conditional autoregressive (CAR) prior distribution following the Leroux model (Leroux et al. (2000) <doi:10.1007/978-1-4612-1284-3_4>). Network structures are modelled by a set of random effects that reflect a multiple membership structure (Browne et al. (2001) <doi:10.1177/1471082X0100100202>).
Imports: Rcpp (>= 1.0.4), coda, ggplot2, mvtnorm, MASS

Title: Statistical Modeling with Network Cohesion
Author: Tianxi Li, Elizaveta Levina, Ji Zhu
Description: 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.

person("Ryan", "Langendorf", role = c("aut", "cre"), email = "ryan.langendorf@colorado.edu"),
person("Debra", "Goldberg", role = "ctb", email = "debra@colorado.edu")
Description: 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.
Imports: clue, expm, igraph, Matrix, pdist, pracma, vegan

Description: 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.