Description: Infer system functioning with empirical NETwork COMparisons.
person("Debra", "Goldberg", role = "ctb", email = "debra@colorado.edu"),
person("Matthew", "Burgess", role = "ctb", email = "Matthew.G.Burgess@colorado.edu")
Imports: stats, dplyr, tibble, clue, expm, igraph, Matrix, pdist,
pracma, vegan, magrittr, foreach, parallel, doParallel, optimx,
GenSA, rlang, ggfortify, ggplot2, ggraph, reshape2

Description: 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>.

Title: Control Theory Methods for Networks
Maintainer: Teague R. Henry <trhenry@email.unc.edu>
Description: Implementations of various control theory methods for use
in brain and psychological networks. Contains controllability
statistics from Pasqualetti, Zampieri & Bullo (2014) <doi:10.1109/TCNS.2014.2310254>,
optimal control algorithms from Lewis, Vrabie & Syrmos (2012, ISBN:978-0-470-63349-6), and various utilities.
Imports: Rcpp (>= 1.0.1), Rdpack, Matrix, MASS, pracma, expm

Authors@R: c(person("Youjin", "Lee", role = c("aut", "cre"),
Imports: stats, igraph, igraphdata, MASS, mvrtn
Description: When network dependence is present, that is when social relations can engender dependence in the outcome of interest, treating such observations as independent results in invalid, anti-conservative statistical inference. We propose a test of independence among observations sampled from a single network <arXiv:1710.03296>.

Title: Network Analysis for Diffusion of Innovations
person("George", "Vega Yon", email="g.vegayon@gmail.com", role=c("aut", "cre"), comment="Rewrite functions with Rcpp, plus new features"),
person("Stephanie", "Dyal", email="stepharp@usc.edu", role=c("aut"), comment="Package's first version"),
person("Tim", "Hayes", email="timothybhayes@gmail.com", role=c("aut"), comment="Package's first version"),
person("Thomas", "Valente", email="tvalente@usc.edu", role=c("aut", "cph"), comment="R original code"))
Description: Empirical statistical analysis, visualization and simulation
of network models of the diffusion of innovations. The package implements
algorithms for calculating network diffusion statistics such as transmission
rate, hazard rates, exposure models, network threshold levels, infectiousness
(contagion), and susceptibility. The package is inspired by work published
Imports: Rcpp (>= 0.12.1), sna, Matrix, SparseM, methods, grDevices,
Suggests: testthat, knitr, ape, RSiena, igraph, survival
Collate: 'RcppExports.R' 'imports.R' 'graph_data.R' 'adjmat.R' 'data.R'
Author: George Vega Yon [aut, cre] (Rewrite functions with Rcpp, plus new
Stephanie Dyal [aut] (Package's first version),
Tim Hayes [aut] (Package's first version),
Thomas Valente [aut, cph] (R original code)