Description: The network structural equation modeling conducts a network
statistical analysis on a data frame of coincident observations of
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
Energy Office of Energy, Efficiency and Renewable Energy (EERE) under
Solar Energy Technologies Office (SETO) Agreement Number DE-EE0007140.
[1] Bruckman, Laura S., Nicholas R. Wheeler, Junheng Ma, Ethan Wang,
Carl K. Wang, Ivan Chou, Jiayang Sun, and Roger H. French. (2013)
person("Jennifer L. W.", "Carter", email="jwc137@case.edu",
Nicholas R. Wheeler [aut] (<https://orcid.org/0000-0003-2248-8919>),
Sameera Nalin Venkat [ctb] (<https://orcid.org/0000-0002-9114-4602>),
Jennifer L. W. Carter [ctb] (<https://orcid.org/0000-0001-6702-729X>),
Laura S. Bruckman [aut, cre] (<https://orcid.org/0000-0003-1271-1072>),
Maintainer: Laura S. Bruckman <lsh41@case.edu>

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,
NSF 20-570 Industry-University Cooperative Research Centers Program

Title: Tool for Comparison and Visualization of Multiple Networks
person("Rocherno", "de Jongh", email = "rocherno.dejongh@hotmail.com", role = "aut"),
person("Pariya", "Behrouzi",  email = "pariya.behrouzi@gmail.com", role = c("aut", "cre"), comment = c(ORCID = "https://orcid.org/0000-0001-6762-5433")))
Description: We developed a comprehensive tool that helps with visualization and analysis of networks with the same variables across multiple factor levels. The 'netShiny' contains most of the popular network features such as centrality measures, modularity, and other summary statistics (e.g. clustering coefficient). It also contains known tools to look at the (dis)similarities between two networks, such as pairwise distance measures between networks, set operations on the nodes of the networks, distribution of the weights of the edges and a network representing the difference between two correlation matrices. The package 'netShiny' also contains tools to perform bootstrapping and find clusters in networks. See the 'netShiny' manual for more information, documentation and examples.
future.callr (>= 0.8.0), ggplot2 (>= 3.3.6), ggVennDiagram (>=
promises (>= 1.2.0.1), readxl (>= 1.4.0), shinycssloaders (>=
Depends: shinyBS (>= 0.61.1), shiny (>= 1.7.2), shinydashboard (>=
Pariya Behrouzi [aut, cre] (<https://orcid.org/0000-0001-6762-5433>)