5) network smoothed t-statistic (stSVM).
Imports: AnnotationDbi, Matrix, ROCR, graph, igraph, samr
Suggests: parallel, Biobase, KEGG.db, pathClass

Title: Model-Based Clustering of Network Data
Description: Clustering unilayer and multilayer network data by means of finite mixtures is the main utility of 'netClust'.

Description: Create interactive networked coincidences. It joins the data analysis power of R to study coincidences and the visualization libraries of JavaScript in one package.

Description: Create interactive analytic networks. It joins the data analysis power of R to obtain coincidences, co-occurrences and correlations, and the visualization libraries of 'JavaScript' in one package.

Suggests: knitr, rmarkdown, lavaan, shiny

Description: Create interactive analytic networks. It joins the data analysis power of R to obtain coincidences, co-occurrences and correlations, and the visualization libraries of 'JavaScript' in one package. The methods are described in Escobar and Martinez-Uribe (2020) <doi:10.18637/jss.v093.i11>.

Suggests: knitr, rmarkdown, lavaan, shiny, htmltools
person("Modesto", "Escobar", email = "modesto@usal.es", role = c("cre", "aut", "cph"), comment = c(ORCID = "0000-0003-2072-6071")),
person("David", "Barrios", email = "metal@usal.es", role = "aut"),
person("Carlos", "Prieto", email = "cprietos@usal.es", role = "aut"),
person("Luis", "Martinez-Uribe", email = "lmartinez@usal.es", role = "aut", comment = c(ORCID = "0000-0002-7795-3972")),
person("Pablo", "Cabrera-Álvarez", email = "pablocal@usal.es", role = "aut")

Title: Network Structural Equation Modeling
Description: 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 work was funded under U. S. Dept. of Energy, Prime Award No. DE-E-0004946,
[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>.
Imports: DiagrammeR (>= 0.9.2), DiagrammeRsvg (>= 0.1), htmlwidgets (>=
Author: Wei-Heng Huang [aut, cre] (<https://orcid.org/0000-0002-6609-4981>),