Title: Analyzing Partial Rankings in Networks
person("David", "Schoch", email = "david.schoch@gess.ethz.ch", role = c("aut", "cre")),
person("Julian","Müller", email = "julian.mueller@gess.ethz.ch", role = "ctb"))
Description: Implements methods for centrality related analyses of networks.
While the package includes the possibility to build more than 20 indices,
its main focus lies on index-free assessment of centrality via partial
rankings obtained by neighborhood-inclusion or positional dominance. These
partial rankings can be analyzed with different methods, including
probabilistic methods like computing expected node ranks and relative
rank probabilities (how likely is it that a node is more central than another?).
The methodology is described in depth in the vignettes and in
Suggests: knitr, rmarkdown, magrittr, ggplot2, testthat, shiny (>=

person("David", "Schoch", email = "david.schoch@manchester.ac.uk", role = c("aut", "cre")),

Suggests: knitr, rmarkdown, magrittr, testthat, shiny (>= 0.13), miniUI

Imports: igraph (>= 1.0.1), Rcpp (>= 0.12.8), Matrix

Author: David Schoch [aut, cre] (<https://orcid.org/0000-0003-2952-4812>),

Authors@R: person("Frank W. Marrs", email = "frank.marrs@colostate.edu", role = c("aut", "cre"))
Description: Regress network responses (both directed and undirected) onto covariates of interest that may be actor-, relation-, or network-valued. In addition, compute principled variance estimates of the coefficients assuming that the errors are jointly exchangeable. Missing data is accommodated. Additionally implements building and inversion of covariance matrices under joint exchangeability, and  generates random covariance matrices of this class. For more detail on methods, see Marrs (2017) <arXiv:1701.05530>.

Authors@R: c(person("Frank W. Marrs", email="frank.marrs@colostate.edu", role=c("aut", "cre")), person("Bailey K. Fosdick", role=c("aut")), person("Tyler H. McCormick", role=c("aut")))
Description: Regress network responses (both directed and undirected) onto covariates of interest that may be actor-, relation-, or network-valued. In addition, compute principled variance estimates of the coefficients assuming that the errors are jointly exchangeable. Missing data is accommodated. Additionally implements building and inversion of covariance matrices under joint exchangeability, and  generates random covariance matrices of this class. For more detail on methods, see Marrs, Fosdick, and McCormick (2017) <arXiv:1701.05530>.