c(
person("David", "Schoch", email = "david.schoch@manchester.ac.uk", role = c("aut", "cre")),
person("Julian","Müller", email = "julian.mueller@gess.ethz.ch", role = "ctb"))

knitr, rmarkdown, magrittr, testthat, shiny (>= 0.13), miniUI
(>= 0.1.1), rstudioapi (>= 0.5)

c(
person("David", "Schoch", email = "david@schochastics.net", role = c("aut", "cre"),
comment = c(ORCID = "0000-0003-2952-4812")),
person("Julian","Müller", email = "julian.mueller@gess.ethz.ch", role = "ctb"))
David Schoch [aut, cre] (<https://orcid.org/0000-0003-2952-4812>),
Julian Müller [ctb]

person("Frank W. Marrs", email = "frank.marrs@colostate.edu", role = c("aut", "cre"))
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>.

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")))
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>.
Frank W. Marrs [aut, cre],
Bailey K. Fosdick [aut],
Tyler H. McCormick [aut]

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 from this class. For more detail on methods, see Marrs, Fosdick, and McCormick (2017) <arXiv:1701.05530>.