igraph, knitr, rmarkdown, speedglm, testthat

Matrix, methods, stats, utils, CVXR, igraph, network

Matrix, methods, stats, utils, CVXR, igraph, network, Rcpp

Matrix, methods, stats, utils, igraph, network

Provides methods for extracting from a weighted graph
a binary or signed backbone that retains only the significant edges.
The user may input a weighted graph, or a bipartite graph
from which a weighted graph is first constructed via projection.
Backbone extraction methods include the stochastic degree sequence model (Neal, Z. P. (2014). <doi:10.1016/j.socnet.2014.06.001>),
hypergeometric model (Neal, Z. (2013). <doi:10.1007/s13278-013-0107-y>),
the fixed degree sequence model (Zweig, K. A., and Kaufmann, M. (2011). <doi:10.1007/s13278-011-0021-0>),
fixed column model, fixed fill model,
as well as a universal threshold method.
Matrix, methods, stats, utils, igraph, network,
PoissonBinomial

c(
person("Zachary", "Neal", email = "zpneal@msu.edu", role = c("aut", "cre")),
person("Rachel", "Domagalski", email = "domagal9@msu.edu", role = "aut"),
person("Bruce", "Sagan", email = "bsagan@msu.edu", role = "aut"))
Provides methods for extracting from a weighted graph
a binary or signed backbone that retains only the significant edges.
The user may input a weighted graph, or a bipartite graph
from which a weighted graph is first constructed via projection.
Backbone extraction methods include the stochastic degree sequence model (SDSM; Neal, Z. P. (2014). <doi:10.1016/j.socnet.2014.06.001>),
the fixed degree sequence model (FDSM; Zweig, K. A., and Kaufmann, M. (2011). <doi:10.1007/s13278-011-0021-0>),
the fixed row model (FRM; Neal, Z. P. (2013). <doi:10.1007/s13278-013-0107-y>),
the fixed column model (FCM; Neal, Domagalski, and Sagan (2021). <arXiv:2105.13396>),
the fixed fill model (FFM; Neal, Domagalski, and Sagan (2021). <arXiv:2105.13396>),
and a universal threshold method.
Zachary Neal [aut, cre],
Rachel Domagalski [aut],
Bruce Sagan [aut]

c(
person("Zachary", "Neal", email = "zpneal@msu.edu", role = c("aut", "cre")),
person("Rachel", "Domagalski", email = "domagal9@msu.edu", role = "aut"),
person("Bruce", "Sagan", email = "bsagan@msu.edu", role = "aut"),
person("Godard", "Karl", email = "godardk@umich.edu", role = "ctb"))
An implementation of methods for extracting an unweighted unipartite
graph (i.e. a backbone) from an unweighted unipartite graph (Hamann et al., 2016
<doi:10.1007/s13278-016-0332-2>), a weighted  unipartite graph (Serrano et al.,
2009 <doi:10.1073/pnas.0808904106>), the projection of an unweighted bipartite
graph (Neal et al., <doi:10.1038/s41598-021-03238-3>, or the projection of a weighted
bipartite graph (Neal, 2017 <doi:10.1177/0308518X16631339>). It also provides utility
functions to generate random binary matrices with (a) given density, (b) given row
and column marginals, and (c) given row and column marginal distributions.
igraph, Matrix, methods, network, PoissonBinomial, stats,
Rcpp, utils,
knitr, MASS, rmarkdown, testthat (>= 3.0.0)
Zachary Neal [aut, cre],
Rachel Domagalski [aut],
Bruce Sagan [aut],
Godard Karl [ctb]