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backbone

Welcome

Thank you for your interest in the backbone package! The backbone package implements methods to extract the backbone of a network, which is a sparse and unweighted subgraph that contains only the most ‘important’ or ‘significant’ edges. A backbone can be useful when the original network is too dense, when edge weights are not needed, or when edge weights are difficult to interpret. Methods are available for:

  • Weighted bipartite projections
  • Non-projection weighted networks
  • Unweighted networks

In addition, the package implements some other utility functions to:

  • Generate random bipartite networks
  • Randomize matrices while preserving the row and column sums
  • Estimate the Bipartite Configuration Model (BiCM)

For more details on these functions and methods, please see:

Installation

The /release branch contains the current CRAN release of the backbone package. You can install it from CRAN with:

install.packages("backbone")

The /devel branch contains the working beta version of the next release of the backbone package. All the functions are documented and have undergone various levels of preliminary debugging, so they should mostly work, but there are no guarantees. Feel free to use the devel version (with caution), and let us know if you run into any problems. You can install it You can install from GitHub with:

library(devtools)
install_github("zpneal/backbone", ref = "devel", build_vignettes = TRUE)

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Version

Install

install.packages('backbone')

Monthly Downloads

833

Version

2.0.0

License

GPL-3

Issues

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Maintainer

Zachary Neal

Last Published

December 10th, 2021

Functions in backbone (2.0.0)

bipartite.from.distribution

Generates a bipartite network with given row and column degree distributions
bicm

Bipartite Configuration Model
disparity

Extract backbone using the Disparity Filter
backbone.suggest

Suggest a backbone model
backbone.extract

Extracts a backbone network from a backbone object
bipartite.add.blocks

Adds a block structure to a bipartite network
curveball

Randomize a binary matrix using the curveball algorithm
backbone

backbone: Extracts the Backbone from Graphs
bipartite.from.probability

Generates a bipartite network with given edge probability
bipartite.from.sequence

Generates a bipartite graph from row and column degree sequences
fastball

Randomize a binary matrix using the fastball algorithm
hyperg

Wrapper for fixedrow()
loglikelihood_bicm

frommatrix

Converts a backbone adjacency matrix to an object of specified class
sparsify.with.localdegree

Extract Hamann et al.'s (2016) Local Degree backbone
sparsify.with.meetmin

Extract Goldberg and Roth's (2003) MeetMin backbone
global

Compute global threshold backbone
sparsify.with.lspar

Extract Satuluri et al's (2011) L-spar backbone
sparsify.with.jaccard

Extract Goldberg and Roth's (2003) Jaccard backbone
loglikelihood_prime_bicm

loglikelihood_hessian_diag_bicm

fdsm

Extract backbone using the Fixed Degree Sequence Model
fdsm.trials

Estimate number of Monte Carlo trials needed for FDSM backbone
sparsify.with.quadrilateral

Extract Nocaj et al.'s (2015) Quadrilateral Simmelian backbone
sparsify.with.simmelian

Extract Nick et al's (2013) Simmelian backbone
sparsify.with.hypergeometric

Extract Goldberg and Roth's (2003) Hypergeometric backbone
sparsify.with.gspar

Extract Satuluri et al's (2011) G-spar backbone
fixedfill

Extract backbone using the Fixed Fill Model
fixedrow

Extract backbone using the Fixed Row Model
sparsify.with.geometric

Extract Goldberg and Roth's (2003) Geometric backbone
sparsify

Extract the backbone from a network using a sparsification model
sparsify.with.skeleton

Extract Karger's (1999) skeleton backbone
tomatrix

Converts an input graph object to an adjacency/incidence matrix and identifies its characteristics
osdsm

Extract backbone using the Ordinal Stochastic Degree Sequence Model
fixedcol

Extract backbone using the Fixed Column Model
sdsm

Extract backbone using the Stochastic Degree Sequence Model
universal

Wrapper for global()
write.narrative

Generates suggested manuscript text