Learn R Programming

⚠️There's a newer version (2.1.4) of this package.Take me there.

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)

Copy Link

Version

Install

install.packages('backbone')

Monthly Downloads

833

Version

2.0.3

License

GPL-3

Issues

Pull Requests

Stars

Forks

Maintainer

Zachary Neal

Last Published

March 22nd, 2022

Functions in backbone (2.0.3)

disparity

Extract backbone using the Disparity Filter
bipartite.from.probability

Generates a bipartite network with given edge probability
backbone.extract

Extracts a backbone network from a backbone object
backbone.suggest

Suggest a backbone model
backbone

backbone: Extracts the Backbone from Graphs
fastball

Randomize a binary matrix using the fastball algorithm
bipartite.from.sequence

Generates a bipartite graph from row and column degree sequences
bicm

Bipartite Configuration Model
bipartite.add.blocks

Adds a block structure to a bipartite network
bipartite.from.distribution

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

sdsm

Extract backbone using the Stochastic Degree Sequence Model
osdsm

Extract backbone using the Ordinal Stochastic Degree Sequence Model
sparsify.with.quadrilateral

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

Extract Nick et al's (2013) Simmelian backbone
loglikelihood_bicm

hyperg

Wrapper for fixedrow()
sparsify

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

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

Extract backbone using the Fixed Fill Model
loglikelihood_prime_bicm

global

Compute global threshold backbone
sparsify.with.jaccard

Extract Goldberg and Roth's (2003) Jaccard backbone
sparsify.with.localdegree

Extract Hamann et al.'s (2016) Local Degree backbone
frommatrix

Converts a backbone adjacency matrix to an object of specified class
universal

Wrapper for global()
fixedcol

Extract backbone using the Fixed Column Model
sparsify.with.hypergeometric

Extract Goldberg and Roth's (2003) Hypergeometric backbone
fdsm

Extract backbone using the Fixed Degree Sequence Model
sparsify.with.gspar

Extract Satuluri et al's (2011) G-spar backbone
write.narrative

Generates suggested manuscript text
sparsify.with.meetmin

Extract Goldberg and Roth's (2003) MeetMin backbone
sparsify.with.skeleton

Extract Karger's (1999) skeleton backbone
sparsify.with.lspar

Extract Satuluri et al's (2011) L-spar backbone
tomatrix

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

Extract backbone using the Fixed Row Model