Learn R Programming

netcom

netcom is an R package to infer system functioning by empirically comparing networks to each other. There are many uses of this general approach to understanding network data. The vignette covers several common kinds of inference. Once installed, this can be opened by running:

vignette("tutorial", package = "netcom")

You can learn more about comparative inference in the following:

  • Langendorf, R. E. & Burgess, M. G. (2020) Empirically classifying network mechanisms. arXiv preprint arXiv:2012.15863.

  • Langendorf, R. E. (2020). Using Structural Comparisons to Measure the Behavior of Complex Systems. In Evaluating Climate Change Impacts (pp. 123-137). Chapman and Hall/CRC.

  • Langendorf, R. E. & Goldberg, D. S. (2019) Aligning statistical dynamics captures biological network functioning. arXiv preprint arXiv:1912.12551.

You can install the netcom package two main ways:

  1. A release version of the package can be installed from CRAN (the Comprehensive R Archive Network): https://cran.r-project.org/package=netcom.
install.packages("netcom").
  1. Alternatively, the (sometimes) more recent development version can be installed from GitHub: https://github.com/langendorfr/netcom. This can be accomplished with the devtools package. We recommend new users install the other version, from CRAN, which may have less functioning but has been more reliably tested.
install.packages("devtools")
devtools::install_github("langendorfr/netcom")

Copy Link

Version

Install

install.packages('netcom')

Monthly Downloads

596

Version

2.1.6

License

GPL-3

Issues

Pull Requests

Stars

Forks

Maintainer

Ryan Langendorf

Last Published

July 21st, 2022

Functions in netcom (2.1.6)

compare

Compare Networks Many-to-Many
grow_DM

Grow a Duplication and Mutation Network
classify

Mechanistic Network Classification
align

Network Alignment
grow_ER

Grow an Erdos-Renyi Random Network
best_fit_optim

Empirical parameterization
classify_Systematic

Mechanistic Network Classification
compare_Target

Compare Networks One-to-Many
gini

Gini coefficient
grow_DD

Grow a Duplication and Divergence Network
grow_SW

Grow a Small-World Network
ics

Induced Conserved Structure (ICS)
make_Systematic_mixture

Systematically Make Networks
make_Systematic_canonical

Systematically Make Networks
make_DM

Make a Duplication and Mutation Network
stir_DM

Stirs a Duplication and Mutation Network
stir_DD

Sitrs a Duplication and Divergence Network
make_DD

Makes a Duplication and Divergence Network
make_Systematic_directedCanonicalLike

Systematically Make Networks
make_Systematic

Systematically Make Networks
stir_ER

Stir an Erdos-Renyi Random Network
stir_NM

Stirs a Niche Model Network
make_Mixture

Make a Mixture Mechanism Network
grow_NM

Grow a Niche Model Network
make_Null_canonical

Mechanism Null Distributions
make_Null_mixture

Mechanism Null Distributions
make_Null

Mechanism Null Distributions
null_fit_optim

Empirical parameterization via null distributions
grow_PA

Grow a Preferential Attachment Network
%>%

Pipe operator
make_SW

Makes a Small-World Network
stir_PA

Stirs a Preferential Attachment Network
stir_SW

Stirs a Small-World Network
make_NM

Make a Niche Model network