NetOrigin package

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Performs network-based source estimation. Different approaches are available: effective distance median, recursive backtracking, and centrality-based source estimation. Additionally, we provide public transportation network data as well as methods for data preparation, source estimation performance analysis and visualization.

Installation

You can install the latest production version from CRAN

install.packages("NetOrigin", dependencies = TRUE)

or the current development version from GitHub

library("devtools")
install_github("jmanitz/NetOrigin")

Then, load the package

library("NetOrigin")

Example: Effective Distance

data(delayGoe)

# compute effective distance
data(ptnGoe)
goenet <- igraph::as_adjacency_matrix(ptnGoe, sparse=FALSE)
p <- goenet/rowSums(goenet)
eff <- eff_dist(p)

# apply effective distance median source estimation
om <- origin(events=delayGoe[10,-c(1:2)], type='edm', distance=eff)
summary(om)
plot(om, 'mdist', start=1)
plot(om, 'wvar', start=1)
performance(om, start=1, graph=ptnGoe)

Example: Backtracking

# backtracking origin estimation (Manitz et al., 2016)
ob <- origin(events=delayGoe[10,-c(1:2)], type='backtracking', graph=ptnGoe)
summary(ob)
plot(ob, start=1)
performance(ob, start=1, graph=ptnGoe)

Example: Multiple Origins

data(ptnAth)
origin_multiple(events=delayAth[10,-c(1:2)], type='backtracking', graph=ptnAth, no=2)
# edm
athnet <- igraph::as_adjacency_matrix(ptnAth, sparse=FALSE)
p <- athnet/rowSums(athnet)
eff <- eff_dist(p)
origin_multiple(events=delayAth[10,-c(1:2)], type='edm', graph=ptnAth, no=2, distance=eff)

References

To cite package ‘NetOrigin’ in publications use:

Juliane Manitz (2018). NetOrigin: Origin Estimation for Propagation Processes on Complex Networks. R package version 1.0-3. https://CRAN.R-project.org/package=NetOrigin

Use toBibtex(citation("NetOrigin")) in R to extract BibTeX references.



jmanitz/NetOrigin documentation built on Sept. 5, 2023, 6:10 a.m.