Package: NetworkChange 0.8

NetworkChange: Bayesian Package for Network Changepoint Analysis

Network changepoint analysis for undirected network data. The package implements a hidden Markov network change point model (Park and Sohn (2020)). Functions for break number detection using the approximate marginal likelihood and WAIC are also provided.

Authors:Jong Hee Park [aut,cre], Yunkyu Sohn [aut]

NetworkChange_0.8.tar.gz
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NetworkChange.pdf |NetworkChange.html✨
NetworkChange/json (API)

# Install 'NetworkChange' in R:

install.packages('NetworkChange', repos = c('https://jongheepark.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/jongheepark/networkchange/issues

Datasets:

MajorAlly - Major Power Alliance Network
PostwarAlly - Postwar Alliance Network

On CRAN:

bayesian changepoint latent-space network

29 exports 4 stars 1.02 score 134 dependencies 13 scripts 222 downloads

Last updated 3 years agofrom:bd9202cbd2. Checks:OK: 1 NOTE: 6. Indexed: yes.

Target	Result	Date
Doc / Vignettes	OK	Aug 30 2024
R-4.5-win	NOTE	Aug 30 2024
R-4.5-linux	NOTE	Aug 30 2024
R-4.4-win	NOTE	Aug 30 2024
R-4.4-mac	NOTE	Aug 30 2024
R-4.3-win	NOTE	Aug 30 2024
R-4.3-mac	NOTE	Aug 30 2024

Exports:BreakDiagnostic BreakPointLoss drawPostAnalysis drawRegimeRaw kmeansU MakeBlockNetworkChange MarginalCompare multiplot NetworkChange NetworkChangeRobust NetworkStatic plotContour plotnetarray plotU plotV startS startUV ULUstateSample ULUstateSample.mpfr updateb updatebm updateP updateS updates2m updateU updateUm updateV updateVm WaicCompare

Dependencies:abind assertthat backports base64enc bit bit64 broom broom.helpers bslib cachem cards checkmate cli clipr cluster coda colorspace commonmark corpcor cpp11 crayon data.table digest dplyr evaluate fansi farver fastmap fdrtool fontawesome forcats foreign Formula fs generics GGally ggplot2 ggrepel ggstats ggvis glasso glue gmp GPArotation gridExtra gtable gtools haven highr Hmisc hms htmlTable htmltools htmlwidgets httpuv igraph isoband jpeg jquerylib jsonlite knitr labeling labelled later lattice lavaan lifecycle magrittr MASS Matrix MatrixModels mcmc MCMCpack memoise mgcv mime mnormt munsell mvtnorm network nlme nnet numDeriv patchwork pbapply pbivnorm pillar pkgconfig plyr png prettyunits progress promises psych purrr qgraph quadprog quantreg R6 rappdirs RColorBrewer Rcpp readr reshape reshape2 rlang rmarkdown Rmpfr rpart rstudioapi sass scales shiny sna sourcetools SparseM statnet.common stringi stringr survival tibble tidyr tidyselect tinytex tzdb utf8 vctrs viridis viridisLite vroom withr xfun xtable yaml

NetworkChange: Analyzing Network Changes in R

Jong Hee Park and Yunkyu Sohn

Rendered fromNetworkChange.Rmdusingknitr::rmarkdownon Aug 30 2024.

Last update: 2022-03-04
Started: 2020-07-01

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Detect a break number using different metrics	BreakDiagnostic
Compute the Average Loss of Hidden State Changes from Expected Break Points	BreakPointLoss
Plot of latent node cluster	drawPostAnalysis
Plot of network by hidden regime	drawRegimeRaw
K-mean clustering of latent node positions	kmeansU
Major Power Alliance Network (1816 - 2012)	MajorAlly
Build a synthetic block-structured temporal data with breaks	MakeBlockNetworkChange
Compare Log Marginal Likelihood	MarginalCompare
Printing multiple ggplots in oone file	multiplot
Changepoint analysis of a degree-corrected multilinear tensor model	NetworkChange
Changepoint analysis of a degree-corrected multilinear tensor model with t-distributed error	NetworkChangeRobust
Degree-corrected multilinear tensor model	NetworkStatic
Contour plot of latent node positions	plotContour
Plot of network array data	plotnetarray
Plot of latent node positions	plotU
Plot of layer-specific network generation rules.	plotV
Postwar Alliance Network (1846 - 2012)	PostwarAlly
Sample a starting value of hidden states	startS
Starting values of U and V	startUV
Hidden State Sampler	ULUstateSample
Hidden State Sampler with precision	ULUstateSample.mpfr
Update time-constant regression parameters	updateb
Update regime-changing regression parameters	updatebm
Update transition matrix	updateP
Update latent states	updateS
Update regime-specific variance	updates2m
Update time-constant latent node positions	updateU
Regime-specific latent node positions	updateUm
Update layer specific network generation rules	updateV
Update V from a change-point network process	updateVm
Compare WAIC	WaicCompare