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'))

Peer review:

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

Datasets:

On CRAN:

bayesianchangepointlatent-spacenetwork

4.41 score 4 stars 13 scripts 216 downloads 29 exports 123 dependencies

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

TargetResultDate
Doc / VignettesOKOct 29 2024
R-4.5-winNOTEOct 31 2024
R-4.5-linuxNOTEOct 29 2024
R-4.4-winNOTEOct 29 2024
R-4.4-macNOTEOct 29 2024
R-4.3-winNOTEOct 29 2024
R-4.3-macNOTEOct 29 2024

Exports:BreakDiagnosticBreakPointLossdrawPostAnalysisdrawRegimeRawkmeansUMakeBlockNetworkChangeMarginalComparemultiplotNetworkChangeNetworkChangeRobustNetworkStaticplotContourplotnetarrayplotUplotVstartSstartUVULUstateSampleULUstateSample.mpfrupdatebupdatebmupdatePupdateSupdates2mupdateUupdateUmupdateVupdateVmWaicCompare

Dependencies:abindassertthatbackportsbase64encbslibcachemcheckmatecliclustercodacolorspacecommonmarkcorpcorcpp11crayondata.tabledigestdplyrevaluatefansifarverfastmapfdrtoolfontawesomeforcatsforeignFormulafsgenericsGGallyggplot2ggrepelggstatsggvisglassogluegmpGPArotationgridExtragtablegtoolshighrHmischmshtmlTablehtmltoolshtmlwidgetshttpuvigraphisobandjpegjquerylibjsonliteknitrlabelinglaterlatticelavaanlifecyclemagrittrMASSMatrixMatrixModelsmcmcMCMCpackmemoisemgcvmimemnormtmunsellmvtnormnetworknlmennetnumDerivpatchworkpbapplypbivnormpillarpkgconfigplyrpngprettyunitsprogresspromisespsychpurrrqgraphquadprogquantregR6rappdirsRColorBrewerRcppreshapereshape2rlangrmarkdownRmpfrrpartrstudioapisassscalesshinysnasourcetoolsSparseMstatnet.commonstringistringrsurvivaltibbletidyrtidyselecttinytexutf8vctrsviridisviridisLitewithrxfunxtableyaml

NetworkChange: Analyzing Network Changes in R

Rendered fromNetworkChange.Rmdusingknitr::rmarkdownon Oct 29 2024.

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

Readme and manuals

Help Manual

Help pageTopics
Detect a break number using different metricsBreakDiagnostic
Compute the Average Loss of Hidden State Changes from Expected Break PointsBreakPointLoss
Plot of latent node clusterdrawPostAnalysis
Plot of network by hidden regimedrawRegimeRaw
K-mean clustering of latent node positionskmeansU
Major Power Alliance Network (1816 - 2012)MajorAlly
Build a synthetic block-structured temporal data with breaksMakeBlockNetworkChange
Compare Log Marginal LikelihoodMarginalCompare
Printing multiple ggplots in oone filemultiplot
Changepoint analysis of a degree-corrected multilinear tensor modelNetworkChange
Changepoint analysis of a degree-corrected multilinear tensor model with t-distributed errorNetworkChangeRobust
Degree-corrected multilinear tensor modelNetworkStatic
Contour plot of latent node positionsplotContour
Plot of network array dataplotnetarray
Plot of latent node positionsplotU
Plot of layer-specific network generation rules.plotV
Postwar Alliance Network (1846 - 2012)PostwarAlly
Sample a starting value of hidden statesstartS
Starting values of U and VstartUV
Hidden State SamplerULUstateSample
Hidden State Sampler with precisionULUstateSample.mpfr
Update time-constant regression parametersupdateb
Update regime-changing regression parametersupdatebm
Update transition matrixupdateP
Update latent statesupdateS
Update regime-specific varianceupdates2m
Update time-constant latent node positionsupdateU
Regime-specific latent node positionsupdateUm
Update layer specific network generation rulesupdateV
Update V from a change-point network processupdateVm
Compare WAICWaicCompare