Package: ConNEcT 0.7.27

ConNEcT: Contingency Measure-Based Networks for Binary Time Series

The ConNEcT approach investigates the pairwise association strength of binary time series by calculating contingency measures and depicts the results in a network. The package includes features to explore and visualize the data. To calculate the pairwise concurrent or temporal sequenced relationship between the variables, the package provides seven contingency measures (proportion of agreement, classical & corrected Jaccard, Cohen's kappa, phi correlation coefficient, odds ratio, and log odds ratio), however, others can easily be implemented. The package also includes non-parametric significance tests, that can be applied to test whether the contingency value quantifying the relationship between the variables is significantly higher than chance level. Most importantly this test accounts for auto-dependence and relative frequency.See Bodner et al.(2021) <doi:10.1111/bmsp.12222>.Finally, a network can be drawn. Variables depicted the nodes of the network, with the node size adapted to the prevalence. The association strength between the variables defines the undirected (concurrent) or directed (temporal sequenced) links between the nodes. The results of the non-parametric significance test can be included by depicting either all links or only the significant ones. Tutorial see Bodner et al.(2021) <doi:10.3758/s13428-021-01760-w>.

Authors:Nadja Bodner [aut, cre], Eva Ceulemans [ctb, rev]

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ConNEcT.pdf |ConNEcT.html
ConNEcT/json (API)

# Install 'ConNEcT' in R:
install.packages('ConNEcT', repos = c('https://nabod0815.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Datasets:

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

19 exports 0.00 score 86 dependencies 2 scripts 368 downloads

Last updated 2 years agofrom:ee8427c4e5. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKAug 21 2024
R-4.5-winOKAug 21 2024
R-4.5-linuxOKAug 21 2024
R-4.4-winOKAug 21 2024
R-4.4-macOKAug 21 2024
R-4.3-winOKAug 21 2024
R-4.3-macOKAug 21 2024

Exports:conDataconMxconNEcTconProfconTestfunClassJaccfunCorrJaccfunKappafunLogOddsfunOddsfunPhiCCfunPropAgreegetProblagthematsmodelADmodelNOpermutADpermutNOqgraph.conNEcT

Dependencies:abindbackportsbase64encbslibcachemcheckmatecliclustercolorspacecorpcorcpp11data.tabledigestevaluatefansifarverfastmapfdrtoolfontawesomeforeignFormulafsggplot2glassoglueGPArotationgridExtragtablegtoolshighrHmischtmlTablehtmltoolshtmlwidgetsigraphisobandjpegjquerylibjsonliteknitrlabelinglatticelavaanlifecyclemagrittrMASSMatrixmemoisemgcvmimemnormtmunsellnlmennetnumDerivpbapplypbivnormpillarpkgconfigplyrpngpsychqgraphquadprogR6rappdirsRColorBrewerRcppreshape2rlangrmarkdownrpartrstudioapisassscalesstringistringrtibbletinytexutf8vctrsviridisviridisLitewithrxfunyaml

Readme and manuals

Help Manual

Help pageTopics
Attachment-related mother-child interaction datasetAttachmentData
Depict the relative frequencies (and conditional probabilities) of a binary time series in a barplotbarplot.conData
Explore and tidy raw dataconData
Calculate contingency measure values of a (lagged) time series matrixconMx
Calculate the link strength between multiple behaviors and return them as a matrix (optionally discarting all non-significant links)conNEcT
Compare different lags in a contingency profileconProf
Test significanceconTest
Affective family interaction datasetFamilyData
Calculate the classic Jaccard index between two vectorsfunClassJacc
Calculate the corrected Jaccard index between two vectorsfunCorrJacc
Calculate Cohen's kappa between two vectorsfunKappa
Calculate the log odds ratio between two vectorsfunLogOdds
Calculate the odds ratio between two vectorsfunOdds
Calculate the phi correllation coefficient index between two vectorsfunPhiCC
Calculate the proportion of agreement between two vectorsfunPropAgree
Retrieve (conditional) probabilities from binary time seriesgetProb
Plot histogram matrix of the significance testhist.conTest
Lag a matrixlagthemats
Generate data with model-based approach accounting for auto-dependencemodelAD
Generate data with model-based approach ignoring auto-dependencemodelNO
Generate data with permutation-based approach accounting for auto-dependencepermutAD
Generate data with permutation-based approach ignoring auto-dependencepermutNO
Visualize the course of the variables over timeplot.conData
Draw contingency profilesplot.conProf
Draws a Network figureqgraph.conNEcT
Depression symptom datasetSymptomData