Package: IsingFit 0.4.1

Sacha Epskamp

IsingFit: Fitting Ising Models Using the ELasso Method

This network estimation procedure eLasso, which is based on the Ising model, combines l1-regularized logistic regression with model selection based on the Extended Bayesian Information Criterion (EBIC). EBIC is a fit measure that identifies relevant relationships between variables. The resulting network consists of variables as nodes and relevant relationships as edges. Can deal with binary data.

Authors:van Borkulo Claudia [aut], Sacha Epskamp [aut, cre], Alexander Robitzsch [ctb], Mihai Alexandru Constantin [ctb], Jesse Boot [ctb]

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IsingFit.pdf |IsingFit.html✨
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NEWS

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

Bug tracker:https://github.com/cvborkulo/isingfit/issues

On CRAN:

Conda:

6.85 score 10 stars 5 packages 25 scripts 1.9k downloads 17 mentions 1 exports 93 dependencies

Last updated 1 years agofrom:d3977032ac. Checks:9 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 05 2025
R-4.5-winOKMar 05 2025
R-4.5-macOKMar 05 2025
R-4.5-linuxOKMar 05 2025
R-4.4-winOKMar 05 2025
R-4.4-macOKMar 05 2025
R-4.4-linuxOKMar 05 2025
R-4.3-winOKMar 05 2025
R-4.3-macOKMar 05 2025

Exports:IsingFit

Dependencies:abindbackportsbase64encbslibcachemcheckmatecliclustercodetoolscolorspacecorpcorcpp11data.tabledigestevaluatefansifarverfastmapfdrtoolfontawesomeforeachforeignFormulafsggplot2glassoglmnetglueGPArotationgridExtragtablegtoolshighrHmischtmlTablehtmltoolshtmlwidgetsigraphisobanditeratorsjpegjquerylibjsonliteknitrlabelinglatticelavaanlifecyclemagrittrMASSMatrixmemoisemgcvmimemnormtmunsellnlmennetnumDerivpbapplypbivnormpillarpkgconfigplyrpngpsychqgraphquadprogR6rappdirsRColorBrewerRcppRcppEigenreshape2rlangrmarkdownrpartrstudioapisassscalesshapestringistringrsurvivaltibbletinytexutf8vctrsviridisviridisLitewithrxfunyaml