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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NEWS

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

On CRAN:

1 exports 10 stars 3.70 score 93 dependencies 4 dependents 17 mentions 24 scripts 1.7k downloads

Last updated 11 months agofrom:d3977032ac. Checks:OK: 7. Indexed: yes.

Exports:IsingFit

Dependencies:abindbackportsbase64encbslibcachemcheckmatecliclustercodetoolscolorspacecorpcorcpp11data.tabledigestevaluatefansifarverfastmapfdrtoolfontawesomeforeachforeignFormulafsggplot2glassoglmnetglueGPArotationgridExtragtablegtoolshighrHmischtmlTablehtmltoolshtmlwidgetsigraphisobanditeratorsjpegjquerylibjsonliteknitrlabelinglatticelavaanlifecyclemagrittrMASSMatrixmemoisemgcvmimemnormtmunsellnlmennetnumDerivpbapplypbivnormpillarpkgconfigplyrpngpsychqgraphquadprogR6rappdirsRColorBrewerRcppRcppEigenreshape2rlangrmarkdownrpartrstudioapisassscalesshapestringistringrsurvivaltibbletinytexutf8vctrsviridisviridisLitewithrxfunyaml