Package: bnviewer 0.1.6

bnviewer: Bayesian Networks Interactive Visualization and Explainable Artificial Intelligence

Bayesian networks provide an intuitive framework for probabilistic reasoning and its graphical nature can be interpreted quite clearly. Graph based methods of machine learning are becoming more popular because they offer a richer model of knowledge that can be understood by a human in a graphical format. The 'bnviewer' is an R Package that allows the interactive visualization of Bayesian Networks. The aim of this package is to improve the Bayesian Networks visualization over the basic and static views offered by existing packages.

Authors:Robson Fernandes [aut, cre, cph]

bnviewer_0.1.6.tar.gz
bnviewer_0.1.6.zip(r-4.5)bnviewer_0.1.6.zip(r-4.4)
bnviewer_0.1.6.tgz(r-4.4-any)
bnviewer_0.1.6.tar.gz(r-4.5-noble)bnviewer_0.1.6.tar.gz(r-4.4-noble)
bnviewer_0.1.6.tgz(r-4.4-emscripten)
bnviewer.pdf |bnviewer.html
bnviewer/json (API)

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

Peer review:

Bug tracker:https://github.com/robson-fernandes/bnviewer/issues

On CRAN:

bayesian-inferencebayesian-networkbayesian-networksprobabilistic-graphical-models

4.77 score 6 stars 1 packages 65 scripts 401 downloads 4 exports 108 dependencies

Last updated 4 years agofrom:924e6786c8. Checks:OK: 1 NOTE: 4. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 14 2024
R-4.5-winNOTENov 14 2024
R-4.5-linuxNOTENov 14 2024
R-4.4-winNOTENov 14 2024
R-4.4-macNOTENov 14 2024

Exports:bn.to.igraphmodel.to.structurestrength.viewerviewer

Dependencies:assertthatbase64encbnlearnbslibcachemcaretclasscliclockcodetoolscolorspacecommonmarkcpp11crayondata.tablediagramdigestdplyre1071evaluatefansifarverfastmapfontawesomeforeachfsfuturefuture.applygenericsggplot2globalsgluegowergtablehardhathighrhtmltoolshtmlwidgetshttpuvigraphipredisobanditeratorsjquerylibjsonliteKernSmoothknitrlabelinglaterlatticelavalifecyclelistenvlubridatemagrittrMASSMatrixmemoisemgcvmimeModelMetricsmunsellnlmennetnumDerivparallellypillarpkgconfigplyrpROCprodlimprogressrpromisesproxypurrrR6rappdirsRColorBrewerRcpprecipesreshape2rlangrmarkdownrpartsassscalesshapeshinysourcetoolsSQUAREMstringistringrsurvivaltibbletidyrtidyselecttimechangetimeDatetinytextzdbutf8vctrsviridisLitevisNetworkwithrxfunxtableyaml