Package: easybgm 0.1.2
Karoline Huth
easybgm: Extracting and Visualizing Bayesian Graphical Models
Fit and visualize the results of a Bayesian analysis of networks commonly found in psychology. The package supports fitting cross-sectional network models fitted using the packages 'BDgraph', 'bgms' and 'BGGM'. The package provides the parameter estimates, posterior inclusion probabilities, inclusion Bayes factor, and the posterior density of the parameters. In addition, for 'BDgraph' and 'bgms' it allows to assess the posterior structure space. Furthermore, the package comes with an extensive suite for visualizing results.
Authors:
easybgm_0.1.2.tar.gz
easybgm_0.1.2.zip(r-4.5)easybgm_0.1.2.zip(r-4.4)easybgm_0.1.2.zip(r-4.3)
easybgm_0.1.2.tgz(r-4.4-any)easybgm_0.1.2.tgz(r-4.3-any)
easybgm_0.1.2.tar.gz(r-4.5-noble)easybgm_0.1.2.tar.gz(r-4.4-noble)
easybgm_0.1.2.tgz(r-4.4-emscripten)easybgm_0.1.2.tgz(r-4.3-emscripten)
easybgm.pdf |easybgm.html✨
easybgm/json (API)
| # Install 'easybgm' in R: |
| install.packages('easybgm', repos = c('https://karolinehuth.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/karolinehuth/easybgm/issues
Last updated 3 months agofrom:88c27f7783. Checks:OK: 7. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Aug 30 2024 |
| R-4.5-win | OK | Aug 30 2024 |
| R-4.5-linux | OK | Aug 30 2024 |
| R-4.4-win | OK | Aug 30 2024 |
| R-4.4-mac | OK | Aug 30 2024 |
| R-4.3-win | OK | Aug 30 2024 |
| R-4.3-mac | OK | Aug 30 2024 |
Exports:easybgmplot_centralityplot_complexity_probabilitiesplot_edgeevidenceplot_networkplot_parameterHDIplot_prior_sensitivityplot_structureplot_structure_probabilitiessparse_or_dense
Dependencies:abindbackportsbainbase64encBDgraphBergmBFpackBGGMbgmsBHbitbit64bootbroombroom.helpersbslibcachemcardscheckmateclicliprclustercodacolorspacecorpcorcpp11crayondata.tableDEoptimRdigestdplyrergmevaluateextraDistrfansifarverfastmapfdrtoolfontawesomeforcatsforeignFormulafsgenericsGGallyggplot2ggridgesggstatsglassoglueGPArotationgridExtragslgtablegtoolshavenHDIntervalhighrHmischmshtmlTablehtmltoolshtmlwidgetsigraphisobandjpegjquerylibjsonliteknitrlabelinglabelledlatticelavaanlifecyclelme4lpSolveAPImagrittrMASSMatrixmatrixcalcMatrixModelsmcmcMCMCpackmemoisemgcvmimeminqamnormtmunsellmvnfastmvtnormnetworknlmenloptrnnetnumDerivpatchworkpbapplypbivnormpillarpkgconfigplyrpngpracmaprettyunitspROCprogresspsychpurrrqgraphQRMquadprogquantregR6rappdirsrbibutilsRColorBrewerRcppRcppArmadilloRcppDistRcppEigenRcppProgressRdpackreadrreshapereshape2RglpkrlangrlermarkdownrobustbaserpartrstudioapisandwichsassscalesslamsnaSparseMstatnet.commonstringistringrsurvivaltibbletidyrtidyselecttimeDatetimeSeriestinytextrusttzdbutf8vctrsviridisviridisLitevroomwithrxfunyamlzoo
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Extract the results of a Bayesian analysis of networks | bgm_extract |
| Fit a Bayesian analysis of networks | bgm_fit |
| Plot strength centralities and 95% highest density interval | centrality plot_centrality |
| Plot posterior complexity probabilities | complexity_probs plot_complexity_probabilities |
| Fit a Bayesian analysis of networks | easybgm |
| Edge evidence plot | edgeevidence plot_edgeevidence |
| Plot of interaction parameters and their 95% highest density intervals | HDI plot_parameterHDI |
| Network plot | network plot_network |
| Print method for 'easybgm' objects | print.easybgm |
| Plot sensitivity to edge inclusion prior setting | plot_prior_sensitivity prior_sensitivity |
| Test for sparse against dense topologies | sparse_or_dense |
| Structure plot | plot_structure structure |
| Plot Posterior Structure Probabilities | plot_structure_probabilities structure_probs |
| Summary method for 'easybgm' objects | summary.easybgm |