{ "_id": "6a1ed68fb401979e7340ea82", "Package": "easybgm", "Type": "Package", "Title": "Extracting and Visualizing Bayesian Graphical Models", "Version": "0.4.0", "Authors@R": "c(\nperson(\"Karoline\", \"Huth\", , \"k.huth@uva.nl\", role = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0002-0662-1591\")),\nperson(\"Sara\", \"Keetelaar\", role = c(\"ctb\")),\nperson(\"Nikola\", \"Sekulovski\", role = c(\"ctb\")),\nperson(\"Gali\", \"Geller\", role = c(\"ctb\"))\n)", "Maintainer": "Karoline Huth ", "Description": "Fit and visualize the results of a Bayesian analysis of\nnetworks commonly found in psychology. The package supports\ncross-sectional network models fitted using the packages\n'BDgraph', 'bgms' and 'BGGM', as well as network comparison\ntests fitted using the packages 'bgms' and 'BBGM'. The package\nprovides the parameter estimates, posterior inclusion\nprobabilities, inclusion Bayes factor, and the posterior\ndensity of the parameters. In addition, for 'BDgraph' and\n'bgms' it allows to assess the posterior structure space.\nFurthermore, the package comes with an extensive suite for\nvisualizing results.", "License": "GPL (>= 2)", "URL": "https://github.com/KarolineHuth/easybgm", "BugReports": "https://github.com/KarolineHuth/easybgm/issues", "Encoding": "UTF-8", "Roxygen": "list(markdown = TRUE)", "RoxygenNote": "7.3.3", "Config/testthat/edition": "3", "Config/pak/sysreqs": "cmake libglpk-dev make libgsl0-dev libicu-dev\nlibjpeg-dev libpng-dev libuv1-dev libxml2-dev libssl-dev", "Repository": "https://karolinehuth.r-universe.dev", "Date/Publication": "2026-04-02 12:57:08 UTC", "RemoteUrl": "https://github.com/karolinehuth/easybgm", "RemoteRef": "HEAD", "RemoteSha": "68533c519978386da10f6ba8b2265e60420203c9", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-20 10:23:39 UTC", "User": "root" }, "Author": "Karoline Huth [aut, cre] (ORCID:\n),\nSara Keetelaar [ctb],\nNikola Sekulovski [ctb],\nGali Geller [ctb]", 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