Package: deal 1.2-42
Claus Dethlefsen
deal: Learning Bayesian Networks with Mixed Variables
Bayesian networks with continuous and/or discrete variables can be learned and compared from data. The method is described in Boettcher and Dethlefsen (2003), <doi:10.18637/jss.v008.i20>.
Authors:
deal_1.2-42.tar.gz
deal_1.2-42.tar.gz(r-4.5-noble)deal_1.2-42.tar.gz(r-4.4-noble)
deal_1.2-42.tgz(r-4.4-emscripten)deal_1.2-42.tgz(r-4.3-emscripten)
deal.pdf |deal.html✨
deal/json (API)
# Install 'deal' in R: |
install.packages('deal', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 2 years agofrom:04729afd3c. Checks:OK: 2. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Dec 15 2024 |
R-4.5-linux-x86_64 | OK | Dec 15 2024 |
Exports:addarrowaddarrowsaddrandomarrowas.networkautosearchbanlistbanlist<-cond.nodeconditionalconditional.contconditional.disccycletestdeleterandomarrowdrawnetworkelementinfindexfindleafgenlatexgenpicfilegetnetworkgettablegettrylistheuristicinsertinspectprobjointcontjointdiscjointpriorlearnlearnnodelocalmasterlocalposteriorlocalpriorlocalproblocalprob<-makenwmakesimprobmaketrylistmodelstringnetworknetworkfamilynodenodesnodes<-numbermixednwequalnwfsortperturbplot.networkplot.networkfamilyplot.nodepostpost0postcpostc0cpostccpostdistpostdist.nodeprint.networkprint.networkfamilyprint.nodeprintlineprobprob.networkprob.nodereadnetremovearrowremoverrnetworksavenetscorescore.networkscore.nodesizeturnarrowturnrandomarrowudisclikunique.networkfamily
Dependencies: