Package: BayesNetBP 1.6.1

Han Yu

BayesNetBP: Bayesian Network Belief Propagation

Belief propagation methods in Bayesian Networks to propagate evidence through the network. The implementation of these methods are based on the article: Cowell, RG (2005). Local Propagation in Conditional Gaussian Bayesian Networks <https://www.jmlr.org/papers/v6/cowell05a.html>. For details please see Yu et. al. (2020) BayesNetBP: An R Package for Probabilistic Reasoning in Bayesian Networks <doi:10.18637/jss.v094.i03>. The optional 'cyjShiny' package for running the Shiny app is available at <https://github.com/cytoscape/cyjShiny>. Please see the example in the documentation of 'runBayesNetApp' function for installing 'cyjShiny' package from GitHub.

Authors:Han Yu, Rachael Blair, Janhavi Moharil, Andrew Yan

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BayesNetBP.pdf |BayesNetBP.html
BayesNetBP/json (API)

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

Peer review:

Bug tracker:https://github.com/hyu-ub/bayesnetbp/issues

Datasets:
  • chest - A simulated data from the Chest Clinic example
  • emission - A ClusterTree Example of Emission Model
  • emission1000 - A simulated data from the Emission example
  • liver - Mus Musculus HDL QTL data from Leduc et. al.
  • toytree - A ClusterTree Example of Liver Model
  • yeast - Saccharomyces Cerevisiae eQTL data from Kruglak et. al.

On CRAN:

bayesian-networksconditional-gaussiannetwork-inferenceprobabilistic-graphical-models

18 exports 19 stars 2.11 score 53 dependencies 1 mentions 2 scripts 397 downloads

Last updated 1 years agofrom:ba2c8a9ced. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKSep 15 2024
R-4.5-winOKSep 15 2024
R-4.5-linuxOKSep 15 2024
R-4.4-winOKSep 15 2024
R-4.4-macOKSep 15 2024
R-4.3-winOKJul 17 2024
R-4.3-macOKJul 17 2024

Exports:AbsorbEvidencebn_to_graphNELClusterTreeCompileComputeKLDsElimTreeInitializeFactorQueryGetValueInitializerLocalModelCompileMarginalsPlotCGBNPlotMarginalsPlotTreePropagateqtlnet_to_graphNELrunBayesNetAppSamplerSummaryMarginals

Dependencies:backportsBiocGenericsbnlearnbootbroomclicolorspacecowplotcpp11DerivdoBydotCall64dplyrfansifarverfieldsgenericsggplot2gluegraphgtableigraphisobandlabelinglatticelifecyclemagrittrmapsMASSMatrixmgcvmicrobenchmarkmodelrmunsellnlmepillarpkgconfigpurrrR6RColorBrewerRcpprlangscalesspamstringistringrtibbletidyrtidyselectutf8vctrsviridisLitewithr