Package: abn 3.1.1
abn: Modelling Multivariate Data with Additive Bayesian Networks
The 'abn' R package facilitates Bayesian network analysis, a probabilistic graphical model that derives from empirical data a directed acyclic graph (DAG). This DAG describes the dependency structure between random variables. The R package 'abn' provides routines to help determine optimal Bayesian network models for a given data set. These models are used to identify statistical dependencies in messy, complex data. Their additive formulation is equivalent to multivariate generalised linear modelling, including mixed models with independent and identically distributed (iid) random effects. The core functionality of the 'abn' package revolves around model selection, also known as structure discovery. It supports both exact and heuristic structure learning algorithms and does not restrict the data distribution of parent-child combinations, providing flexibility in model creation and analysis. The 'abn' package uses Laplace approximations for metric estimation and includes wrappers to the 'INLA' package. It also employs 'JAGS' for data simulation purposes. For more resources and information, visit the 'abn' website.
Authors:
abn_3.1.1.tar.gz
abn_3.1.1.tar.gz(r-4.5-noble)abn_3.1.1.tar.gz(r-4.4-noble)
abn_3.1.1.tgz(r-4.4-emscripten)abn_3.1.1.tgz(r-4.3-emscripten)
abn.pdf |abn.html✨
abn/json (API)
# Install 'abn' in R: |
install.packages('abn', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/furrer-lab/abn/issues
Pkgdown:https://r-bayesian-networks.org
Last updated 6 months agofrom:a68c8622c9. Checks:OK: 2. Indexed: no.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 27 2024 |
R-4.5-linux-x86_64 | OK | Nov 27 2024 |
Exports:abn.versionAIC.abnFitbern_bugsbern_bugsGroupBIC.abnFitbuild.controlbuildcachematrixbuildScoreCachebuildScoreCache.bayesbuildScoreCache.mlecalc.node.inla.glmcalc.node.inla.glmmcategorical_bugscategorical_bugsGroupcheck.valid.buildControlscheck.valid.dagcheck.valid.datacheck.valid.fitControlscheck.valid.groupscheck.valid.parentscheck.which.valid.nodescheckforcyclescoef.abnFitcompareDagcompareEGcreateAbnDagdiscretizationentropyDataessentialGrapheval.across.gridexpitexpit_cppfactorialfactorial_fastfamily.abnFitfind.next.left.xfind.next.right.xfit_single_nodefit.controlfitAbnfitabn_marginalsfitAbn.bayesfitAbn.mleforLoopContentforLoopContentBayesforLoopContentFitBayesformula_abngauss_bugsgauss_bugsGroupget.ind.quantilesget.quantilesget.var.typesgetmarginalsgetMargsINLAgetModeVectorgetMSEfromModesinfoDagirls_binomial_cppirls_binomial_cpp_brirls_binomial_cpp_fastirls_binomial_cpp_fast_brirls_gaussian_cppirls_gaussian_cpp_fastirls_poisson_cppirls_poisson_cpp_fastlinkStrengthlogitlogit_cpplogLik.abnFitmakebugsmakebugsGroupmbmi_cppmiDatamodes2coefsmostProbablemostprobable_Cnobs.abnFitoddsorplot.abnDagplot.abnFitplot.abnHeuristicplot.abnHillClimberplot.abnMostprobableplotAbnpois_bugspois_bugsGroupprint.abnCacheprint.abnDagprint.abnFitprint.abnHeuristicprint.abnHillClimberprint.abnMostprobablerank_cppregressionLoopscoreContributionsearchHeuristicsearchhillsearchHillClimbersimulateAbnsimulateDagskewnessstd.area.under.gridstrsplitssummary.abnDagsummary.abnFitsummary.abnMostprobabletidy.cachetoGraphvizvalidate_abnDagvalidate_dists
Dependencies:BiocGenericsbootcodacodetoolsdata.tabledoParallelforeachgenericsgraphiteratorsjsonlitelatticelme4MASSMatrixmclogitmemiscminqanlmenloptrnnetRcppRcppArmadilloRcppEigenRgraphvizrjagsstringiyaml
Bayesian Network Structure Learning
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usingknitr::rmarkdown
on Nov 27 2024.Last update: 2024-03-23
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Data Simulation
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usingknitr::rmarkdown
on Nov 27 2024.Last update: 2024-03-23
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Mixed-effect Bayesian Network Model
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usingknitr::rmarkdown
on Nov 27 2024.Last update: 2024-03-23
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Model Specification: Build a Cache of Scores
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Parallelisation
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usingknitr::rmarkdown
on Nov 27 2024.Last update: 2024-05-31
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Parameter Learning
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usingknitr::rmarkdown
on Nov 27 2024.Last update: 2024-03-23
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Quick Start Example
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usingknitr::rmarkdown
on Nov 27 2024.Last update: 2024-03-23
Started: 2024-03-23