Package: abn 3.1.2

Matteo Delucchi

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:Matteo Delucchi [aut, cre], Reinhard Furrer [aut], Gilles Kratzer [aut], Fraser Iain Lewis [aut], Jonas I. Liechti [ctb], Marta Pittavino [ctb], Kalina Cherneva [ctb]

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Peer review:

Bug tracker:https://github.com/furrer-lab/abn/issues

On CRAN:

bayesian-networkbinomialcategorical-datagaussiangrouped-datasetsmixed-effectsmultinomialmultivariatepoissonstructure-learning

112 exports 2 stars 3.02 score 27 dependencies 6 mentions 82 scripts 822 downloads

Last updated 7 days agofrom:3ff721d653. Checks:OK: 7 ERROR: 2. Indexed: yes.

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.tabledoParallelforeachgraphiteratorsjsonlitelatticelme4MASSMatrixmclogitmemiscminqanlmenloptrnnetRcppRcppArmadilloRcppEigenRgraphvizrjagsstringiyaml