Package: abn 3.1.1

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]

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'))

Peer review:

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

Pkgdown:https://r-bayesian-networks.org

Uses libs:
  • gsl– GNU Scientific Library (GSL)
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
  • jags– Just Another Gibbs Sampler for Bayesian MCMC
Datasets:

    gslopenblascppopenmpjags

    6.05 score 1 stars 90 scripts 717 downloads 6 mentions 112 exports 28 dependencies

    Last updated 6 months agofrom:a68c8622c9. Checks:OK: 2. Indexed: no.

    TargetResultDate
    Doc / VignettesOKNov 27 2024
    R-4.5-linux-x86_64OKNov 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

    Rendered fromstructure_learning.Rmdusingknitr::rmarkdownon Nov 27 2024.

    Last update: 2024-03-23
    Started: 2024-03-23

    Data Simulation

    Rendered fromdata_simulation.Rmdusingknitr::rmarkdownon Nov 27 2024.

    Last update: 2024-03-23
    Started: 2024-03-23

    Mixed-effect Bayesian Network Model

    Rendered frommixed_effect_BN_model.Rmdusingknitr::rmarkdownon Nov 27 2024.

    Last update: 2024-03-23
    Started: 2024-03-23

    Model Specification: Build a Cache of Scores

    Rendered frommodel_specification.Rmdusingknitr::rmarkdownon Nov 27 2024.

    Last update: 2024-03-23
    Started: 2024-03-23

    Parallelisation

    Rendered frommultiprocessing.Rmdusingknitr::rmarkdownon Nov 27 2024.

    Last update: 2024-05-31
    Started: 2024-03-23

    Parameter Learning

    Rendered fromparameter_learning.Rmdusingknitr::rmarkdownon Nov 27 2024.

    Last update: 2024-03-23
    Started: 2024-03-23

    Quick Start Example

    Rendered fromquick_start_example.Rmdusingknitr::rmarkdownon Nov 27 2024.

    Last update: 2024-03-23
    Started: 2024-03-23

    Readme and manuals

    Help Manual

    Help pageTopics
    Print AIC of objects of class 'abnFit'AIC.abnFit
    Print BIC of objects of class 'abnFit'BIC.abnFit
    Control the iterations in 'buildScoreCache'build.control
    Simple check on the control parameterscheck.valid.fitControls
    Print coefficients of objects of class 'abnFit'coef.abnFit
    Compare two DAGs or EGscompareDag
    Compare two DAGs or EGscompareEG
    Discretization of a Possibly Continuous Data Frame of Random Variables based on their distributiondiscretization
    Computes an Empirical Estimation of the Entropy from a Table of CountsentropyData
    Construct the essential graphessentialGraph
    expit of proportionsexpit
    expit functionexpit_cpp
    Print family of objects of class 'abnFit'family.abnFit
    Control the iterations in 'fitAbn'fit.control
    Extract Standard Deviations from all Gaussian NodesgetMSEfromModes
    Compute standard information for a DAG.infoDag
    Returns the strengths of the edge connections in a Bayesian Network learned from observational datalinkStrength
    Logit of proportionslogit
    logit functionslogit_cpp
    Print logLik of objects of class 'abnFit'logLik.abnFit
    Compute the Markov blanketmb
    Empirical Estimation of the Entropy from a Table of CountsmiData
    Convert modes to fitAbn.mle$coefs structuremodes2coefs
    Find most probable DAG structuremostProbable
    Print number of observations of objects of class 'abnFit'nobs.abnFit
    Probability to oddsodds
    Odds Ratio from a matrixor
    Plots DAG from an object of class 'abnDag'plot.abnDag
    Plot objects of class 'abnFit'plot.abnFit
    Plot objects of class 'abnHeuristic'plot.abnHeuristic
    Plot objects of class 'abnHillClimber'plot.abnHillClimber
    Plot objects of class 'abnMostprobable'plot.abnMostprobable
    Print objects of class 'abnCache'print.abnCache
    Print objects of class 'abnDag'print.abnDag
    Print objects of class 'abnFit'print.abnFit
    Print objects of class 'abnHeuristic'print.abnHeuristic
    Print objects of class 'abnHillClimber'print.abnHillClimber
    Print objects of class 'abnMostprobable'print.abnMostprobable
    Compute the score's contribution in a network of each observation.scoreContribution
    A family of heuristic algorithms that aims at finding high scoring directed acyclic graphssearchHeuristic
    Find high scoring directed acyclic graphs using heuristic search.searchHillClimber
    Simulate data from a fitted additive Bayesian network.simulateAbn
    Simulate a DAG with with arbitrary arcs densitysimulateDag
    Computes skewness of a distributionskewness
    Prints summary statistics from an object of class 'abnDag'summary.abnDag
    Print summary of objects of class 'abnFit'summary.abnFit
    Print summary of objects of class 'abnMostprobable'summary.abnMostprobable
    Convert a DAG into graphviz formattoGraphviz