Package: abn
Title: Modelling Multivariate Data with Additive Bayesian Networks
Version: 3.1.13
Date: 2025-12-18
Authors@R: c(
    person("Matteo", "Delucchi", , "matteo.delucchi@math.uzh.ch", role = c("aut", "cre"),
           comment = c(ORCID = "0000-0002-9327-1496")),
    person("Reinhard", "Furrer", , "reinhard.furrer@math.uzh.ch", role = "aut",
           comment = c(ORCID = "0000-0002-6319-2332")),
    person("Gilles", "Kratzer", , "gilles.kratzer@gmail.com", role = "aut",
           comment = c(ORCID = "0000-0002-5929-8935")),
    person("Fraser Iain", "Lewis", , "fraser.iain.lewis@gmail.com", role = "aut",
           comment = c(ORCID = "0000-0003-4580-2712")),
    person("Jonas I.", "Liechti", , "j-i-l@t4d.ch", role = "ctb",
           comment = c(ORCID = "0000-0003-3447-3060")),
    person("Marta", "Pittavino", , "marta.pittavino@math.uzh.ch", role = "ctb",
           comment = c(ORCID = "0000-0002-1232-1034")),
    person("Kalina", "Cherneva", , "kalinacherneva@gmail.com", role = "ctb")
  )
Description: 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.
License: GPL (>= 3)
URL: https://r-bayesian-networks.org/,
        https://github.com/furrer-lab/abn
BugReports: https://github.com/furrer-lab/abn/issues
Depends: R (>= 4.0.0)
Imports: doParallel, foreach, glmmTMB, graph, jsonlite, lme4, mclogit,
        methods, nnet, Rcpp, Rgraphviz, rjags, stringi
Suggests: bookdown, boot, brglm, devtools (>= 2.4.5), ggplot2,
        gridExtra, INLA, knitr, Matrix, MatrixModels (>= 0.5.3),
        microbenchmark, R.rsp, RhpcBLASctl, rmarkdown, testthat (>=
        3.0.0), entropy, moments, R6
LinkingTo: Rcpp, RcppArmadillo
VignetteBuilder: knitr
Additional_repositories: https://inla.r-inla-download.org/R/stable/
Config/testthat/edition: 3
Encoding: UTF-8
LazyData: TRUE
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.3.3
SystemRequirements: pkg-config, cmake, gsl, jpeg, gdal, geos, proj,
        udunits-2, openssl, libcurl, jags
Repository: https://furrer-lab.r-universe.dev
Date/Publication: 2026-06-08 12:11:26 UTC
RemoteUrl: https://github.com/furrer-lab/abn
RemoteRef: HEAD
RemoteSha: 22f9a5077690bc0a5da06b2e91573179fad30360
NeedsCompilation: yes
Packaged: 2026-06-08 15:16:22 UTC; root
Author: Matteo Delucchi [aut, cre] (ORCID:
    <https://orcid.org/0000-0002-9327-1496>),
  Reinhard Furrer [aut] (ORCID: <https://orcid.org/0000-0002-6319-2332>),
  Gilles Kratzer [aut] (ORCID: <https://orcid.org/0000-0002-5929-8935>),
  Fraser Iain Lewis [aut] (ORCID:
    <https://orcid.org/0000-0003-4580-2712>),
  Jonas I. Liechti [ctb] (ORCID: <https://orcid.org/0000-0003-3447-3060>),
  Marta Pittavino [ctb] (ORCID: <https://orcid.org/0000-0002-1232-1034>),
  Kalina Cherneva [ctb]
Maintainer: Matteo Delucchi <matteo.delucchi@math.uzh.ch>