Package: abn
Title: Modelling Multivariate Data with Additive Bayesian Networks
Version: 3.0.6
Date: 2024-03-21
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("Marta", "Pittavino", , "marta.pittavino@math.uzh.ch", role = "ctb",
           comment = c(ORCID = "0000-0002-1232-1034")),
    person("Kalina", "Cherneva", , "kalinacherneva@gmail.com", role = "ctb")
  )
Author: Matteo Delucchi [aut, cre] (<https://orcid.org/0000-0002-9327-1496>),
  Reinhard Furrer [aut] (<https://orcid.org/0000-0002-6319-2332>),
  Gilles Kratzer [aut] (<https://orcid.org/0000-0002-5929-8935>),
  Fraser Iain Lewis [aut] (<https://orcid.org/0000-0003-4580-2712>),
  Marta Pittavino [ctb] (<https://orcid.org/0000-0002-1232-1034>),
  Kalina Cherneva [ctb]
Maintainer: Matteo Delucchi <matteo.delucchi@math.uzh.ch>
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, graph, lme4, mclogit, methods, nnet,
        Rcpp, Rgraphviz, rjags, stringi
Suggests: bookdown, boot, brglm, devtools (>= 2.4.5), entropy, ggplot2,
        gridExtra, knitr, Matrix (>= 1.6.3), MatrixModels (>= 0.5.3),
        microbenchmark, moments, R.rsp, RhpcBLASctl, rmarkdown,
        testthat (>= 3.0.0)
LinkingTo: Rcpp, RcppArmadillo
VignetteBuilder: knitr
Additional_repositories: https://inla.r-inla-download.org/R/stable/
Config/testthat/edition: 3
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.3.1
SystemRequirements: Gnu Scientific Library version >= 1.12
NeedsCompilation: yes
Packaged: 2024-03-21 17:22:41 UTC; matteo
Repository: CRAN
Date/Publication: 2024-03-22 19:30:06 UTC
Built: R 4.3.3; powerpc-apple-darwin10.0.0d2; 2024-03-23 20:43:14 UTC; unix
