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.
| Version: | 3.1.7 |
| Depends: | R (≥ 4.0.0) |
| Imports: | doParallel, foreach, graph, lme4, mclogit, methods, nnet, Rcpp, Rgraphviz, rjags, stringi |
| LinkingTo: | Rcpp, RcppArmadillo |
| 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 |
| Published: | 2025-06-06 |
| DOI: | 10.32614/CRAN.package.abn |
| Author: | Matteo Delucchi 
[aut, cre],
Reinhard Furrer
[aut],
Gilles Kratzer
[aut],
Fraser Iain Lewis
[aut],
Jonas I. Liechti
[ctb],
Marta Pittavino
[ctb],
Kalina Cherneva [ctb] |
| Maintainer: | Matteo Delucchi <matteo.delucchi at math.uzh.ch> |
| BugReports: | https://github.com/furrer-lab/abn/issues |
| License: | GPL (≥ 3) |
| URL: | https://r-bayesian-networks.org/, https://github.com/furrer-lab/abn |
| NeedsCompilation: | yes |
| SystemRequirements: | pkg-config, cmake, gsl, jpeg, gdal, geos, proj, udunits-2, openssl, libcurl, jags |
| Additional_repositories: | https://inla.r-inla-download.org/R/stable/ |
| Citation: | abn citation info |
| Materials: | README |
| In views: | GraphicalModels |
| CRAN checks: | abn results |
Documentation:
| Reference manual: | abn.pdf |
| Vignettes: |
Data Simulation (source) Mixed-effect Bayesian Network Model (source, R code) Model Specification: Build a Cache of Scores (source, R code) Parallelisation (source) Parameter Learning (source) Quick Start Example (source, R code) Bayesian Network Structure Learning (source, R code) |
Downloads:
| Package source: | abn_3.1.7.tar.gz |
| Windows binaries: | r-devel: abn_3.1.1.zip, r-release: abn_3.1.1.zip, r-oldrel: abn_3.1.1.zip |
| macOS binaries: | r-release (arm64): abn_3.1.7.tgz, r-oldrel (arm64): abn_3.1.7.tgz, r-release (x86_64): abn_3.1.1.tgz, r-oldrel (x86_64): not available |
| Old sources: | abn archive |
Linking:
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