DESCRIPTION

Package: SGS
Type: Package
Title: Inference in Bayesian Networks
Description: Implementation of common inference algorithms for Bayesian networks. Allows for efficient exact and approximate inference that works both in low- and high-dimensional settings. Efficient marginalization is reached by splitting the calculation into sub-calculations of lower dimensionality. Implemented approximate inference algoti Gibbs sampling, loopy belief propagation and SubGroupSeparation. 
	Implemented exact inference methods: SubGroupSeparation (fastest), junction-tree algorithm, complete enumeration. 
	Implemented approximate inference methods: SubGroupSeparation (highest accuracy), loopy belief propagation, Markov chain Monte Carlo (MCMC) sampling.
	References: Bayer, F.M., Moffa, G., Beerenwinkel, N. and Kuipers, J., 2021. High-Dimensional Inference in Bayesian Networks. arXiv preprint <doi:10.48550/arXiv.2112.09217>.
Author: Fritz Bayer [aut, cre]
Maintainer: Fritz Bayer <frbayer@ethz.ch>
Version: 1.0.0
Date: 2022-09-16
Depends:
    R (>= 3.5.0),
    bitops,
    methods
biocViews:
Suggests:
    graph,
    Rgraphviz,
    RBGL,
    qgraph,
    knitr,
    testthat (>= 3.0.0),
    rmarkdown
Imports: 
    igraph,
    stats,
    BiDAG,
    Bestie,
    cowplot,
    ggplot2,
    pcalg,
    RColorBrewer,
    gridExtra
License: GPL-3 | file LICENSE
Encoding: UTF-8
RoxygenNote: 7.2.1
LazyData: true
VignetteBuilder: knitr
Config/testthat/edition: 3
URL: https://cbg-ethz.github.io/SGS/