Package: BiDAG
Type: Package
Title: Bayesian Inference for Directed Acyclic Graphs
Version: 2.1.4
Author: Polina Suter [aut, cre], Jack Kuipers [aut]
Maintainer: Polina Suter <polina.suter@gmail.com>
Description: Implementation of a collection of MCMC methods for Bayesian structure learning
    of directed acyclic graphs (DAGs), both from continuous and discrete data. For efficient
    inference on larger DAGs, the space of DAGs is pruned according to the data. To filter
    the search space, the algorithm employs a hybrid approach, combining constraint-based 
    learning with search and score. A reduced search space is initially defined on the basis
    of a skeleton obtained by means of the PC-algorithm, and then iteratively improved with
    search and score. Search and score is then performed following two approaches:
    Order MCMC, or Partition MCMC.
    The BGe score is implemented for continuous data and the BDe score is implemented
    for binary data or categorical data. The algorithms may provide the maximum a posteriori 
    (MAP) graph or a sample (a collection of DAGs) from the posterior distribution given the data.
    All algorithms are also applicable for structure learning and sampling for dynamic Bayesian networks.
    References:
    J. Kuipers, P. Suter, G. Moffa (2022) <doi:10.1080/10618600.2021.2020127>,
    N. Friedman and D. Koller (2003) <doi:10.1023/A:1020249912095>, 
    J. Kuipers and G. Moffa (2017) <doi:10.1080/01621459.2015.1133426>, 
    M. Kalisch et al. (2012) <doi:10.18637/jss.v047.i11>,
    D. Geiger and D. Heckerman (2002) <doi:10.1214/aos/1035844981>, 
    P. Suter, J. Kuipers, G. Moffa, N.Beerenwinkel (2023) <doi:10.18637/jss.v105.i09>.
Acknowledgments: We would like to thank Giusi Moffa for discussion and
        comments on the package and its manual.
License: GPL (>= 2)
Depends: R (>= 3.5.0)
Imports: Rcpp (>= 0.12.7), methods, graph, Rgraphviz, RBGL, pcalg,
        graphics, Matrix, coda
LinkingTo: Rcpp
RoxygenNote: 7.2.0
Encoding: UTF-8
LazyData: TRUE
NeedsCompilation: yes
Packaged: 2023-05-15 11:04:33 UTC; polinasuter
Repository: CRAN
Date/Publication: 2023-05-16 12:46:02 UTC
Built: R 4.2.3; x86_64-w64-mingw32; 2024-04-24 02:35:49 UTC; windows
ExperimentalWindowsRuntime: ucrt
Archs: x64
