The main function biclust() provides several algorithms to
        find biclusters in two-dimensional data: Cheng and Church (2000, ISBN:1-57735-115-0),
        spectral (2003) <doi:10.1101/gr.648603>, plaid model (2005) <doi:10.1016/j.csda.2004.02.003>, xmotifs (2003) <doi:10.1142/9789812776303_0008> and bimax (2006) <doi:10.1093/bioinformatics/btl060>. In addition, the
        package provides methods for data preprocessing (normalization
        and discretisation), visualisation, and validation of bicluster
        solutions.

Prior transcription factor binding knowledge and target gene expression data are integrated in a Bayesian framework for functional cis-regulatory module inference. Using Gibbs sampling, we iteratively estimate transcription factor associations for each gene, regulation strength for each binding event and the hidden activity for each transcription factor.

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>.