A framework to infer causality on binary data using techniques in frequent pattern mining and estimation statistics. Given a set of individual vectors S={x} where x(i) is a realization value of binary variable i, the framework infers empirical causal relations of binary variables i,j from S in a form of causal graph G=(V,E) where V is a set of nodes representing binary variables and there is an edge from i to j in E if the variable i causes j. The framework determines dependency among variables as well as analyzing confounding factors before deciding whether i causes j.  The publication of this package is at Chainarong Amornbunchornvej, Navaporn Surasvadi, Anon Plangprasopchok, and Suttipong Thajchayapong (2023) <doi:10.1016/j.heliyon.2023.e15947>.

Implements methods for sample size 
  reduction within Linear and Quadratic Discriminant Analysis in Lapanowski and Gaynanova (2020) <arXiv:2005.03858>.
  Also includes methods for non-linear discriminant analysis with simultaneous sparse feature selection in Lapanowski and Gaynanova (2019) PMLR 89:1704-1713.

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.

Biclustering is a statistical learning technique that simultaneously 
    partitions and clusters rows and columns of a data matrix. Since the solution 
    space of biclustering is in infeasible to completely search with current 
    computational mechanisms, this package uses a greedy heuristic. The algorithm 
    featured in this package is, to the best our knowledge, the first biclustering 
    algorithm to work on data with missing values. Li, J., Reisner, J., Pham, H., 
    Olafsson, S., and Vardeman, S. (2020) Biclustering with Missing Data. Information 
    Sciences, 510, 304–316.