A simple R wrapper for the Java BiBit algorithm from "A
biclustering algorithm for extracting bit-patterns from binary datasets"
from Domingo et al. (2011) <DOI:10.1093/bioinformatics/btr464>. An simple adaption for the BiBit algorithm which allows noise in the biclusters is also introduced as well as a function to guide the algorithm towards given (sub)patterns. Further, a workflow to derive noisy biclusters from discoverd larger column patterns is included as well.

person(given = "Chainarong",
family = "Amornbunchornvej",
role = c("aut", "cre"),
email = "grandca@gmail.com",
comment = c(ORCID = "0000-0003-3131-0370"))
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 <arXiv:2205.06131>.
Chainarong Amornbunchornvej [aut, cre]
(<https://orcid.org/0000-0003-3131-0370>)

Bayesian Inference for Directed Acyclic Graphs (BiDAG): Software
for the Efficient Inference and Sampling of Bayesian Networks
Polina Minkina [aut, cre], Jack Kuipers [aut]
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. The algorithms may provide the maximum a posteriori (MAP) graph or
a sample (a collection of DAGs) from the posterior distribution given the data.
We would like to thank Giusi Moffa for discussion and
comments on the package and its manual.
Rcpp (>= 0.12.7), pcalg, methods, stats, utils