Author: E. Georg Luebeck <gluebeck@fhcrc.org>
Description: Functions for MLE, MCMC, CIs (originally in Fortran)
License: Unlimited distribution for noncommercial use.

Packaged: Wed Feb  8 12:58:20 2006; hornik

Author: Georg Luebeck <gluebeck@fhcrc.org> and Rafael Meza
Packaged: 2013-01-12 20:50:16 UTC; rafaelmeza

Description: Provides functions for Maximum Likelihood
Estimation, Markov Chain Monte Carlo, finding confidence
intervals.  The implementation is heavily based on the
original Fortran source code translated to R.
Author: Georg Luebeck [aut] (<https://orcid.org/0000-0002-4378-2829>),

Title: R Wrapper for Java Implementation of BiBit
Maintainer: De Troyer Ewoud <ewoud.detroyer@uhasselt.be>
Description: 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 adaption for the BiBit algorithm which allows noise in the biclusters is also included.

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. Further, a workflow to guide the algorithm towards given patterns is included as well.

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.

Title: Binary Causality Inference Framework
Description: 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>.
Suggests: knitr, rmarkdown, markdown, igraph
Author: Chainarong Amornbunchornvej [aut, cre]