Compare the Goodness of Fit of Benford's and Blondeau Da Silva's
Digit Distributions to a Given Dataset
Blondeau Da Silva Stephane <Stephane.Blondeau-Da-Silva@ac-limoges.fr>
Allows to compare the goodness of fit of Benford's and Blondeau Da Silva's digit distributions in a dataset. It is used to check whether the data distribution is consistent with theoretical distributions highlighted by Blondeau Da Silva or not (through the dat.distr() function): this ideal theoretical distribution must be at least approximately followed by the data for the use of Blondeau Da Silva's model to be well-founded. It also enables to plot histograms of digit distributions, both observed in the dataset and given by the two theoretical approaches (with the digit.ditr() function). Finally, it proposes to quantify the goodness of fit via Pearson's chi-squared test (with the chi2() function).

Functions for MLE, MCMC, CIs (originally in Fortran)
Unlimited distribution for noncommercial use.

Georg Luebeck <gluebeck@fhcrc.org> and Rafael Meza
<rmeza@umich.edu>

c(person(given = "Georg",
family = "Luebeck",
role = c("aut"),
email = "gluebeck@fhcrc.org",
comment = c(ORCID = "0000-0002-4378-2829")),
person(given = "Rafael",
family = "Meza",
role = c("aut", "cre"),
email = "rmeza@umich.edu",
comment = c(ORCID = "0000-0002-1076-5037")),
person(given = "Alexander",
family = "Gaenko",
role = "ctb",
email = "galexv@umich.edu",
comment = c(ORCID = "0000-0002-1901-0394")))
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.

R Wrapper for Java Implementation of BiBit
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.

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