Title: Co-Clustering package for binary, contingency, continuous and
Author: Parmeet Singh Bhatia <bhatia.parmeet@gmail.com>, Serge Iovleff, Gerard
Goavert, Vincent Brault, with contributions from Christophe Biernacki and
Description: Simultaneous clustering of rows and columns, usually designated by
biclustering, co-clustering or block clustering, is an important technique
in two way data analysis. It consists of estimating a mixture model which
takes into account the block clustering problem on both the individual and
variables sets. The blockcluster package provides a bridge between the C++
core library and the R statistical computing environment. This package
allows to co-cluster binary, contingency, continuous and categorical
data-sets. It also provides utility functions to visualize the results.
This package may be useful for various applications in fields of Data
mining,Information retrieval, Biology, computer vision and many more. More
information about the project and comprehensive tutorial can be found on
Collate: 'Rcoclust.R' 'Strategy.R' 'summary.R' 'plot.R'

Title: Coclustering package for binary, contingency, continuous and

Author: Parmeet Bhatia & Serge Iovleff & Gerard Goavert & Vincent Brault, with

Title: Coclustering Package for Binary, Categorical, Contingency and
Authors@R: c( person("Serge", "Iovleff", role = c("aut","cre"), email = "Serge.Iovleff@stkpp.org")
, person("Parmeet", "Singh Bhatia", role = "aut", email = "bhatia.parmeet@gmail.com")
, person("Vincent", "Kubicki", role = "ctb", email = "Vincent.Kubicki@inria.fr")
, person("Gerard", "Goavert", role = "ctb")
, person("Vincent", "Brault", role = "ctb")
, person("Christophe", "Biernacki", role = "ctb")
, person("Gilles", "Celeux", role = "ctb"))

, person("Josselin", "Demont", role = "ctb")
mining, Information retrieval, Biology, computer vision and many more. More

core library build on top of the 'STK++' library, and the R statistical
computing environment. This package allows to co-cluster binary, contingency,
continuous and categorical data-sets. It also provides utility functions to
visualize the results. This package may be useful for various applications
in fields of Data mining, Information retrieval, Biology, computer vision
and many more. More information about the project and comprehensive tutorial
can be found on the link mentioned in URL.