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blockcluster  

Co-Clustering Package for Binary, Categorical, Contingency and Continuous Data-Sets
View on CRAN: Click here


Download and install blockcluster package within the R console
Install from CRAN:
install.packages("blockcluster")

Install from Github:
library("remotes")
install_github("cran/blockcluster")

Install by package version:
library("remotes")
install_version("blockcluster", "4.5.5")



Attach the package and use:
library("blockcluster")
Maintained by
Serge Iovleff
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2012-07-04
Latest Update: 2024-02-23
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 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.
How to cite:
Serge Iovleff (2012). blockcluster: Co-Clustering Package for Binary, Categorical, Contingency and Continuous Data-Sets. R package version 4.5.5, https://cran.r-project.org/web/packages/blockcluster. Accessed 17 Jul. 2026.
Previous versions and publish date:
(2026-07-09 07:22), 1.0 (2012-07-04 18:56), 1.01 (2012-09-05 15:23), 2.0.1 (2013-02-22 16:21), 2.0.2 (2013-04-12 17:46), 2.0 (2013-02-22 12:13), 3.0.1 (2014-01-16 23:08), 3.0.2 (2015-01-23 12:38), 3.0 (2013-12-03 11:26), 4.0.2 (2015-11-27 11:41), 4.2.0 (2016-11-29 15:08), 4.2.1 (2016-11-30 14:55), 4.2.3 (2017-02-27 21:39), 4.2.6 (2018-01-24 18:56), 4.3.2 (2018-10-05 13:50), 4.4.3 (2019-03-26 19:00), 4.5.0 (2021-07-13 14:00), 4.5.1 (2021-07-28 13:20), 4.5.2 (2023-01-08 15:20), 4.5.3 (2023-02-17 23:10), 4.5.4 (2024-01-23 14:02), 4.5.5 (2024-02-23 14:40)
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