Package: blockcluster 4.5.5

blockcluster: Co-Clustering Package for Binary, Categorical, Contingency and Continuous Data-Sets

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 <doi:10.1016/j.csda.2007.09.007>, contingency <doi:10.1080/03610920903140197>, continuous <doi:10.1007/s11634-013-0161-3> and categorical data-sets <doi:10.1007/s11222-014-9472-2>. 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.

Authors:Serge Iovleff [aut, cre], Parmeet Singh Bhatia [aut], Josselin Demont [ctb], Vincent Brault [ctb], Vincent Kubicki [ctb], Gerard Goavert [ctb], Christophe Biernacki [ctb], Gilles Celeux [ctb]

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blockcluster.pdf |blockcluster.html
blockcluster/json (API)
NEWS

# Install 'blockcluster' in R:
install.packages('blockcluster', repos = c('https://joblion.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Uses libs:
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

2.90 score 1 stars 40 scripts 540 downloads 1 mentions 8 exports 3 dependencies

Last updated 9 months agofrom:240b062c1c. Checks:OK: 3 NOTE: 6. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 20 2024
R-4.5-win-x86_64OKNov 20 2024
R-4.5-linux-x86_64OKNov 20 2024
R-4.4-win-x86_64NOTENov 20 2024
R-4.4-mac-x86_64NOTENov 20 2024
R-4.4-mac-aarch64NOTENov 20 2024
R-4.3-win-x86_64NOTENov 20 2024
R-4.3-mac-x86_64NOTENov 20 2024
R-4.3-mac-aarch64NOTENov 20 2024

Exports:coclustercoclusterBinarycoclusterCategoricalcoclusterContingencycoclusterContinuouscoclusterStrategyplotsummary

Dependencies:inlineRcpprtkore

blockcluster tutorial

Rendered fromblockcluster_tutorial.Rnwusingutils::Sweaveon Nov 20 2024.

Last update: 2023-01-08
Started: 2015-11-27

Readme and manuals

Help Manual

Help pageTopics
Getter method for blockcluster output[ [,BinaryOptions-method [,CategoricalOptions-method [,ContingencyOptions-method [,ContinuousOptions-method [,strategy-method
Simulated Binary Data-setbinarydata
Binary input/output optionsBinaryOptions-class
Co-Clustering Packageblockcluster
Simulated categorical Data-setcategoricaldata
Categorical input/output optionsCategoricalOptions-class
Co-Clustering function.cocluster
Co-Clustering function for Binary data.coclusterBinary
Co-Clustering function for categorical data-sets.coclusterCategorical
Co-Clustering function.coclusterContingency
Co-Clustering function.coclusterContinuous
Strategy functioncoclusterStrategy strategy-class
Common Input/Output options.CommonOptions-class
Simulated Contingency Data-setcontingencydatalist
Simulated Contingency Data-setcontingencydataunknown
Contingency input/output optionsContingencyOptions-class
Continuous input/output optionsContinuousOptions-class
Simulated Gaussian Data-setgaussiandata
Plot function.plot plot,BinaryOptions-method plot,CategoricalOptions-method plot,ContingencyOptions-method plot,ContinuousOptions-method
Summary function.summary summary,BinaryOptions-method summary,CategoricalOptions-method summary,ContingencyOptions-method summary,ContinuousOptions-method summary,strategy-method
An EM strategy to obtain a good optimum.XEMStrategy