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cclustr  

Consensus Clustering Methods for Multiple Imputed Data
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("cclustr", "0.1.1")



Attach the package and use:
library("cclustr")
Maintained by
Andres Montenegro Lemus
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-05-18
Latest Update: 2026-05-18
Description:
Provides tools for performing consensus clustering on multiple imputed datasets. The package supports a range of clustering algorithms across imputations, including hierarchical methods (e.g., Ward, single, complete, average) and partition-based approaches such as k-means, k-medoids (PAM), fuzzy clustering, model-based clustering ('mclust'), and methods for mixed or categorical data (k-modes and k-prototypes). A co-assignment matrix is constructed to quantify agreement between partitions, and consensus solutions are derived via hierarchical clustering applied to the resulting dissimilarity matrix. Additional functions are provided for validation and visualization of clustering results, facilitating robust analysis in the presence of missing data. Consensus clustering framework is based on Monti et al. (2003) <doi:10.1023/A:1023949509487>, rank aggregation methods follow Pihur et al. (2007) <doi:10.1093/bioinformatics/btm158>, and the PAC (Proportion of Ambiguous Clustering) metric is based on Senbabaoglu et al. (2014) <doi:10.1038/srep06207>.
How to cite:
Andres Montenegro Lemus (2026). cclustr: Consensus Clustering Methods for Multiple Imputed Data. R package version 0.1.1, https://cran.r-project.org/web/packages/cclustr. Accessed 28 Jul. 2026.
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