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prclust  

Penalized Regression-Based Clustering Method
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("prclust", "1.3")



Attach the package and use:
library("prclust")
Maintained by
Chong Wu
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2015-11-06
Latest Update: 2016-12-13
Description:
Clustering is unsupervised and exploratory in nature. Yet, it can be performed through penalized regression with grouping pursuit. In this package, we provide two algorithms for fitting the penalized regression-based clustering (PRclust) with non-convex grouping penalties, such as group truncated lasso, MCP and SCAD. One algorithm is based on quadratic penalty and difference convex method. Another algorithm is based on difference convex and ADMM, called DC-ADD, which is more efficient. Generalized cross validation and stability based method were provided to select the tuning parameters. Rand index, adjusted Rand index and Jaccard index were provided to estimate the agreement between estimated cluster memberships and the truth.
How to cite:
Chong Wu (2015). prclust: Penalized Regression-Based Clustering Method. R package version 1.3, https://cran.r-project.org/web/packages/prclust. Accessed 28 Jun. 2026.
Previous versions and publish date:
1.0 (2015-11-06 17:24), 1.1 (2015-11-13 23:29), 1.2 (2016-07-20 00:38), 1.3 (2016-12-13 07:57)
Other packages that cited prclust R package
View prclust citation profile
Other R packages that prclust depends, imports, suggests or enhances
Complete documentation for prclust
Functions, R codes and Examples using the prclust R package
Some associated functions: GCV . PRclust . clusterStat . prclust-package . stability . 
Some associated R codes: GCVOrignial.R . RcppExports.R . stability.R .  Full prclust package functions and examples
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