Package: prclust 1.3

prclust: Penalized Regression-Based Clustering Method

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.

Authors:Chong Wu, Wei Pan

prclust_1.3.tar.gz
prclust_1.3.zip(r-4.5)prclust_1.3.zip(r-4.4)prclust_1.3.zip(r-4.3)
prclust_1.3.tgz(r-4.4-x86_64)prclust_1.3.tgz(r-4.4-arm64)prclust_1.3.tgz(r-4.3-x86_64)prclust_1.3.tgz(r-4.3-arm64)
prclust_1.3.tar.gz(r-4.5-noble)prclust_1.3.tar.gz(r-4.4-noble)
prclust_1.3.tgz(r-4.4-emscripten)prclust_1.3.tgz(r-4.3-emscripten)
prclust.pdf |prclust.html
prclust/json (API)

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

Peer review:

Bug tracker:https://github.com/chongwu-biostat/prclust/issues

Uses libs:
  • c++– GNU Standard C++ Library v3

On CRAN:

4 exports 0.62 score 1 dependencies 6 scripts 167 downloads

Last updated 7 years agofrom:3da1a5ebea. Checks:OK: 1 NOTE: 8. Indexed: yes.

TargetResultDate
Doc / VignettesOKAug 24 2024
R-4.5-win-x86_64NOTEAug 24 2024
R-4.5-linux-x86_64NOTEAug 24 2024
R-4.4-win-x86_64NOTEAug 24 2024
R-4.4-mac-x86_64NOTEAug 24 2024
R-4.4-mac-aarch64NOTEAug 24 2024
R-4.3-win-x86_64NOTEAug 24 2024
R-4.3-mac-x86_64NOTEAug 24 2024
R-4.3-mac-aarch64NOTEAug 24 2024

Exports:clusterStatGCVPRcluststability

Dependencies:Rcpp