Package: cord 0.2.0

cord: Community Estimation in G-Models via CORD

Partitions data points (variables) into communities/clusters, similar to clustering algorithms such as k-means and hierarchical clustering. This package implements a clustering algorithm based on a new metric CORD, defined for high-dimensional parametric or semiparametric distributions. For more details see Bunea et al. (2020), Annals of Statistics <doi:10.1214/18-AOS1794>.

Authors:Xi LUO [aut, cre], Florentina Bunea [aut], Christophe Giraud [aut]

cord_0.2.0.tar.gz
cord_0.2.0.zip(r-4.7)cord_0.2.0.zip(r-4.6)cord_0.2.0.zip(r-4.5)
cord_0.2.0.tgz(r-4.6-x86_64)cord_0.2.0.tgz(r-4.6-arm64)cord_0.2.0.tgz(r-4.5-x86_64)cord_0.2.0.tgz(r-4.5-arm64)
cord_0.2.0.tar.gz(r-4.7-arm64)cord_0.2.0.tar.gz(r-4.7-x86_64)cord_0.2.0.tar.gz(r-4.6-arm64)cord_0.2.0.tar.gz(r-4.6-x86_64)
cord_0.2.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
cord/json (API)

# Install 'cord' in R:

install.packages('cord', repos = c('https://rluo.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/rluo/cord/issues

Uses libs:

c++– GNU Standard C++ Library v3

On CRAN:

cpp

2.00 score 3 scripts 247 downloads 1 exports 2 dependencies

Last updated from:f2a73b9aa3. Checks:13 OK. Indexed: yes.

Target	Result	Time
linux-devel-arm64	OK	119
linux-devel-x86_64	OK	134
source / vignettes	OK	158
linux-release-arm64	OK	116
linux-release-x86_64	OK	123
macos-release-arm64	OK	137
macos-release-x86_64	OK	261
macos-oldrel-arm64	OK	137
macos-oldrel-x86_64	OK	236
windows-devel	OK	97
windows-release	OK	94
windows-oldrel	OK	103
wasm-release	OK	119