Package: oneclust 0.3.0

oneclust: Maximum Homogeneity Clustering for Univariate Data
Maximum homogeneity clustering algorithm for one-dimensional data described in W. D. Fisher (1958) <doi:10.1080/01621459.1958.10501479> via dynamic programming.
Authors:
oneclust_0.3.0.tar.gz
oneclust_0.3.0.zip(r-4.7-x86_64)oneclust_0.3.0.zip(r-4.7-arm64)oneclust_0.3.0.zip(r-4.6-x86_64)oneclust_0.3.0.zip(r-4.6-arm64)oneclust_0.3.0.zip(r-4.5-x86_64)
oneclust_0.3.0.tgz(r-4.6-x86_64)oneclust_0.3.0.tgz(r-4.6-arm64)oneclust_0.3.0.tgz(r-4.5-x86_64)oneclust_0.3.0.tgz(r-4.5-arm64)
oneclust_0.3.0.tar.gz(r-4.7-arm64)oneclust_0.3.0.tar.gz(r-4.7-x86_64)oneclust_0.3.0.tar.gz(r-4.6-arm64)oneclust_0.3.0.tar.gz(r-4.6-x86_64)
oneclust_0.3.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
oneclust/json (API)
| # Install 'oneclust' in R: |
| install.packages('oneclust', repos = c('https://nanxstats.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/nanxstats/oneclust/issues
Pkgdown/docs site:https://nanx.me
clustering-algorithmfeature-engineeringhomogeneitypeak-callingunivariate-datacpp
Last updated from:121505b164. Checks:15 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 108 | ||
| linux-devel-x86_64 | OK | 130 | ||
| source / vignettes | OK | 192 | ||
| linux-release-arm64 | OK | 102 | ||
| linux-release-x86_64 | OK | 119 | ||
| macos-release-arm64 | OK | 99 | ||
| macos-release-x86_64 | OK | 265 | ||
| macos-oldrel-arm64 | OK | 81 | ||
| macos-oldrel-x86_64 | OK | 157 | ||
| windows-devel-arm64 | OK | 175 | ||
| windows-devel-x86_64 | OK | 128 | ||
| windows-release-arm64 | OK | 195 | ||
| windows-release-x86_64 | OK | 85 | ||
| windows-oldrel-x86_64 | OK | 79 | ||
| wasm-release | OK | 105 |
Exports:cudoneclustsim_postcode_levelssim_postcode_samples
Dependencies:Rcpp
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Masataka Okabe and Kei Ito's Color Universal Design palette | cud |
| Maximum homogeneity clustering for one-dimensional data | oneclust |
| Simulate the levels and their sizes in a high-cardinality feature | sim_postcode_levels |
| Simulate a high-cardinality feature and a binary response | sim_postcode_samples |