CRAN/E | Ckmeans.1d.dp

Ckmeans.1d.dp

Optimal, Fast, and Reproducible Univariate Clustering

Installation

About

Fast, optimal, and reproducible weighted univariate clustering by dynamic programming. Four problems are solved, including univariate k-means (Wang & Song 2011) doi:10.32614/RJ-2011-015 (Song & Zhong 2020) doi:10.1093/bioinformatics/btaa613, k-median, k-segments, and multi-channel weighted k-means. Dynamic programming is used to minimize the sum of (weighted) within-cluster distances using respective metrics. Its advantage over heuristic clustering in efficiency and accuracy is pronounced when there are many clusters. Multi-channel weighted k-means groups multiple univariate signals into k clusters. An auxiliary function generates histograms adaptive to patterns in data. This package provides a powerful set of tools for univariate data analysis with guaranteed optimality, efficiency, and reproducibility, useful for peak calling on temporal, spatial, and spectral data.

Citation Ckmeans.1d.dp citation info

Key Metrics

Version 4.3.5
Published 2023-08-19 245 days ago
Needs compilation? yes
License LGPL (≥ 3)
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Maintainer

Maintainer

Joe Song

joemsong@cs.nmsu.edu

Authors

Joe Song

aut / cre

Hua Zhong

aut

Haizhou Wang

aut

Material

README
NEWS
Reference manual
Package source

Vignettes

Tutorial: Optimal univariate clustering
Note: Weight scaling in cluster analysis
Tutorial: Adaptive versus regular histograms

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

Ckmeans.1d.dp archive

Imports

Rcpp
Rdpack ≥ 0.6-1

Suggests

testthat
knitr
rmarkdown
RColorBrewer

LinkingTo

Rcpp

Reverse Depends

GenomicOZone

Reverse Imports

CellBarcode
clusterHD
GridOnClusters
Harman
OptCirClust
SPECK
STREAK
weitrix

Reverse Suggests

autostats
bakR
DiffXTables
FunChisq
xgboost