Ckmeans.1d.dp statistics

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Ckmeans.1d.dp specifications


Unique identifier OMICS_19461
Name Ckmeans.1d.dp
Software type Package/Module
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux, Mac OS, Windows
Programming languages C++, R
License GNU General Public License version 3.0
Computer skills Advanced
Version 4.2.2
Stability Stable
testthat, rmarkdown, knitr, Rcpp(≥0.12.18)
Maintained Yes


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  • person_outline Haizhou Wang <>
  • person_outline Mingzhou Song <>

Publication for Ckmeans.1d.dp

Ckmeans.1d.dp in publications

PMCID: 5692148
PMID: 29162974
DOI: 10.1177/1176935117740132

[…] under all settings., we first analyzed the papilloma gene expression data set for single-gene dose-response using funchisq. the expression of each gene was quantized into 4 levels, using r package “ckmeans.1d.dp” relying on dynamic programming to guarantee optimal clustering for sample points of each gene. then, we generated 3 × 4 contingency tables with each gene’s discrete expression level […]

PMCID: 5645324
PMID: 29042602
DOI: 10.1038/s41598-017-13640-5

[…] k-means algorithm is unable to guarantee an optimal number of clusters for univariate data. as in this study we used one-dimensional data (i.e., actors’ positional dynamicity), we used the ‘ckmeans.1d.dp’ algorithm developed by wang and song in, which performs optimal one-dimensional k-means clustering via dynamic programming. using this evaluation method, we first attempted […]

PMCID: 5499742
PMID: 28486705
DOI: 10.1093/nar/gkx361

[…] we retained the starting position, range lengths and n values of all significant fits on the basis of p-value. we then used an optimized one-dimensional k means clustering algorithm in the r ‘ckmeans.1d.dp’ package () to localize strongly significant starting locations of histone periods. for all adjacent (i, j) pairs of cluster positions representing the putative best locations […]

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Ckmeans.1d.dp institution(s)
Department of Computer Science, New Mexico State University, NM, USA
Ckmeans.1d.dp funding source(s)
Supported by U.S. National Science Foundation with the grant number HRD-0420407.

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