Source: r-cran-ckmeans.1d.dp
Standards-Version: 4.7.4
Maintainer: Debian R Packages Maintainers <r-pkg-team@alioth-lists.debian.net>
Uploaders:
 Charles Plessy <plessy@debian.org>,
Section: gnu-r
Testsuite: autopkgtest-pkg-r
Build-Depends:
 debhelper-compat (= 13),
 dh-r,
 r-base-dev,
 r-cran-rcpp,
 r-cran-rdpack,
 architecture-is-64-bit,
 architecture-is-little-endian,
Vcs-Browser: https://salsa.debian.org/r-pkg-team/r-cran-ckmeans.1d.dp
Vcs-Git: https://salsa.debian.org/r-pkg-team/r-cran-ckmeans.1d.dp.git
Homepage: https://cran.r-project.org/package=Ckmeans.1d.dp

Package: r-cran-ckmeans.1d.dp
Architecture: any
Depends:
 ${R:Depends},
 ${shlibs:Depends},
 ${misc:Depends},
Recommends:
 ${R:Recommends},
Suggests:
 ${R:Suggests},
Description: Optimal, Fast, and Reproducible Univariate Clustering
 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.