Package: Ckmeans.1d.dp 4.3.5
Ckmeans.1d.dp: 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.
Authors:
Ckmeans.1d.dp_4.3.5.tar.gz
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Ckmeans.1d.dp.pdf |Ckmeans.1d.dp.html✨
Ckmeans.1d.dp/json (API)
NEWS
| # Install 'Ckmeans.1d.dp' in R: |
| install.packages('Ckmeans.1d.dp', repos = c('https://joemsong.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 1 years agofrom:1f5ef9bdc3. Checks:OK: 9. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Sep 08 2024 |
| R-4.5-win-x86_64 | OK | Sep 08 2024 |
| R-4.5-linux-x86_64 | OK | Sep 08 2024 |
| R-4.4-win-x86_64 | OK | Sep 08 2024 |
| R-4.4-mac-x86_64 | OK | Sep 08 2024 |
| R-4.4-mac-aarch64 | OK | Sep 08 2024 |
| R-4.3-win-x86_64 | OK | Sep 08 2024 |
| R-4.3-mac-x86_64 | OK | Sep 08 2024 |
| R-4.3-mac-aarch64 | OK | Sep 08 2024 |
Exports:ahistCkmeans.1d.dpCkmedian.1d.dpCksegs.1d.dpMultiChannel.WUCplot.Ckmeans.1d.dpplot.Ckmedian.1d.dpplot.Cksegs.1d.dpplot.MultiChannelClustersplotBICprint.Ckmeans.1d.dpprint.Ckmedian.1d.dpprint.Cksegs.1d.dp
Note: Weight scaling in cluster analysis
Rendered fromWeights.Rmdusingknitr::rmarkdownon Sep 08 2024.Last update: 2019-09-07
Started: 2017-07-09
Tutorial: Adaptive versus regular histograms
Rendered fromahist.Rmdusingknitr::rmarkdownon Sep 08 2024.Last update: 2022-01-31
Started: 2016-12-05
Tutorial: Optimal univariate clustering
Rendered fromCkmeans.1d.dp.Rmdusingknitr::rmarkdownon Sep 08 2024.Last update: 2022-01-31
Started: 2016-12-05
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Optimal, Fast, and Reproducible Univariate Clustering | Ckmeans.1d.dp-package |
| Adaptive Histograms | ahist |
| Optimal Multi-channel Weighted Univariate Clustering | MultiChannel.WUC |
| Plot Optimal Univariate Clustering Results | plot.Ckmeans.1d.dp plot.Ckmedian.1d.dp |
| Plot Optimal Univariate Segmentation Results | plot.Cksegs.1d.dp |
| Plot Multi-Channel Clustering Results | plot.MultiChannelClusters |
| Plot Bayesian Information Criterion as a Function of Number of Clusters | plotBIC |
| Print Optimal Univariate Clustering Results | print.Ckmeans.1d.dp print.Ckmedian.1d.dp |
| Print Optimal Univariate Segmentation Results | print.Cksegs.1d.dp |
| Optimal (Weighted) Univariate Clustering | Ckmeans.1d.dp Ckmedian.1d.dp Univariate Clustering |
| Optimal Univariate Segmentation | Cksegs.1d.dp Univariate Segmentation |
