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BayesCPclust: A Bayesian Approach for Clustering Constant-Wise Change-Point Data

A Gibbs sampler algorithm was developed to estimate change points in constant-wise data sequences while performing clustering simultaneously. The algorithm is described in da Cruz, A. C. and de Souza, C. P. E "A Bayesian Approach for Clustering Constant-wise Change-point Data" <doi:10.48550/arXiv.2305.17631>.

Version: 0.1.0
Depends: R (≥ 3.5.0)
Imports: extraDistr, RcppAlgos, stats
Suggests: testthat (≥ 3.0.0)
Published: 2025-01-29
DOI: 10.32614/CRAN.package.BayesCPclust
Author: Ana Carolina da Cruz [aut, cre]
Maintainer: Ana Carolina da Cruz <adacruz at uwo.ca>
License: MIT + file LICENSE
NeedsCompilation: no
Materials: README
CRAN checks: BayesCPclust results

Documentation:

Reference manual: BayesCPclust.pdf

Downloads:

Package source: BayesCPclust_0.1.0.tar.gz
Windows binaries: r-devel: BayesCPclust_0.1.0.zip, r-release: BayesCPclust_0.1.0.zip, r-oldrel: BayesCPclust_0.1.0.zip
macOS binaries: r-devel (arm64): BayesCPclust_0.1.0.tgz, r-release (arm64): BayesCPclust_0.1.0.tgz, r-oldrel (arm64): BayesCPclust_0.1.0.tgz, r-devel (x86_64): BayesCPclust_0.1.0.tgz, r-release (x86_64): BayesCPclust_0.1.0.tgz, r-oldrel (x86_64): BayesCPclust_0.1.0.tgz

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These binaries (installable software) and packages are in development.
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