doi:10.1214/14-BA878> and Corradin, Danese and Ongaro (2022) <doi:10.1016/j.ijar.2021.12.019>. It also clusters different types of time dependent data with common change points, see "Model-based clustering of time-dependent observations with common structural changes" (Corradin,Danese,KhudaBukhsh and Ongaro, 2024) <doi:10.48550/arXiv.2410.09552> for details.">

BayesChange: Bayesian Methods for Change Points Analysis (original) (raw)

Perform change points detection on univariate and multivariate time series according to the methods presented by Asael Fabian Martínez and Ramsés H. Mena (2014) <doi:10.1214/14-BA878> and Corradin, Danese and Ongaro (2022) <doi:10.1016/j.ijar.2021.12.019>. It also clusters different types of time dependent data with common change points, see "Model-based clustering of time-dependent observations with common structural changes" (Corradin,Danese,KhudaBukhsh and Ongaro, 2024) <doi:10.48550/arXiv.2410.09552> for details.

Version: 2.1.3
Depends: R (≥ 3.5.0)
Imports: Rcpp, salso, dplyr, tidyr, ggplot2, ggpubr
LinkingTo: Rcpp, RcppArmadillo, RcppGSL
Suggests: knitr, rmarkdown, testthat (≥ 3.0.0)
Published: 2025-11-11
DOI: 10.32614/CRAN.package.BayesChange
Author: Luca Danese ORCID iD [aut, cre, cph], Riccardo Corradin [aut], Andrea Ongaro [aut]
Maintainer: Luca Danese <l.danese1 at campus.unimib.it>
BugReports: https://github.com/lucadanese/BayesChange/issues
License: GPL (≥ 3)
URL: https://github.com/lucadanese/BayesChange
NeedsCompilation: yes
Materials: README, NEWS
In views: TimeSeries
CRAN checks: BayesChange results

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