doi:10.1214/14-BA878> ; Corradin, Danese & Ongaro, 2022, <doi:10.1016/j.ijar.2021.12.019>) and clusters time-dependent data with common change points (Corradin, Danese, KhudaBukhsh & Ongaro, 2026, <doi:10.1007/s11222-025-10756-x>).">

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

Performs change point detection on univariate and multivariate time series (Martínez & Mena, 2014, <doi:10.1214/14-BA878> ; Corradin, Danese & Ongaro, 2022, <doi:10.1016/j.ijar.2021.12.019>) and clusters time-dependent data with common change points (Corradin, Danese, KhudaBukhsh & Ongaro, 2026, <doi:10.1007/s11222-025-10756-x>).

Version: 2.3.0
Depends: R (≥ 3.5.0)
Imports: Rcpp, salso, dplyr, tidyr, ggplot2, ggpubr, coda, rlang, reshape2
LinkingTo: Rcpp, RcppArmadillo, RcppGSL
Suggests: knitr, rmarkdown, testthat (≥ 3.0.0)
Published: 2026-03-06
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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