Package: BayesChange 2.3.0

Luca Danese

BayesChange: Bayesian Methods for Change Point Analysis

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>).

Authors:Luca Danese [aut, cre, cph], Riccardo Corradin [aut], Andrea Ongaro [aut]

BayesChange_2.3.0.tar.gz
BayesChange_2.3.0.zip(r-4.7)BayesChange_2.3.0.zip(r-4.6)BayesChange_2.3.0.zip(r-4.5)
BayesChange_2.3.0.tgz(r-4.6-x86_64)BayesChange_2.3.0.tgz(r-4.6-arm64)BayesChange_2.3.0.tgz(r-4.5-x86_64)BayesChange_2.3.0.tgz(r-4.5-arm64)
BayesChange_2.3.0.tar.gz(r-4.7-arm64)BayesChange_2.3.0.tar.gz(r-4.7-x86_64)BayesChange_2.3.0.tar.gz(r-4.6-arm64)BayesChange_2.3.0.tar.gz(r-4.6-x86_64)
BayesChange_2.3.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
BayesChange/json (API)

# Install 'BayesChange' in R:

install.packages('BayesChange', repos = c('https://lucadanese.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/lucadanese/bayeschange/issues

Uses libs:

openblas– Optimized BLAS
gsl– GNU Scientific Library (GSL)
c++– GNU Standard C++ Library v3
openmp– GCC OpenMP (GOMP) support library

Datasets:

epi_synthetic - Synthetic Epidemiological Time-Series Data
epi_synthetic_multi - Multivariate Synthetic Epidemiological Time-Series Data
eu_inflation - EU Inflation Dataset
stock_multi - Daily Stock Price Dataset
stock_uni - Daily Stock Price Dataset

On CRAN:

openblas gsl cpp openmp

4.65 score 5 scripts 454 downloads 7 exports 81 dependencies

Last updated from:31dbd4f7dd. Checks:13 OK. Indexed: yes.

Target	Result	Time
linux-devel-arm64	OK	281
linux-devel-x86_64	OK	191
source / vignettes	OK	318
linux-release-arm64	OK	272
linux-release-x86_64	OK	190
macos-release-arm64	OK	194
macos-release-x86_64	OK	323
macos-oldrel-arm64	OK	275
macos-oldrel-x86_64	OK	470
windows-devel	OK	218
windows-release	OK	174
windows-oldrel	OK	188
wasm-release	OK	209

Exports:clust_cp ClustCpObj detect_cp DetectCpObj plot_psm posterior_estimate sim_epi_data

Dependencies:abind backports boot broom car carData cli coda colorspace corrplot cowplot cpp11 Deriv doBy dplyr farver forecast Formula fracdiff generics ggplot2 ggpubr ggrepel ggsci ggsignif glue gridExtra gtable isoband labeling lattice lifecycle lme4 lmtest magrittr MASS Matrix MatrixModels mgcv minqa modelr nlme nloptr nnet numDeriv pbkrtest pillar pkgconfig plyr polynom purrr quantreg R6 rbibutils RColorBrewer Rcpp RcppArmadillo RcppEigen RcppGSL Rdpack reformulas reshape2 rlang rstatix S7 salso scales SparseM stringi stringr survival tibble tidyr tidyselect timeDate urca utf8 vctrs viridisLite withr zoo

BayesChange Tutorial

Detecting change points | Clustering time dependent data with common change points

Rendered fromtutorial.Rmdusingknitr::rmarkdown

Last update: 2026-01-14
Started: 2025-02-06

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Clustering time dependent observations with common change points.	clust_cp
ClustCpObj class constructor	ClustCpObj
Detect change points on time series.	detect_cp
DetectCpObj class constructor	DetectCpObj
Synthetic Epidemiological Time-Series Data	epi_synthetic
Multivariate Synthetic Epidemiological Time-Series Data	epi_synthetic_multi
EU Inflation Dataset (Standardized Matrix Form)	eu_inflation
Plot the Posterior Similarity Matrix (PSM) for a ClustCpObj	plot_psm.ClustCpObj
Plot estimated partition	plot.ClustCpObj
Plot estimated change points	plot.DetectCpObj
Estimate the change points of the data	posterior_estimate.ClustCpObj
Estimate the change points of the data	posterior_estimate.DetectCpObj
ClustCpObj print method	print.ClustCpObj
DetectCpObj print method	print.DetectCpObj
Simulate epidemiological data	sim_epi_data
Daily Stock Price Dataset (Multivariate Open/Close Series)	stock_multi
Daily Stock Price Dataset (Univariate Mean Series)	stock_uni
ClustCpObj summary method	summary.ClustCpObj
DetectCpObj summary method	summary.DetectCpObj