R Observatory: Metrics for Rbeast version 1.0.2

Balamuta, James Joseph

doi:10.5281/zenodo.21843040

Rbeast

1.0.2Package record

Bayesian Change-Point Detection and Time Series Decomposition

0packages dependsuggested by 1

8Kdownloads / year

–test coverage

13/13flavors OK

Overview

About

Maintained by Kaiguang ZhaoCRAN page ↗GitHub ↗

BEAST is a Bayesian estimator of abrupt change, seasonality, and trend for decomposing univariate time series and 1D sequential data. Interpretation of time series depends on model choice; different models can yield contrasting or contradicting estimates of patterns, trends, and mechanisms. BEAST alleviates this by abandoning the single-best-model paradigm and instead using Bayesian model averaging over many competing decompositions. It detects and characterizes abrupt changes (changepoints, breakpoints, structural breaks, joinpoints), cyclic or seasonal variation, and nonlinear trends. BEAST not only detects when changes occur but also quantifies how likely the changes are true. It estimates not just piecewise linear trends but also arbitrary nonlinear trends. BEAST is generically applicable to any real-valued time series, such as those from remote sensing, economics, climate science, ecology, hydrology, and other environmental and biological systems. Example applications include identifying regime shifts in ecological data, mapping forest disturbance and land degradation from satellite image time series, detecting market trends in economic indicators, pinpointing anomalies and extreme events in climate records, and analyzing system dynamics in biological time series. Details are given in Zhao et al. (2019) doi:10.1016/j.rse.2019.04.034.

Changes in 1.0.2

Add two new arguments s.complexfct and t.complexfct ( prior$seasonComplexityFactor and prior$trendComplexityFactor)
to allow users to mannually tune how complex the seasonal and trend curves are, rather than using the default
non-informative priors (s.complexfct=0, and t.complexfct=0).

From NEWS.md in the release

Install

R (CRAN)pak (dev)Ubuntu (r2u)Fedora (COPR)openSUSE (OBS)

install.packages("Rbeast", repos = "https://cloud.r-project.org/")

Installs the current CRAN release. Prebuilt binaries on Windows and macOS.

if (!requireNamespace("pak", quietly = TRUE))
  install.packages("pak", repos = "https://cloud.r-project.org/")
pak::pak("zhaokg/Rbeast")

Development version from zhaokg/Rbeast. Installs pak first if it is not already available.

Release

# enable cranapt (r2u) if it is not already set up, then install
grep -rqs r2u.stat.illinois.edu /etc/apt/sources.list /etc/apt/sources.list.d/ \
  || curl -fsSL https://raw.githubusercontent.com/eddelbuettel/r2u/master/inst/scripts/add_cranapt_noble.sh | sudo bash
sudo apt install -y r-cran-rbeast

Fast Ubuntu binaries via r2u (no compilation). The cranapt setup runs once and is skipped when it is already enabled. We list this channel from recorded r2u download activity rather than from a live index of the repository, so treat it as last known rather than guaranteed.

bspm (used by r2u) also needs make: sudo apt install make

# enable the iucar/cran COPR if it is not already present, then install
ls /etc/yum.repos.d/*iucar*cran*.repo >/dev/null 2>&1 \
  || sudo dnf copr enable -y iucar/cran
sudo dnf install -y R-CRAN-Rbeast

RPMs from the iucar/cran COPR, rebuilt from CRAN.

For install.packages() integration in R, also: sudo dnf install R-CoprManager

# add the autoCRAN OBS repo (Tumbleweed) if it is missing, then install
zypper -q lr | grep -qi autoCRAN \
  || sudo zypper addrepo https://download.opensuse.org/repositories/devel:/languages:/R:/autoCRAN/openSUSE_Tumbleweed/devel:languages:R:autoCRAN.repo
sudo zypper install -y R-Rbeast

RPMs auto-built from CRAN on openSUSE OBS (Tumbleweed). We list this channel from recorded autoCRAN download activity rather than from a live index of the repository, so treat it as last known rather than guaranteed.

Health

CRAN checks

13OK

Additional issues

linux-arm64

CRAN check page ↗

Slowest check: 4.5 min · r-devel-windows-x86_64

Status changes

OK13 OK2026-09-08
ERROR12 OK, 1 ERROR2026-09-07
OK13 OK2026-08-05
NOTE12 OK, 1 NOTE2026-08-01
OK14 OK2026-03-10

Advisories

none known

How this is measured·How advisories are found

Documentation

In release 1.0.2

READMEYes · 5,661 wordsVignettesNoNEWSYes · 100% structured · mentions 1.0.2Citation fileYes · cites a publicationWebsitenot declared in DESCRIPTION

Help pages: 16
Examples that run: 81%13 run in a plain check · 3 only when donttest examples are enabled (R CMD check --as-cran)
Exported functions with examples: 100%
Documented parameters: 97%
Return-value docs: 100%
Help pages citing references: 100%

How these are measured

Downloads

8K

CRAN downloads in the past year

Rank #4,622 · ~22/day · ~668/mo

Releases R versions

Daily download trend is not available in this view yet.

64530 days

1.8K90 days

8K1 year

Compare downloads with other packages →

Also on368 r2u5 autocran125 c2d4u

How downloads are counted

Repository

zhaokg/rbeast ↗Account: zhaokg

374Stars

50Forks

19Open issues

0Open PRs

3Releases

224Commits

7Commit identities

time-serieschangepoint-detectiontime-series-decompositionbayesian-time-seriestrend-analysisbreakpoint-detectioninterrupted-time-seriesseasonality-analysis

224 commits · Last activity 2026-04-30 · +0.8% stars, 30d

Responsiveness

How these are sampled

PRs merged (all time): 83%
Median days to close, 50 most recently updated closed issues: 161
Median days to close, 50 most recently updated closed PRs: 1

Stars over time

2025-10-01 · 3342026-10-18 · 374

How these are measured

PracticesBeta

Checks run against github.com/zhaokg/rbeast on 2026-10-11.

No development-tooling practices detected in the upstream repository.

How this is detected·Detection ruleset v3 (2026-09-26)

Dependencies

Declared dependencies

0 external dependencies (excludes base and recommended)

Installing Rbeast adds no packages beyond those that come with R.

Depends (3)

R >= 2.10.0methodsutils

Imports (1)

grid

LinkingTo (0)

none

Suggests (0)

none

Enhances (0)

none

Dependency license breakdown →

Reverse dependencies

0direct

0indirect

Suggested by 1

Suggests, most downloaded first

ggchangepoint

How these are counted

Code & Tests

Datasets

People & History

People (6)

Kaiguang Zhao

Author, Maintainer since 0.1 (2019)

Tongxi Hu

Author

Yang Li

Author

Xuesong Zhang

Author · added in 0.9.6

Jack Dongarra

Contributor · added in 0.2.2

Cleve Moler

Contributor · added in 0.2.2

Releases (17)

Pick two releases to compare their code metrics.

R
R 4.6.0 released · 2026-04-24
1.0.2Latest
record
2025-12-19 · current release · diff ↗
R
R 4.5.0 released · 2025-04-11
1.0.1
record
2024-08-30 · diff ↗
R
R 4.4.0 released · 2024-04-24
1.0.0
record
2023-12-08 · diff ↗
0.9.9
record
2023-05-14 · diff ↗
0.9.8
record
2023-05-11 · diff ↗
R
R 4.3.0 released · 2023-04-21
0.9.7
record
2023-01-22 · diff ↗
0.9.6
record
2023-01-15 · diff ↗
0.9.5
record
2022-08-09 · diff ↗
0.9.4
record
2022-05-18 · diff ↗
R
R 4.2.0 released · 2022-04-22
0.9.3
record
2022-03-04 · diff ↗ ·
added 2 exports
plot.beast, print.beast
unarchivedReturned to CRAN
2021-12-23

Show 13 earlier eventsHide 13 earlier events

0.9.2
record
2021-12-23 · diff ↗
archivedRemoved from CRAN
2021-12-19
check problems were not corrected in time
0.9.1
record
2021-11-24 · diff ↗ ·
added 1 export
tetris
0.9.0
record
2021-11-15 · diff ↗ ·
added 5 exports
beast.irreg, beast123, geeLandsat, tsextract, minesweeper
R
R 4.1.0 released · 2021-05-18
R
R 4.0.0 released · 2020-04-24
0.2.2
record
2019-11-21 · diff ↗
0.2.1
record
2019-07-26 · diff ↗
unarchivedReturned to CRAN
2019-07-23
0.2
record
2019-07-23 · diff ↗
archivedRemoved from CRAN
2019-07-20
installation errors were not corrected in time
0.1
record
2019-05-17
R
R 3.6.0 released · 2019-04-26

Package metadata

First published: 2019-05-17
Total releases: 17 / 7 yrs
License: GPL (>= 2) OSI
Minimum R: ≥ 2.10.0
Bundled data: 219 KB / 8 files
Download size: 1.4 MB

Appears in task views

Bayesian Inference Analysis of Ecological and Environmental Data Time Series Analysis

Cite

Cite this package

Run in R for the authors' entry:

citation("Rbeast")

The package provides a citation file, so citation() returns the authors' own entry.

It points to 10.1016/j.rse.2019.04.034, 10.1016/j.rse.2012.12.026, 10.1016/j.isprsjprs.2021.04.008.

Cite the R Observatory

For a number measured here: a download total, a coverage figure, an archival date.

Format

APA

Balamuta, J. J. (2026). R Observatory: Metrics for Rbeast version 1.0.2 [Data set]. HJJB, LLC. Data release v2026-10-19. https://doi.org/10.5281/zenodo.21843040

From data release v2026-10-19, which the citation names so these numbers can be found later. More on citing and the projects behind them.

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