R Observatory: Metrics for bayespm version 0.2.0

Balamuta, James Joseph

doi:10.5281/zenodo.21843040

bayespm

0.2.0Package record

Bayesian Statistical Process Monitoring

0packages depend

2.7Kdownloads / year

–test coverage

13/13flavors OK

Overview

About

Maintained by Dimitrios KiagiasCRAN page ↗

The R-package bayespm implements Bayesian Statistical Process Control and Monitoring (SPC/M) methodology. These methods utilize available prior information and/or historical data, providing efficient online quality monitoring of a process, in terms of identifying moderate/large transient shifts (i.e., outliers) or persistent shifts of medium/small size in the process. These self-starting, sequentially updated tools can also run under complete absence of any prior information. The Predictive Control Charts (PCC) are introduced for the quality monitoring of data from any discrete or continuous distribution that is a member of the regular exponential family. The Predictive Ratio CUSUMs (PRC) are introduced for the Binomial, Poisson and Normal data (a later version of the library will cover all the remaining distributions from the regular exponential family). The PCC targets transient process shifts of typically large size (a.k.a. outliers), while PRC is focused in detecting persistent (structural) shifts that might be of medium or even small size. Apart from monitoring, both PCC and PRC provide the sequentially updated posterior inference for the monitored parameter. Bourazas K., Kiagias D. and Tsiamyrtzis P. (2022) "Predictive Control Charts (PCC): A Bayesian approach in online monitoring of short runs" doi:10.1080/00224065.2021.1916413, Bourazas K., Sobas F. and Tsiamyrtzis, P. 2023. "Predictive ratio CUSUM (PRC): A Bayesian approach in online change point detection of short runs" doi:10.1080/00224065.2022.2161434, Bourazas K., Sobas F. and Tsiamyrtzis, P. 2023. "Design and properties of the predictive ratio cusum (PRC) control charts" doi:10.1080/00224065.2022.2161435.

Install

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

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

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

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-bayespm

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-bayespm

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-bayespm

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

CRAN check page ↗

Slowest check: 2.8 min · r-oldrel-windows-x86_64

Status changes

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 0.2.0

READMENoVignettesNoNEWSNoCitation fileNoWebsitenot declared in DESCRIPTION

Help pages: 28
Examples that run: 100%28 run in a plain check
Exported functions with examples: 100%
Documented parameters: not tracked
Return-value docs: not tracked
Help pages citing references: 4%

How these are measured

Downloads

2.7K

CRAN downloads in the past year

Rank #17,199 · ~7/day · ~221/mo

Releases R versions

Daily download trend is not available in this view yet.

25230 days

74590 days

2.7K1 year

Compare downloads with other packages →

Also on97 r2u21 autocran

How downloads are counted

Dependencies

Declared dependencies

5 external dependencies (excludes base and recommended)

Installing bayespm adds 23 packages (11 with compiled code, 20 MB of source tarballs). Heaviest: ggplot2 6.0 MB, invgamma 2.5 MB, Rcpp 1.8 MB.

Depends (1)

R >= 3.5.0

Imports (8)

grDevicesstatsggplot2gridgridExtra extraDistr rmutil invgamma

LinkingTo (0)

none

Suggests (0)

none

Enhances (0)

none

Dependency license breakdown →

Reverse dependencies

0direct

0indirect

Nothing depends on this yet.

How these are counted

Code & Tests

Datasets

People & History

People (3)

Dimitrios Kiagias

Konstantinos Bourazas

Author, Copyright holder

Panagiotis Tsiamyrtzis

Author, Copyright holder

Releases (2)

Pick two releases to compare their code metrics.

R
R 4.6.0 released · 2026-04-24
R
R 4.5.0 released · 2025-04-11
R
R 4.4.0 released · 2024-04-24
0.2.0Latest
record
2023-09-11 · current release · diff ↗
0.1.0
record
2023-07-05
R
R 4.3.0 released · 2023-04-21

Package metadata

First published: 2023-07-05
Total releases: 2 / 3 yrs
License: GPL (>= 2) OSI
Minimum R: ≥ 3.5.0
Bundled data: 0.5 KB / 2 files
Download size: 48 KB

Cite

Cite this package

Run in R for the authors' preferred citation:

citation("bayespm")

Format

Kiagias, D., Bourazas, K., & Tsiamyrtzis, P. (2023). bayespm: Bayesian Statistical Process Monitoring (Version 0.2.0) [Computer software]. https://doi.org/10.32614/CRAN.package.bayespm

This package has no citation file, so citation() returns this entry built from DESCRIPTION.

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 bayespm version 0.2.0 [Data set]. HJJB, LLC. Data release v2026-10-25. https://doi.org/10.5281/zenodo.21843040

From data release v2026-10-25, which the citation names so these numbers can be found later. More on citing and the projects behind them.

Report a problem with this page →