Robust Online Bayesian Monitoring
Overview
An implementation of Bayesian online changepoint detection (Adams and MacKay (2007) doi:10.48550/arXiv.0710.3742) with an option for probability based outlier detection and removal (Wendelberger et. al. (2021) doi:10.48550/arXiv.2112.12899). Building on the independent multivariate constant mean model implemented in the 'R' package 'ocp', this package models multivariate data as multivariate normal about a linear trend, defined by user input covariates, with an unstructured error covariance. Changepoints are identified based on a probability threshold for windows of points.
Changes:
Modified references in DESCRIPTION
From NEWS in the release
Install
Health
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK12 OK2026-04-25
- NOTE12 OK, 2 NOTE2026-04-22
- ERROR11 OK, 2 NOTE, 1 ERROR2026-04-18
Show 1 earlier changeHide 1 earlier change
- NOTE12 OK, 2 NOTE2026-03-10
Documentation
- Help pages
- 1
- Examples that run
- 100%1 run in a plain check
- Exported functions with examples
- 100%
- Documented parameters
- 88%
- Return-value docs
- 100%
- Help pages citing references
- 0%
Downloads
Dependencies
Installing roboBayes adds 3 packages (3 with compiled code, 2.9 MB of source tarballs). Heaviest: Rcpp 1.8 MB, RcppArmadillo 1.0 MB, RcppDist 140 KB.
Code & Tests
People & History
Pick two releases to compare their code metrics.
- RR 4.6.0 released · 2026-04-24
- 1.3Latest
- RR 4.5.0 released · 2025-04-11
- RR 4.4.0 released · 2024-04-24
- 1.22023-12-13 · diff ↗
- RR 4.3.0 released · 2023-04-21
- 1.12022-08-16
- RR 4.2.0 released · 2022-04-22
Package metadata
- First published
- 2022-08-16
- Total releases
- 3 / 4 yrs
- License
- GPL-2 OSI
- Minimum R
- ≥ 3.5.0
- Download size
- 27 KB
Cite
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