Sequential Change Point Detection for High-Dimensional VAR Models
Overview
Implements the algorithm introduced in Tian, Y., and Safikhani, A. (2024) doi:10.5705/ss.202024.0182, "Sequential Change Point Detection in High-dimensional Vector Auto-regressive Models". This package provides tools for detecting change points in the transition matrices of VAR models, effectively identifying shifts in temporal and cross-correlations within high-dimensional time series data.
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Install
Health
- OK13 OK2026-09-08
- ERROR12 OK, 1 ERROR2026-09-07
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK14 OK2026-03-10
Documentation
- Help pages
- 13 (8 internal)
- Examples that run
- 75%3 run in a plain check · 1 only when donttest examples are enabled (R CMD check --as-cran)
- Exported functions with examples
- 75%
- Documented parameters
- 100%
- Return-value docs
- 100%
- Help pages citing references
- 8%
Downloads
Repository
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Issues over time
1 development-tooling and community-health practice detected across 1 family in the upstream repository
Checks run against github.com/helloworld9293/varcpdetectonline on 2026-10-21.
Dependencies
Installing VARcpDetectOnline adds 8 packages (3 with compiled code, 7.4 MB of source tarballs). Heaviest: glmnet 2.7 MB, Rcpp 1.8 MB, RcppEigen 1.7 MB.
Code & Tests
Datasets
People & History
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- 0.2.1Latest
- RR 4.6.0 released · 2026-04-24
- RR 4.5.0 released · 2025-04-11
- unarchivedReturned to CRAN2025-02-13
- 0.2.0
- archivedRemoved from CRAN2025-02-09requires archived package 'sparsevar'
- 0.1.02025-01-09
- RR 4.4.0 released · 2024-04-24
Package metadata
- First published
- 2025-01-09
- Total releases
- 3 / 1 yrs
- License
- GPL-2 | file LICENSE OSI · relicensed from MIT + file LICENSE in 0.2.0
- Minimum R
- ≥ 3.5.0
- Bundled data
- 4.1 MB / 1 file
- Download size
- 4.1 MB
Cite
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