L2 Inference for Change Points in High-Dimensional Time Series
Overview
Provides a method for detecting multiple change points in high-dimensional time series, targeting dense or spatially clustered signals. See Li et al. (2023) "L2 Inference for Change Points in High-Dimensional Time Series via a Two-Way MOSUM". arXiv preprint arXiv:2208.13074.
Install
Health
NOTECRAN incoming feasibilityr-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc
Maintainer: ‘Rui Lin <ruilin1081@gmail.com>’ The Description field contains <arXiv:2208.13074>. Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.
- NOTE11 OK, 2 NOTENow
- NOTE12 OK, 2 NOTE2026-03-10
Documentation
- Help pages
- 32
- Examples that run
- 94%15 run in a plain check · 1 only when donttest examples are enabled (R CMD check --as-cran)
- Exported functions with examples
- 100%
- Documented parameters
- 95%
- Return-value docs
- 100%
- Help pages citing references
- 66%
Downloads
Dependencies
Installing L2hdchange adds no packages beyond those that come with R.
Code & Tests
Datasets
People & History
- RR 4.6.0 released · 2026-04-24
- RR 4.5.0 released · 2025-04-11
- RR 4.4.0 released · 2024-04-24
- 1.0Latest2023-07-20 · current release
- RR 4.3.0 released · 2023-04-21
Package metadata
- First published
- 2023-07-20
- Total releases
- 1 / 3 yrs
- License
- GPL (>= 3) OSI
- Minimum R
- ≥ 2.10
- Bundled data
- 271 KB / 2 files
- Download size
- 290 KB
Cite
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