Constructing Dynamic Networks for Time-Series Data
Overview
Implements the method in Behrouzi, Abegaz, and Wit (2018) arXiv:1805.09840 to learn intra-time and inter-time conditional independence networks for ordinal time-series data and mixed discrete-and-continuous time-series data. This package assumes a stationary process, where both instantaneous and dynamic networks stay fixed over time.
Install
Health
CRAN check results are not tracked yet.
Documentation
- Help pages
- 6 (1 internal)includes a package overview page
- Examples that run
- 100%3 run in a plain check
- Exported functions with examples
- 67%
- Documented parameters
- 100%
- Return-value docs
- 67%
- Help pages citing references
- 50%
Downloads
Dependencies
Code & Tests
Datasets
People & History
- RR 4.1.0 released · 2021-05-18
- archivedRemoved from CRAN2020-08-03required archived package 'QUIC'
- RR 4.0.0 released · 2020-04-24
- 1.22020-02-29
- RR 3.6.0 released · 2019-04-26
Package metadata
- First published
- 2020-02-29
- Total releases
- 1 / 6 yrs
- License
- GPL (>= 3) OSI
- Minimum R
- ≥ 3.3.2
- Bundled data
- 1.6 KB / 1 file
Cite
Cite this package
Run in R for the authors' preferred citation:
citation("tsnetwork")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.
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.