Variable Length Markov Chain with Exogenous Covariates
Overview
Models categorical time series through a Markov Chain when a) covariates are predictors for transitioning into the next state/symbol and b) when the dependence in the past states has variable length. The probability of transitioning to the next state in the Markov Chain is defined by a multinomial regression whose parameters depend on the past states of the chain and, moreover, the number of states in the past needed to predict the next state also depends on the observed states themselves. See Zambom, Kim, and Garcia (2022) doi:10.1111/jtsa.12615.
Install
Health
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK14 OK2026-03-10
Documentation
- Help pages
- 11
- Examples that run
- 0%11 only when donttest examples are enabled (R CMD check --as-cran)
- Exported functions with examples
- 100%
- Documented parameters
- 100%
- Return-value docs
- 100%
- Help pages citing references
- 9%
Downloads
Dependencies
Installing VLMCX adds 2 packages (0 with compiled code, 1.2 MB of source tarballs). Heaviest: berryFunctions 1.2 MB, abind 21 KB.
Code & Tests
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
- 1.0Latest2024-02-08 · current release
- RR 4.3.0 released · 2023-04-21
Package metadata
- First published
- 2024-02-08
- Total releases
- 1 / 2 yrs
- License
- GPL (>= 2) OSI
- Download size
- 17 KB
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