Parsimonious Families of Hidden Markov Models for Matrix-Variate Longitudinal Data
Overview
Implements three families of parsimonious hidden Markov models (HMMs) for matrix-variate longitudinal data using the Expectation-Conditional Maximization (ECM) algorithm. The package supports matrix-variate normal, t, and contaminated normal distributions as emission distributions. For each hidden state, parsimony is achieved through the eigen-decomposition of the covariance matrices associated with the emission distribution. This approach results in a comprehensive set of 98 parsimonious HMMs for each type of emission distribution. Atypical matrix detection is also supported, utilizing the fitted (heavy-tailed) models.
Install
Health
- OK13 OK2026-10-20
- ERROR12 OK, 1 ERROR2026-10-17
- OK13 OK2026-09-08
- ERROR12 OK, 1 ERROR2026-09-07
- OK13 OK2026-08-05
Show 7 earlier changesHide 7 earlier changes
- NOTE12 OK, 1 NOTE2026-08-01
- OK13 OK2026-07-04
- ERROR12 OK, 1 ERROR2026-07-03
- OK14 OK2026-03-31
- ERROR13 OK, 1 ERROR2026-03-30
- OK14 OK2026-03-21
- ERROR13 OK, 1 ERROR2026-03-10
Documentation
- Help pages
- 8
- Examples that run
- 100%6 run in a plain check
- Exported functions with examples
- 100%
- Documented parameters
- 100%
- Return-value docs
- 100%
- Help pages citing references
- 0%
Downloads
Dependencies
Installing MatrixHMM adds 32 packages (14 with compiled code, 29 MB of source tarballs). Heaviest: stringi 11 MB, data.table 5.7 MB, mclust 2.7 MB.
Code & Tests
Datasets
People & History
- RR 4.6.0 released · 2026-04-24
- RR 4.5.0 released · 2025-04-11
- 1.0.0Latest2024-08-28 · current release
- RR 4.4.0 released · 2024-04-24
Package metadata
- First published
- 2024-08-28
- Total releases
- 1 / 2 yrs
- License
- GPL (>= 3) OSI
- Minimum R
- ≥ 2.10
- Bundled data
- 15 KB / 2 files
- Download size
- 32 KB
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