MatrixHMM
1.0.0Parsimonious 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.
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- NOTE r-devel-linux-x86_64-debian-gcc
- NOTE2026-08-0112 OK · 1 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- OK2026-07-0413 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- ERROR2026-07-0312 OK · 0 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
- OK2026-03-3114 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- ERROR2026-03-3013 OK · 0 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
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- OK2026-03-2114 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- ERROR2026-03-1013 OK · 0 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
Documentation
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- Documented parameters
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- Return-value docs
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Code & Tests
People & History
1 release. R releases are shown for context.
- RR 4.6.0 released · 2026-04-24
- 1.0.0Latest2026-03-10 · current release
- RR 4.5.0 released · 2025-04-11
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
- Installed size
- not tracked yet
- With dependencies
- not tracked yet