FourWayHMM
1.0.0Parsimonious Hidden Markov Models for Four-Way Data
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Overview
About
Implements parsimonious hidden Markov models for four-way data via expectation- conditional maximization algorithm, as described in Tomarchio et al. (2020) arXiv:2107.04330. The matrix-variate normal distribution is used as emission distribution. For each hidden state, parsimony is reached via the eigen-decomposition of the covariance matrices of the emission distribution. This produces a family of 98 parsimonious hidden Markov models.
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Slowest check: 56 s · r-oldrel-windows-x86_64
Check history
- NOTE2026-03-1010 OK · 4 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
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FourWayHMM
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25730 days
78590 days
3.4K1 year
Also on92 r2u8 autocran
Dependencies
Declared dependencies
9 external dependencies (excludes base and recommended)
Depends (1)
R >= 2.10
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none
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none
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Code & Tests
People & History
People (3)
Maintainer (1)
Author, Maintainer
Authors (3)
Package Timeline
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
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Package metadata
- First published
- 2021-11-30
- Total releases
- 1 / 5 yrs
- License
- GPL (>= 3) OSI
- Download size
- 33 KB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet