Parsimonious Hidden Markov Models for Four-Way Data
Overview
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.
Install
Health
NOTECRAN incoming feasibilityr-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc
Maintainer: ‘Salvatore D. Tomarchio <daniele.tomarchio@unict.it>’ The Description field contains Tomarchio et al. (2020) <arXiv:2107.04330>. The matrix-variate normal Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.
NOTEdependencies in R coder-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc
Namespace in Imports field not imported from: ‘LaplacesDemon’ All declared Imports should be used.
- NOTE9 OK, 4 NOTENow
- NOTE10 OK, 4 NOTE2026-03-10
Documentation
- Help pages
- 3
- Examples that run
- 100%2 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 FourWayHMM adds 28 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
- RR 4.4.0 released · 2024-04-24
- RR 4.3.0 released · 2023-04-21
- RR 4.2.0 released · 2022-04-22
- 1.0.0Latest2021-11-30 · current release
- RR 4.1.0 released · 2021-05-18
Package metadata
- First published
- 2021-11-30
- Total releases
- 1 / 5 yrs
- License
- GPL (>= 3) OSI
- Minimum R
- ≥ 2.10
- Bundled data
- 23 KB / 1 file
- Download size
- 33 KB
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