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MatrixHMM

1.0.0

Parsimonious Families of Hidden Markov Models for Matrix-Variate Longitudinal Data

0packages depend
2.5Kdownloads / year
test coverage
13/13checks pass

Overview

About
Maintained by Salvatore D. TomarchioFirst published 2024-08-281 releasesCRAN page ↗

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

CRAN checks
13OK
Slowest check: 2.3 min · r-devel-windows-x86_64
Code health
None
Tests · ratio 0.00
not tracked
Coverage
100%
Documentation · exports
10
Dependencies · direct
Check history
  • OK2026-08-05
    13 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
  • NOTE2026-08-01
    12 OK · 1 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
  • OK2026-07-04
    13 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
  • ERROR2026-07-03
    12 OK · 0 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
  • OK2026-03-31
    14 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Show 3 earlier snapshots
  • ERROR2026-03-30
    13 OK · 0 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
  • OK2026-03-21
    14 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
  • ERROR2026-03-10
    13 OK · 0 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE

Documentation

Documentation
READMENoVignettesNopkgdown siteNoNEWSNoCode of conductNoContributing guideNo
Examples that run
100%
Documented parameters
100%
Return-value docs
100%
References docs
0%

Downloads

2.5K
CRAN downloads in the past year
Rank #19,256 · ~7/day · ~209/mo
Daily download trend is not available in this view yet.
18730 days
56890 days
2.5K1 year
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Also on99 r2u13 autocran

Dependencies

Declared dependencies
10 external dependencies (excludes base and recommended)
Depends (1)
R >= 2.10
LinkingTo (0)
none
Suggests (0)
none
Enhances (0)
none
Reverse dependencies
0direct
0indirect

Nothing depends on this yet.

Code & Tests

People & History

People (1)
Maintainer (1)
Author, Maintainer
Authors (1)
Author, Maintainer
Package Timeline

1 release. R releases are shown for context.

  • R
    R 4.6.0 released · 2026-04-24
  • 1.0.0Latest
    2026-03-10 · current release
  • R
    R 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

Cite

Cite this package

Run in R for the authors' preferred citation:

citation("MatrixHMM")
Tomarchio, S. D. (2024). MatrixHMM: Parsimonious Families of Hidden Markov Models for Matrix-Variate Longitudinal Data (Version 1.0.0) [Computer software]. https://doi.org/10.32614/CRAN.package.MatrixHMM

This is what citation() produces when a package has no citation file of its own. If it prints something else, use that.

Cite the R Observatory

For a number measured here: a download total, a coverage figure, an archival date.

APA

Balamuta, J. J. (2026). R Observatory: Metrics for MatrixHMM version 1.0.0 [Data set]. HJJB, LLC. Data release v2026-08-18. https://doi.org/10.5281/zenodo.21843040

From data release v2026-08-18, which the citation names so these numbers can be found later. More on citing and the projects behind them.

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