oHMMed: HMMs with Ordered Hidden States and Emission Densities
Inference using a class of Hidden Markov models
(HMMs) called 'oHMMed'(ordered HMM with emission densities
<doi:10.1186/s12859-024-05751-4>): The 'oHMMed' algorithms identify
the number of comparably homogeneous regions within observed sequences
with autocorrelation patterns. These are modelled as discrete hidden
states; the observed data points are then realisations of continuous
probability distributions with state-specific means that enable
ordering of these distributions. The observed sequence is labelled
according to the hidden states, permitting only neighbouring states
that are also neighbours within the ordering of their associated
distributions. The parameters that characterise these state-specific
distributions are then inferred. Relevant for application to genomic
sequences, time series, or any other sequence data with serial
autocorrelation.
| Version: |
1.0.2 |
| Depends: |
R (≥ 3.5.0) |
| Imports: |
cvms, ggmcmc, ggplot2, gridExtra, mistr, scales, stats, vcd |
| Published: |
2024-04-19 |
| Author: |
Michal Majka 
[aut, cre],
Lynette Caitlin Mikula
[aut],
Claus Vogl [aut] |
| Maintainer: |
Michal Majka <michalmajka at hotmail.com> |
| BugReports: |
https://github.com/LynetteCaitlin/oHMMed/issues |
| License: |
GPL-3 |
| URL: |
https://github.com/LynetteCaitlin/oHMMed,
https://lynettecaitlin.github.io/oHMMed/ |
| NeedsCompilation: |
no |
| CRAN checks: |
oHMMed results |
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