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treeHFM: Hidden Factor Graph Models
Hidden Factor graph models generalise Hidden Markov Models to tree structured data. The distinctive feature of 'treeHFM' is that it learns a transition matrix for first order (sequential) and for second order (splitting) events. It can be applied to all discrete and continuous data that is structured as a binary tree. In the case of continuous observations, 'treeHFM' has Gaussian distributions as emissions.
Getting started
Browse package contents
Package details |
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| Author | Henrik Failmezger, Achim Tresch |
| Bioconductor views | Clustering HiddenMarkovModel |
| Maintainer | Henrik Failmezger <Henrik.Failmezger@googlemail.com> |
| License | GPL (>= 2) |
| Version | 1.0.3 |
| Package repository | View on CRAN |
| Installation |
Install the latest version of this package by entering the following in R:
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