treeHFM: Hidden Factor Graph Models
Version 1.0.3

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

Package details

AuthorHenrik Failmezger, Achim Tresch
Bioconductor views Clustering HiddenMarkovModel
Date of publication2016-09-19 01:45:39
MaintainerHenrik Failmezger <>
LicenseGPL (>= 2)
Package repositoryView on CRAN
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treeHFM documentation built on May 30, 2017, 3:01 a.m.