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.

Version: 1.0.3
Depends: mclust
Published: 2016-09-19
Author: Henrik Failmezger, Achim Tresch
Maintainer: Henrik Failmezger <Henrik.Failmezger at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
CRAN checks: treeHFM results


Reference manual: treeHFM.pdf
Package source: treeHFM_1.0.3.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X El Capitan binaries: r-release: treeHFM_1.0.3.tgz
OS X Mavericks binaries: r-oldrel: treeHFM_1.0.3.tgz
Old sources: treeHFM archive


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