hmmTensor: Hidden Markov Model by Matrix and Tensor Decomposition

Solves Hidden Markov Models (HMMs) via matrix and tensor decomposition. Converts observation sequences to co-occurrence matrices/tensors and applies Symmetric Non-negative Matrix Factorization (symNMF), Singular Value Decomposition (SVD), CANDECOMP/PARAFAC (CP) decomposition, or Tensor-Train (TT) decomposition to recover HMM parameters. Also provides standard HMM algorithms (Forward, Backward, Viterbi, Baum-Welch) for comparison. The spectral learning approach for HMMs is based on Hsu, Kakade, and Zhang (2012) <doi:10.1016/j.jcss.2011.12.025>. The symNMF method is described in Kuang, Yun, and Park (2015) <doi:10.1007/s10898-014-0247-2>. The Tensor-Train decomposition is described in Oseledets (2011) <doi:10.1137/090752286>.

Version:	0.1.0
Depends:	R (≥ 3.5.0)
Imports:	rTensor, symTensor, methods, stats
Suggests:	testthat
Published:	2026-05-27
DOI:	10.32614/CRAN.package.hmmTensor
Author:	Koki Tsuyuzaki [aut, cre]
Maintainer:	Koki Tsuyuzaki <k.t.the-answer at hotmail.co.jp>
License:	MIT + file LICENSE
NeedsCompilation:	no
CRAN checks:	hmmTensor results

Documentation:

Reference manual:

hmmTensor.html , hmmTensor.pdf

Downloads:

Package source:	hmmTensor_0.1.0.tar.gz
Windows binaries:	r-devel: hmmTensor_0.1.0.zip, r-release: hmmTensor_0.1.0.zip, r-oldrel: hmmTensor_0.1.0.zip
macOS binaries:	r-release (arm64): hmmTensor_0.1.0.tgz, r-oldrel (arm64): hmmTensor_0.1.0.tgz, r-release (x86_64): hmmTensor_0.1.0.tgz, r-oldrel (x86_64): hmmTensor_0.1.0.tgz

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