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cff-version: 1.2.0
message: 'To cite package "hmmTensor" in publications use:'
type: software
license: MIT
title: 'hmmTensor: Hidden Markov Model by Matrix and Tensor Decomposition'
version: 0.1.0
doi: 10.32614/CRAN.package.hmmTensor
abstract: 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) <https://doi.org/10.1016/j.jcss.2011.12.025>. The
  symNMF method is described in Kuang, Yun, and Park (2015) <https://doi.org/10.1007/s10898-014-0247-2>.
  The Tensor-Train decomposition is described in Oseledets (2011) <https://doi.org/10.1137/090752286>.
authors:
- family-names: Tsuyuzaki
  given-names: Koki
  email: k.t.the-answer@hotmail.co.jp
repository: https://kokitsuyuzaki.r-universe.dev
commit: be8657d954e97303151fce55019125bf37df7449
date-released: '2026-05-27'
contact:
- family-names: Tsuyuzaki
  given-names: Koki
  email: k.t.the-answer@hotmail.co.jp