Hidden Markov Model by Matrix and Tensor Decomposition R package hmmTensor version 0.1.0 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>. Software Depends: R (>= 3.5.0) Imports: rTensor, symTensor, methods, stats Suggests: testthat Koki Tsuyuzaki <k.t.the-answer@hotmail.co.jp> Comprehensive R Archive Network (CRAN) Koki Tsuyuzaki [aut, cre] MIT + file LICENSE (https://CRAN.R-project.org/package=hmmTensor/LICENSE) 2026-05-27 application/tgz https://CRAN.R-project.org/package=hmmTensor doi:10.32614/CRAN.package.hmmTensor