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hmmTensor  

Hidden Markov Model by Matrix and Tensor Decomposition
View on CRAN: Click here


Download and install hmmTensor package within the R console
Install from CRAN:
install.packages("hmmTensor")

Install from Github:
library("remotes")
install_github("cran/hmmTensor")

Install by package version:
library("remotes")
install_version("hmmTensor", "0.1.0")



Attach the package and use:
library("hmmTensor")
Maintained by
Koki Tsuyuzaki
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-05-27
Latest Update: 2026-05-27
Description:
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>.
How to cite:
Koki Tsuyuzaki (2026). hmmTensor: Hidden Markov Model by Matrix and Tensor Decomposition. R package version 0.1.0, https://cran.r-project.org/web/packages/hmmTensor. Accessed 31 Oct. 2026.
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