hmmTensor
0.1.0Hidden Markov Model by Matrix and Tensor Decomposition
Overview
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.
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Health
- OK2026-08-0513 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- NOTE2026-08-0112 OK · 1 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- OK2026-05-287 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
- Examples that run
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- Documented parameters
- 100%
- Return-value docs
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- References docs
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Code & Tests
People & History
1 release. R releases are shown for context.
- 0.1.0Latest2026-05-27 · current release
- RR 4.6.0 released · 2026-04-24
Package metadata
- First published
- 2026-05-27
- Total releases
- 1 / 1 yrs
- License
- MIT + file LICENSE OSI
- Minimum R
- ≥ 3.5.0
- Download size
- 10 KB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet
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