Gaussian Mixture Topic Models
Overview
Gaussian mixture models (GMM) and k-means for topic analysis of dense document vectors. The underlying clustering functions rely on the Armadillo library (Sanderson & Curtin, 2017) doi:10.1109/ICSPCS.2017.8270510.
- Add
probability()to extract topic likelihood of documents. - Add
seedstotextmodel_kmeans()andtextmodel_gmm()to initialize clustering with user-provided centers. - Add
as.seedwords()to create a seed words matrix from **quanteda** dictionaries.
From NEWS.md in the release
Install
Health
- OK13 OKNow
- OK7 OK2026-09-16
Documentation
- Help pages
- 8 (2 internal)
- Examples that run
- 100%3 run in a plain check
- Exported functions with examples
- 43%
- Documented parameters
- 100%
- Return-value docs
- 71%
- Help pages citing references
- 0%
Downloads
Dependencies
Installing GMTM adds 21 packages (17 with compiled code, 31 MB of source tarballs). Heaviest: stringi 11 MB, quanteda 4.2 MB, wordvector 4.2 MB.
Code & Tests
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- 0.2.0Latest
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Package metadata
- First published
- 2026-09-15
- Total releases
- 2 / 1 yrs
- License
- Apache License (>= 2.0)
- Minimum R
- ≥ 3.5.0
- Download size
- 18 KB
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