tmfast
Fast Topic Models Using Varimax
Fits topic models using varimax-rotated principal component analysis (PCA), following the "vintage factor analysis" approach of Rohe & Zheng (2020) <doi:10.48550/arXiv.2004.05387>. Leverages truncated PCA via 'irlba' for sparse matrices, enabling fast model fitting on large corpora. Includes an information-theoretic approach to vocabulary selection, 'broom'-compatible tidiers for extracting word-topic and topic-document matrices into a tidy data workflow, and samplers for constructing simulated corpora for benchmarking and method evaluation.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.1.1 |
rolling linux/jammy R-4.5 | tmfast_0.1.1.tar.gz |
3.0 MiB |
0.1.1 |
rolling linux/noble R-4.5 | tmfast_0.1.1.tar.gz |
3.0 MiB |
0.1.1 |
rolling source/ R- | tmfast_0.1.1.tar.gz |
2.9 MiB |
0.1.1 |
latest linux/jammy R-4.5 | tmfast_0.1.1.tar.gz |
3.0 MiB |
0.1.1 |
latest linux/noble R-4.5 | tmfast_0.1.1.tar.gz |
3.0 MiB |
0.1.1 |
latest source/ R- | tmfast_0.1.1.tar.gz |
2.9 MiB |
0.1.1 |
2026-04-23 source/ R- | tmfast_0.1.1.tar.gz |
0 B |