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fastTopics: Fast Algorithms for Fitting Topic Models and Non-Negative Matrix Factorizations to Count Data
Implements fast, scalable optimization algorithms for fitting topic models ("grade of membership" models) and non-negative matrix factorizations to count data. The methods exploit the special relationship between the multinomial topic model (also, "probabilistic latent semantic indexing") and Poisson non-negative matrix factorization. The package provides tools to compare, annotate and visualize model fits, including functions to efficiently create "structure plots" and identify key features in topics. The 'fastTopics' package is a successor to the 'CountClust' package. For more information, see <doi:10.48550/arXiv.2105.13440> and <doi:10.1186/s13059-023-03067-9>. Please also see the GitHub repository for additional vignettes not included in the package on CRAN.
Package details |
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| Author | Peter Carbonetto [aut, cre], Kevin Luo [aut], Kushal Dey [aut], Joyce Hsiao [ctb], Abhishek Sarkar [ctb], Anthony Hung [ctb], Xihui Lin [ctb], Paul C. Boutros [ctb], Minzhe Wang [ctb], Tracy Ke [ctb], Eric Weine [ctb], Matthew Stephens [aut] |
| Maintainer | Peter Carbonetto <peter.carbonetto@gmail.com> |
| License | BSD_2_clause + file LICENSE |
| Version | 0.6-192 |
| URL | https://stephenslab.github.io/fastTopics/ https://github.com/stephenslab/fastTopics |
| Package repository | View on CRAN |
| Installation |
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