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>.

Package details

AuthorPeter 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]
MaintainerPeter Carbonetto <peter.carbonetto@gmail.com>
LicenseBSD_2_clause + file LICENSE
Version0.6-186
URL https://stephenslab.github.io/fastTopics/ https://github.com/stephenslab/fastTopics
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("fastTopics")

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fastTopics documentation built on June 29, 2024, 9:09 a.m.