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fastTopics  

Fast Algorithms for Fitting Topic Models and Non-Negative Matrix Factorizations to Count Data
View on CRAN: Click here


Download and install fastTopics package within the R console
Install from CRAN:
install.packages("fastTopics")

Install from Github:
library("remotes")
install_github("cran/fastTopics")

Install by package version:
library("remotes")
install_version("fastTopics", "0.6-192")



Attach the package and use:
library("fastTopics")
Maintained by
Peter Carbonetto
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2022-05-27
Latest Update: 2024-01-15
Description:
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. Note that the 'fastTopicis' package on GitHub has more vignettes illustrating application to single-cell RNA-seq data.
How to cite:
Peter Carbonetto (2022). fastTopics: Fast Algorithms for Fitting Topic Models and Non-Negative Matrix Factorizations to Count Data. R package version 0.6-192, https://cran.r-project.org/web/packages/fastTopics. Accessed 18 Feb. 2025.
Previous versions and publish date:
0.6-135 (2022-05-27 09:30), 0.6-142 (2022-09-06 17:00), 0.6-163 (2024-01-16 00:30), 0.6-186 (2024-06-29 08:50)
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