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TopicScore  

The Topic SCORE Algorithm to Fit Topic Models
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("TopicScore", "0.0.1")



Attach the package and use:
library("TopicScore")
Maintained by
Minzhe Wang
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2019-06-06
Latest Update: 2019-06-06
Description:
Provides implementation of the "Topic SCORE" algorithm that is proposed by Tracy Ke and Minzhe Wang. The singular value decomposition step is optimized through the usage of svds() function in 'RSpectra' package, on a 'dgRMatrix' sparse matrix. Also provides a column-wise error measure in the word-topic matrix A, and an algorithm for recovering the topic-document matrix W given A and D based on quadratic programming. The details about the techniques are explained in the paper "A new SVD approach to optimal topic estimation" by Tracy Ke and Minzhe Wang (2017) <doi:10.48550/arXiv.1704.07016>.
How to cite:
Minzhe Wang (2019). TopicScore: The Topic SCORE Algorithm to Fit Topic Models. R package version 0.0.1, https://cran.r-project.org/web/packages/TopicScore. Accessed 26 Jun. 2026.
Previous versions and publish date:
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Complete documentation for TopicScore
Functions, R codes and Examples using the TopicScore R package
Some associated functions: AP . W_from_AD . error_A . simplex_dist . topic_score . vertices_est . 
Some associated R codes: W_from_AD.R . data.R . error_A.R . topic_score.R .  Full TopicScore package functions and examples
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