Package: TopicScore 0.0.1

TopicScore: The Topic SCORE Algorithm to Fit Topic Models

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) <arxiv:1704.07016>.

Authors:Minzhe Wang [aut, cre], Tracy Ke [aut]

TopicScore_0.0.1.tar.gz
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TopicScore_0.0.1.tgz(r-4.6-any)TopicScore_0.0.1.tgz(r-4.5-any)
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TopicScore_0.0.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
TopicScore/json (API)

# Install 'TopicScore' in R:
install.packages('TopicScore', repos = c('https://minzhew.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:
  • AP - Associated Press data

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.00 score 321 downloads 5 exports 8 dependencies

Last updated from:17fc161e3f. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK133
source / vignettesOK240
linux-releaseOK120
macos-releaseOK149
macos-oldrelOK201
windows-develOK80
windows-releaseOK78
windows-oldrelOK101
wasm-releaseOK92

Exports:error_Asimplex_disttopic_scorevertices_estW_from_AD

Dependencies:combinatlatticeMatrixquadprogRcppRcppEigenRSpectraslam