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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>.
| Version: | 0.0.1 |
| Depends: | R (≥ 3.5.0) |
| Imports: | utils, stats, graphics, RSpectra, combinat, quadprog, methods, Matrix, slam |
| Published: | 2019-06-06 |
| DOI: | 10.32614/CRAN.package.TopicScore |
| Author: | Minzhe Wang [aut, cre], Tracy Ke [aut] |
| Maintainer: | Minzhe Wang <minzhew at uchicago.edu> |
| License: | MIT + file LICENSE |
| NeedsCompilation: | no |
| CRAN checks: | TopicScore results |
| Reference manual: | TopicScore.pdf |
| Package source: | TopicScore_0.0.1.tar.gz |
| Windows binaries: | r-devel: TopicScore_0.0.1.zip, r-release: TopicScore_0.0.1.zip, r-oldrel: TopicScore_0.0.1.zip |
| macOS binaries: | r-release (arm64): TopicScore_0.0.1.tgz, r-oldrel (arm64): TopicScore_0.0.1.tgz, r-release (x86_64): TopicScore_0.0.1.tgz, r-oldrel (x86_64): TopicScore_0.0.1.tgz |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.