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LSX: Semi-Supervised Algorithm for Document Scaling

A word embeddings-based semi-supervised model for document scaling Watanabe (2020) <doi:10.1080/19312458.2020.1832976>. LSS allows users to analyze large and complex corpora on arbitrary dimensions with seed words exploiting efficiency of word embeddings (SVD, Glove). It can generate word vectors on a users-provided corpus or incorporate a pre-trained word vectors.

Version: 1.4.0
Depends: methods, R (≥ 3.5.0)
Imports: quanteda (≥ 2.0), quanteda.textstats, stringi, digest, Matrix, RSpectra, irlba, rsvd, rsparse, proxyC, stats, ggplot2, ggrepel, reshape2, locfit
Suggests: knitr, rmarkdown, testthat
Published: 2024-03-05
DOI: 10.32614/CRAN.package.LSX
Author: Kohei Watanabe [aut, cre, cph]
Maintainer: Kohei Watanabe <watanabe.kohei at gmail.com>
BugReports: https://github.com/koheiw/LSX/issues
License: GPL-3
URL: https://koheiw.github.io/LSX/
NeedsCompilation: no
Materials: NEWS
CRAN checks: LSX results

Documentation:

Reference manual: LSX.pdf

Downloads:

Package source: LSX_1.4.0.tar.gz
Windows binaries: r-devel: LSX_1.4.0.zip, r-release: LSX_1.4.0.zip, r-oldrel: LSX_1.4.0.zip
macOS binaries: r-release (arm64): LSX_1.4.0.tgz, r-oldrel (arm64): LSX_1.4.0.tgz, r-release (x86_64): LSX_1.4.0.tgz, r-oldrel (x86_64): LSX_1.4.0.tgz
Old sources: LSX archive

Linking:

Please use the canonical form https://CRAN.R-project.org/package=LSX to link to this page.

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.
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