Semi-Supervised Algorithm for Document Scaling R package LSX version 1.5.2 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. Software Depends: R (>= 3.5.0) Imports: methods, quanteda (>= 2.0), quanteda.textstats, stringi, digest, Matrix, RSpectra, proxyC, stats, ggplot2, ggrepel, reshape2, locfit Suggests: testthat, spelling, knitr, rmarkdown, wordvector (>= 0.5.0), irlba, rsvd, rsparse Kohei Watanabe <watanabe.kohei@gmail.com> Comprehensive R Archive Network (CRAN) Kohei Watanabe [aut, cre, cph] GPL-3 2026-04-06 application/tgz https://CRAN.R-project.org/package=LSX doi:10.32614/CRAN.package.LSX en-US