Package: LSX
Type: Package
Title: Semi-Supervised Algorithm for Document Scaling
Version: 1.4.5
Authors@R: person("Kohei", "Watanabe", email = "watanabe.kohei@gmail.com", role = c("aut", "cre", "cph"))
Description: 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.
License: GPL-3
LazyData: TRUE
Encoding: UTF-8
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, irlba,
        rsvd, rsparse
RoxygenNote: 7.3.2
BugReports: https://github.com/koheiw/LSX/issues
URL: https://koheiw.github.io/LSX/
Language: en-US
NeedsCompilation: no
Packaged: 2025-06-19 20:14:51 UTC; watan
Author: Kohei Watanabe [aut, cre, cph]
Maintainer: Kohei Watanabe <watanabe.kohei@gmail.com>
Repository: CRAN
Date/Publication: 2025-06-19 20:30:02 UTC
Built: R 4.3.3; ; 2025-06-19 22:08:10 UTC; unix
