Package: LSX
Type: Package
Title: Semisupervised Document Scaling by Word-Embedding Models
Version: 1.1.2
Authors@R: person("Kohei", "Watanabe", email = "watanabe.kohei@gmail.com", role = c("aut", "cre", "cph"))
Description: A word embeddings-based semisupervised 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: 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: testthat
RoxygenNote: 7.2.1
BugReports: https://github.com/koheiw/LSX/issues
NeedsCompilation: no
Packaged: 2022-10-02 02:49:24 UTC; kohei
Author: Kohei Watanabe [aut, cre, cph]
Maintainer: Kohei Watanabe <watanabe.kohei@gmail.com>
Repository: CRAN
Date/Publication: 2022-10-02 16:30:05 UTC
