Package: LSX
Type: Package
Title: Model for Semisupervised Text Analysis Based on Word Embeddings
Date: 2020-12-07
Version: 0.9.6
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.textmodels, quanteda.textstats,
        stringi, digest, Matrix, RSpectra, irlba, rsvd, rsparse,
        proxyC, grDevices, stats, ggplot2, ggrepel, reshape2, e1071,
        locfit
Suggests: testthat
RoxygenNote: 7.1.1
BugReports: https://github.com/koheiw/LSX/issues
NeedsCompilation: no
Packaged: 2020-12-17 11:16:17 UTC; kohei
Author: Kohei Watanabe [aut, cre, cph]
Maintainer: Kohei Watanabe <watanabe.kohei@gmail.com>
Repository: CRAN
Date/Publication: 2020-12-17 16:30:19 UTC
