Package: LSX
Type: Package
Title: Model for Semisupervised Text Analysis Based on Word Embeddings
Date: 2020-09-22
Version: 0.9.2
Authors@R: person("Kohei", "Watanabe", email = "watanabe.kohei@gmail.com", role = c("aut", "cre", "cph"))
Description: A word embeddings-based semisupervised models for document scaling Watanabe (2017) <doi:10.1177/0267323117695735>.
    LSS allows users to analyze large and complex corpora on arbitrary dimensions with seed words exploiting efficiency of word embeddings (SVD, Glove).
License: GPL-3
LazyData: TRUE
Encoding: UTF-8
Depends: quanteda (>= 2.0), quanteda.textmodels, methods, R (>= 3.5.0)
Imports: digest, Matrix, RSpectra, irlba, rsvd, rsparse, proxyC,
        grDevices, stats, ggplot2, ggrepel, reshape2, e1071
Suggests: testthat
RoxygenNote: 7.1.1
NeedsCompilation: no
Packaged: 2020-09-22 10:45:13 UTC; kohei
Author: Kohei Watanabe [aut, cre, cph]
Maintainer: Kohei Watanabe <watanabe.kohei@gmail.com>
Repository: CRAN
Date/Publication: 2020-09-22 11:20:03 UTC
