_            _    _        _         _
      /\ \         /\ \ /\ \     /\_\      / /\
      \_\ \       /  \ \\ \ \   / / /     / /  \
      /\__ \     / /\ \ \\ \ \_/ / /     / / /\ \__
     / /_ \ \   / / /\ \ \\ \___/ /     / / /\ \___\
    / / /\ \ \ / / /  \ \_\\ \ \_/      \ \ \ \/___/
   / / /  \/_// / /   / / / \ \ \        \ \ \
  / / /      / / /   / / /   \ \ \   _    \ \ \
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/_/ /      / / /____\/ /       \ \_\\ \/___/ /
\_\/       \/_________/         \/_/ \_____\/
r-quanteda-textmodels 0.9.10
Propagated dependencies: r-stringi@1.8.7 r-rspectra@0.16-2 r-rcpparmadillo@14.4.3-1 r-rcpp@1.0.14 r-quanteda@4.3.0 r-matrix@1.7-3 r-glmnet@4.1-8
Channel: guix-cran
Location: guix-cran/packages/q.scm (guix-cran packages q)
Home page: https://github.com/quanteda/quanteda.textmodels
Licenses: GPL 3
Synopsis: Scaling Models and Classifiers for Textual Data
Description:

Scaling models and classifiers for sparse matrix objects representing textual data in the form of a document-feature matrix. Includes original implementations of Laver', Benoit', and Garry's (2003) <doi:10.1017/S0003055403000698>, Wordscores model, the Perry and Benoit (2017) <doi:10.48550/arXiv.1710.08963> class affinity scaling model, and the Slapin and Proksch (2008) <doi:10.1111/j.1540-5907.2008.00338.x> wordfish model, as well as methods for correspondence analysis, latent semantic analysis, and fast Naive Bayes and linear SVMs specially designed for sparse textual data.

Total results: 1