quanteda.textmodels (0.9.1)

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Scaling Models and Classifiers for Textual Data.


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) , 'Wordscores' model, Perry and 'Benoit's' (2017) class affinity scaling model, and 'Slapin' and 'Proksch's' (2008) '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.

Maintainer: Kenneth Benoit
Author(s): Kenneth Benoit [cre, aut, cph] (<https://orcid.org/0000-0002-0797-564X>), Kohei Watanabe [aut] (<https://orcid.org/0000-0001-6519-5265>), Haiyan Wang [aut] (<https://orcid.org/0000-0003-4992-4311>), Stefan Mller [aut] (<https://orcid.org/0000-0002-6315-4125>), Patrick O. Perry [aut] (<https://orcid.org/0000-0001-7460-127X>), Benjamin Lauderdale [aut] (<https://orcid.org/0000-0003-3090-0969>), William Lowe [aut] (<https://orcid.org/0000-0002-1549-6163>), European Research Council [fnd] (ERC-2011-StG 283794-QUANTESS)

License: GPL-3

Uses: ggplot2, LiblineaR, Matrix, quanteda, Rcpp, RcppParallel, RSpectra, RSSL, SparseM, stringi, RColorBrewer, ca, lsa, testthat, microbenchmark, knitr, rmarkdown, covr, naivebayes, spelling, fastNaiveBayes
Reverse suggests: explor, quanteda, rainette

Released 3 months ago.

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