quanteda.textmodels: 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) <doi:10.1017/S0003055403000698>, 'Wordscores' model, Perry and 'Benoit's' (2017) <arXiv:1710.08963> class affinity scaling model, and 'Slapin' and 'Proksch's' (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.

Version: 0.9.1
Depends: R (≥ 3.1.0), methods
Imports: ggplot2, LiblineaR, Matrix (≥ 1.2), quanteda (≥ 2.0), RSpectra, Rcpp (≥ 0.12.12), RcppParallel, RSSL, SparseM, stringi
LinkingTo: Rcpp, RcppParallel, RcppArmadillo (≥ 0.7.600.1.0), quanteda
Suggests: ca, covr, fastNaiveBayes, knitr, lsa, microbenchmark, naivebayes, spelling, RColorBrewer, testthat, rmarkdown
Published: 2020-03-13
Author: Kenneth Benoit ORCID iD [cre, aut, cph], Kohei Watanabe ORCID iD [aut], Haiyan Wang ORCID iD [aut], Stefan Müller ORCID iD [aut], Patrick O. Perry ORCID iD [aut], Benjamin Lauderdale ORCID iD [aut], William Lowe ORCID iD [aut], European Research Council [fnd] (ERC-2011-StG 283794-QUANTESS)
Maintainer: Kenneth Benoit <kbenoit at lse.ac.uk>
License: GPL-3
URL: https://github.com/quanteda/quanteda.textmodels
NeedsCompilation: yes
SystemRequirements: C++11
Language: en-GB
Materials: README NEWS
CRAN checks: quanteda.textmodels results


Reference manual: quanteda.textmodels.pdf
Vignettes: textmodel Performance Comparisons
Package source: quanteda.textmodels_0.9.1.tar.gz
Windows binaries: r-devel: quanteda.textmodels_0.9.1.zip, r-release: quanteda.textmodels_0.9.1.zip, r-oldrel: quanteda.textmodels_0.9.1.zip
macOS binaries: r-release: quanteda.textmodels_0.9.1.tgz, r-oldrel: quanteda.textmodels_0.9.1.tgz
Old sources: quanteda.textmodels archive

Reverse dependencies:

Reverse depends: LSX
Reverse suggests: explor, quanteda, rainette, seededlda


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