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 
[cre, aut, cph],
Kohei Watanabe
[aut],
Haiyan Wang [aut],
Stefan Müller
[aut],
Patrick O. Perry
[aut],
Benjamin Lauderdale
[aut],
William Lowe
[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 |