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) tools:::Rd_expr_doi("10.1017/S0003055403000698"), 'Wordscores' model, the Perry and 'Benoit' (2017) tools:::Rd_expr_doi("10.48550/arXiv.1710.08963") class affinity scaling model, and the 'Slapin' and 'Proksch' (2008) tools:::Rd_expr_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.
Maintainer: Kenneth Benoit kbenoit@lse.ac.uk (ORCID) [copyright holder]
Authors:
Kohei Watanabe watanabe.kohei@gmail.com (ORCID)
Haiyan Wang whyinsa@yahoo.com (ORCID)
Patrick O. Perry patperry@gmail.com (ORCID)
Benjamin Lauderdale b.e.lauderdale@lse.ac.uk (ORCID)
Johannes Gruber JohannesB.Gruber@gmail.com (ORCID)
William Lowe lowe@hertie-school.org (ORCID)
Other contributors:
Vikas Sindhwani vikas.sindhwani@gmail.com (authored svmlin C++ source code) [copyright holder]
European Research Council (ERC-2011-StG 283794-QUANTESS) [funder]
Useful links: