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, 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.
- Version0.9.9
- R version≥ 3.1.0 methods
- LicenseGPL-3
- Needs compilation?Yes
- Languageen-GB
- Last release09/03/2024
Documentation
Team
Kenneth Benoit
Haiyan Wang
Show author detailsRolesAuthorKohei Watanabe
Show author detailsRolesAuthorJohannes Gruber
Show author detailsRolesAuthorPatrick O. Perry
Show author detailsRolesAuthorEuropean Research Council
Show author detailsRolesfndWilliam Lowe
Show author detailsRolesAuthorBenjamin Lauderdale
Vikas Sindhwani
Show author detailsRolesCopyright holder
Insights
Last 30 days
Last 365 days
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
Data provided by CRAN
Binaries
Dependencies
- Imports8 packages
- Suggests11 packages
- Linking To3 packages
- Reverse Suggests5 packages