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.