quanteda.textmodels

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.

Last 30 days

This package has been downloaded 2,722 times in the last 30 days. That's enough downloads to impress a room full of undergrads. A commendable achievement indeed. The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 198 times.

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Last 365 days

This package has been downloaded 23,334 times in the last 365 days. The academic equivalent of having a dedicated subreddit. There are fans, and maybe even a few trolls! The day with the most downloads was May 08, 2024 with 226 downloads.

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