stylest2

Estimates the authors or speakers of texts. Methods developed in Huang, Perry, and Spirling (2020) doi:10.1017/pan.2019.49. The model is built on a Bayesian framework in which the distinctiveness of each speaker is defined by how different, on average, the speaker's terms are to everyone else in the corpus of texts. An optional cross-validation method is implemented to select the subset of terms that generate the most accurate speaker predictions. Once a set of terms is selected, the model can be estimated. Speaker distinctiveness and term influence can be recovered from parameters in the model using package functions. Once fitted, the model can be used to predict authorship of new texts.

Last 30 days

This package has been downloaded 99 times in the last 30 days. Not quite viral, but there's some loyal interest. Consider it a cult classic. The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 1 times.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.

Last 365 days

This package has been downloaded 1,376 times in the last 365 days. That's enough downloads to impress a room full of undergrads. A commendable achievement indeed. The day with the most downloads was Mar 25, 2024 with 29 downloads.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.