Austin helps you see what people, usually politicians, do with words. Currently that means how positions on a presumed underlying policy scale are taken by manipulating word occurrence counts. The models implemented here try to try to recover those positions using only this information, plus some heroic assumptions about language generation, e.g. unidimensionality, conditional independence of words given ideal point and Poisson-distributed word counts.
The package currently implements Wordfish (Slapin and Proksch, 2008) and Wordscores (Laver, Benoit and Garry, 2003). See references for details.
J. Slapin and S.-O. Proksch (2008) 'A scaling model for estimating time-series party positions from texts' American Journal of Political Science 52(3), 705-722.
Laver, Benoit and Garry (2003) `Estimating policy positions from political text using words as data' American Political Science Review 97(2).