Estimating Speakers of Texts

Estimates the authors or speakers of texts. Methods developed in Huang, Perry, and Spirling (2020) . 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.

stylest2 version 0.1.0

stylest2 estimates speaker style distinctiveness.

To see how performance of stylest2 compares with its predecessor stylest, see the following package vignette:

Installation

The dev version of stylest2 on GitHub may have additional features (and bugs) and is not guaranteed to be stable. Power users may install it with:

install.packages("devtools")
devtools::install_github("ArthurSpirling/stylest2", build_vignettes=FALSE)
#set build_vignettes to TRUE if vignettes desired