Package: sts
Type: Package
Title: Estimation of the Structural Topic and Sentiment-Discourse Model
        for Text Analysis
Version: 1.4
Date: 2025-01-25
Author: Shawn Mankad [aut, cre], Li Chen [aut]
Authors@R: c(person(given = "Shawn",
                      family = "Mankad",
                      role = c("aut", "cre"),
                      email = "smankad@ncsu.edu"),
               person(given = "Li",
                      family = "Chen",
                      role = "aut",
                      email="li.chen@cornell.edu"))
Maintainer: Shawn Mankad <smankad@ncsu.edu>
Description: The Structural Topic and Sentiment-Discourse (STS) model
        allows researchers to estimate topic models with document-level
        metadata that determines both topic prevalence and
        sentiment-discourse. The sentiment-discourse is modeled as a
        document-level latent variable for each topic that modulates
        the word frequency within a topic. These latent topic
        sentiment-discourse variables are controlled by the
        document-level metadata. The STS model can be useful for
        regression analysis with text data in addition to topic
        modeling’s traditional use of descriptive analysis. The method
        was developed in Chen and Mankad (2024)
        <doi:10.1287/mnsc.2022.00261>.
License: MIT + file LICENSE
Imports: glmnet, matrixStats, slam, foreach, doParallel, parallel, stm,
        Matrix, mvtnorm, ggplot2, tm
Suggests: knitr, rmarkdown
LinkingTo: Rcpp, RcppArmadillo
VignetteBuilder: knitr
Encoding: UTF-8
RoxygenNote: 7.2.1
NeedsCompilation: yes
Packaged: 2026-07-04 18:00:01 UTC; root
Repository: https://cran.r-universe.dev
Date/Publication: 2025-01-25 23:00:02 UTC
RemoteUrl: https://github.com/cran/sts
RemoteRef: HEAD
RemoteSha: 67c0b3f6f5e4e8774706a2c839728dd5977ae93f