Package: sts
Type: Package
Title: Estimation of the Structural Topic and Sentiment-Discourse Model
        for Text Analysis
Version: 1.6
Date: 2026-10-23
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"),
               person(given = c("Margaret", "E."),
                      family = "Roberts",
                      role = c("ctb", "cph"),
                      comment = "Author of adapted code from the stm package"),
               person(given = c("Brandon", "M."),
                      family = "Stewart",
                      role = c("ctb", "cph"),
                      comment = "Author of adapted code from the stm package"),
               person(given = "Dustin",
                      family = "Tingley",
                      role = c("ctb", "cph"),
                      comment = "Author of adapted code from the stm package"))
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 (2025)
        <doi:10.1287/mnsc.2022.00261>.
License: MIT + file LICENSE
URL: https://github.com/mankad-research/sts_r
BugReports: https://github.com/mankad-research/sts_r/issues
Depends: R (>= 3.5.0)
Imports: Rcpp, glmnet, matrixStats, slam, foreach, doParallel,
        parallel, Matrix, mvtnorm, ggplot2, tm, SnowballC, graphics,
        stats
Suggests: R.rsp
LinkingTo: Rcpp, RcppArmadillo
VignetteBuilder: R.rsp
Encoding: UTF-8
LazyData: true
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.3.1
Config/pak/sysreqs: libxml2-dev
Repository: https://mankad-research.r-universe.dev
Date/Publication: 2026-10-23 16:55:02 UTC
RemoteUrl: https://github.com/mankad-research/sts_r
RemoteRef: HEAD
RemoteSha: da7d4bd52b767ed548f198e83386a6606705f87b
NeedsCompilation: yes
Packaged: 2026-10-24 09:00:47 UTC; root
Author: Shawn Mankad [aut, cre],
  Li Chen [aut],
  Margaret E. Roberts [ctb, cph] (Author of adapted code from the stm
    package),
  Brandon M. Stewart [ctb, cph] (Author of adapted code from the stm
    package),
  Dustin Tingley [ctb, cph] (Author of adapted code from the stm package)
Maintainer: Shawn Mankad <smankad@ncsu.edu>