stm: Estimation of the Structural Topic Model
The Structural Topic Model (STM) allows researchers
to estimate topic models with document-level covariates.
The package also includes tools for model selection, visualization,
and estimation of topic-covariate regressions. Methods developed in
Roberts et. al. (2014) <doi:10.1111/ajps.12103> and
Roberts et. al. (2016) <doi:10.1080/01621459.2016.1141684>. Vignette
is Roberts et. al. (2019) <doi:10.18637/jss.v091.i02>.
Version:
1.3.8
Depends:
R (≥ 3.5.0), methods
Imports:
Rcpp (≥ 0.11.3),
data.table,
glmnet, grDevices, graphics,
lda,
Matrix,
matrixStats, parallel,
quadprog,
quanteda,
slam, splines, stats,
stringr, utils
Suggests:
clue,
geometry,
huge,
hunspell,
igraph,
LDAvis,
KernSmooth,
NLP,
rsvd,
Rtsne,
SnowballC,
spelling,
testthat,
tm (≥ 0.6),
wordcloud
Published:
2025年09月03日
Author:
Margaret Roberts [aut],
Brandon Stewart [aut, cre],
Dustin Tingley [aut],
Kenneth Benoit [ctb]
Maintainer:
Brandon Stewart <bms4 at princeton.edu>
NeedsCompilation:
yes
Language:
en-US
Documentation:
Downloads:
Reverse dependencies:
Linking:
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