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: |
1.3.5 |
| Depends: |
R (≥ 3.2.2), methods |
| Imports: |
Rcpp (≥ 0.11.3), data.table, glmnet, grDevices, graphics, lda, Matrix, matrixStats, parallel, quadprog, quanteda, slam, splines, stats, stringr, utils |
| LinkingTo: |
Rcpp, RcppArmadillo |
| Suggests: |
clue, geometry, huge, igraph, LDAvis, KernSmooth, NLP, rsvd, Rtsne, SnowballC, spelling, testthat, tm (≥ 0.6), wordcloud |
| Published: |
2019-12-17 |
| Author: |
Margaret Roberts [aut],
Brandon Stewart [aut, cre],
Dustin Tingley [aut],
Kenneth Benoit [ctb] |
| Maintainer: |
Brandon Stewart <bms4 at princeton.edu> |
| BugReports: |
https://github.com/bstewart/stm/issues |
| License: |
MIT + file LICENSE |
| URL: |
http://structuraltopicmodel.com |
| NeedsCompilation: |
yes |
| Language: |
en-US |
| Citation: |
stm citation info |
| Materials: |
NEWS |
| In views: |
NaturalLanguageProcessing |
| CRAN checks: |
stm results |