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>.
| Version: |
1.3.3 |
| Depends: |
R (≥ 3.2.2) |
| Imports: |
matrixStats, splines, slam, lda, quanteda, stringr, Matrix, glmnet, Rcpp (≥ 0.11.3), grDevices, graphics, stats, utils, data.table, quadprog, parallel, methods |
| LinkingTo: |
Rcpp, RcppArmadillo |
| Suggests: |
igraph, SnowballC, tm (≥ 0.6), huge, clue, wordcloud, KernSmooth, NLP, LDAvis, geometry, Rtsne, testthat, rsvd |
| Published: |
2018-01-28 |
| Author: |
Margaret Roberts [aut, cre],
Brandon Stewart [aut, cre],
Dustin Tingley [aut, cre],
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 |
| Citation: |
stm citation info |
| Materials: |
NEWS |
| In views: |
NaturalLanguageProcessing |
| CRAN checks: |
stm results |