mlstm: Multilevel Supervised Topic Models with Multiple Outcomes
Fits latent Dirichlet allocation (LDA), supervised topic models,
and multilevel supervised topic models for text data with multiple
outcome variables. Core estimation routines are implemented in C++
using the 'Rcpp' ecosystem.
For topic models, see Blei et al. (2003) <https://www.jmlr.org/papers/volume3/blei03a/blei03a.pdf>.
For supervised topic models, see Blei and McAuliffe (2007) <https://papers.nips.cc/paper_files/paper/2007/hash/d56b9fc4b0f1be8871f5e1c40c0067e7-Abstract.html>.
| Version: |
0.1.6 |
| Depends: |
R (≥ 4.0.0) |
| Imports: |
Rcpp, Matrix, data.table, RcppParallel, stats |
| LinkingTo: |
Rcpp, RcppArmadillo, RcppParallel, BH |
| Suggests: |
knitr, rmarkdown, testthat (≥ 3.0.0) |
| Published: |
2026-04-03 |
| DOI: |
10.32614/CRAN.package.mlstm (may not be active yet) |
| Author: |
Tomoya Himeno [aut, cre] |
| Maintainer: |
Tomoya Himeno <bd24f002 at g.hit-u.ac.jp> |
| BugReports: |
https://github.com/thimeno1993/mlstm/issues |
| License: |
MIT + file LICENSE |
| URL: |
https://thimeno1993.github.io/mlstm/ |
| NeedsCompilation: |
yes |
| SystemRequirements: |
C++17 |
| Materials: |
README, NEWS |
| CRAN checks: |
mlstm results |
Documentation:
Downloads:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=mlstm
to link to this page.