keyATM: Keyword Assisted Topic Model

Fits keyword assisted topic models (keyATM) using collapsed Gibbs samplers. The keyATM combines the latent dirichlet allocation (LDA) models with a small number of keywords selected by researchers in order to improve the interpretability and topic classification of the LDA. The keyATM can also incorporate covariates and directly model time trends. The keyATM is proposed in Eshima, Imai, and Sasaki (2020) <arXiv:2004.05964>.

Version: 0.1.0
Depends: R (≥ 3.5)
Imports: Rcpp, dplyr, fastmap, ggplot2, ggrepel, magrittr, Matrix, parallel, purrr, quanteda, rlang, stats, stringr, tibble, tidyr
LinkingTo: Rcpp, RcppEigen, RcppProgress
Suggests: readtext
Published: 2020-04-15
Author: Shusei Eshima [aut, cre], Tomoya Sasaki [aut], William Lowe [ctb], Kosuke Imai [aut]
Maintainer: Shusei Eshima <shuseieshima at g.harvard.edu>
BugReports: https://github.com/keyATM/keyATM/issues
License: GPL-3
URL: https://keyatm.github.io/keyATM/
NeedsCompilation: yes
SystemRequirements: C++11
CRAN checks: keyATM results

Downloads:

Reference manual: keyATM.pdf
Package source: keyATM_0.1.0.tar.gz
Windows binaries: r-prerelease: keyATM_0.1.0.zip, r-release: keyATM_0.1.0.zip, r-oldrel: keyATM_0.1.0.zip
macOS binaries: r-prerelease: not available, r-release: keyATM_0.1.0.tgz, r-oldrel: not available

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