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: 0.2.0
Depends: R (≥ 3.6)
Imports: Rcpp, dplyr, fastmap, ggplot2, ggrepel, magrittr, Matrix, parallel, purrr, quanteda, rlang, stats, stringr, tibble, tidyr (≥ 1.0.0)
LinkingTo: Rcpp, RcppEigen, RcppProgress
Suggests: readtext, testthat (≥ 2.1.0)
Published: 2020-05-23
Author: Shusei Eshima ORCID iD [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
Citation: keyATM citation info
Materials: NEWS
CRAN checks: keyATM results

Downloads:

Reference manual: keyATM.pdf
Package source: keyATM_0.2.0.tar.gz
Windows binaries: r-devel: keyATM_0.2.0.zip, r-release: keyATM_0.2.0.zip, r-oldrel: keyATM_0.2.0.zip
macOS binaries: r-release: keyATM_0.1.0.tgz, r-oldrel: keyATM_0.2.0.tgz
Old sources: keyATM archive

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