doi:10.18653/v1/2021.findings-emnlp.201>. By a sequential approach, it enables the construction of LDA-based time series of topics that are consistent with previous states of LDA models. After an initial modeling, updates can be computed efficiently, allowing for real-time monitoring and detection of events or structural breaks.">

rollinglda: Construct Consistent Time Series from Textual Data (original) (raw)

A rolling version of the Latent Dirichlet Allocation, see Rieger et al. (2021) <doi:10.18653/v1/2021.findings-emnlp.201>. By a sequential approach, it enables the construction of LDA-based time series of topics that are consistent with previous states of LDA models. After an initial modeling, updates can be computed efficiently, allowing for real-time monitoring and detection of events or structural breaks.

Version: 0.1.4
Depends: R (≥ 4.0.0), ldaPrototype (≥ 0.3.0)
Imports: checkmate (≥ 1.8.5), data.table (≥ 1.11.2), lubridate, stats, tosca (≥ 0.2-0), utils
Suggests: covr, testthat
Published: 2025-08-20
DOI: 10.32614/CRAN.package.rollinglda
Author: Jonas Rieger ORCID iD [aut, cre]
Maintainer: Jonas Rieger <jonas.rieger at tu-dortmund.de>
BugReports: https://github.com/JonasRieger/rollinglda/issues
License: GPL (≥ 3)
URL: https://github.com/JonasRieger/rollinglda
NeedsCompilation: yes
Citation: rollinglda citation info
Materials: README
CRAN checks: rollinglda results

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