Interactive Topic Modeling and Bibliometric Analysis via Shiny R package LDAShiny version 1.0.0 Provides a 'Shiny' graphical interface for the complete workflow of Latent Dirichlet Allocation (LDA) topic modelling on bibliometric data from Scopus and Web of Science. Steps include data import and deduplication, text preprocessing (stopword removal, stemming, n-grams, sparse-term filtering), statistical inference to select the optimal number of topics via coherence, final model training, and topic trend analysis over time using linear regression. All results can be exported as Excel files, RDS objects, and publication-quality plots. Software Depends: R (>= 4.1.0) Imports: colourpicker, config (>= 0.3.1), dplyr, DT, ggplot2, golem (>= 0.4.0), Matrix, openxlsx, quanteda, RColorBrewer, readxl, shiny (>= 1.7.0), shinybusy, shinydashboard, shinyjs, shinyWidgets, slam, SnowballC, stopwords, textmineR, tibble, tidyr, tm, wordcloud, broom, parallel, stats, utils, grDevices Suggests: knitr, rmarkdown, testthat (>= 3.0.0), withr Javier De La Hoz-M <jdelahoz@unimagdalena.edu.co> Comprehensive R Archive Network (CRAN) Javier De La Hoz-M [aut, cre] (ORCID: <https://orcid.org/0000-0001-7779-0803>) GPL-3 2026-06-08 application/tgz https://CRAN.R-project.org/package=LDAShiny doi:10.32614/CRAN.package.LDAShiny en-US