Package: LDAShiny 1.0.0

Javier De La Hoz-M

LDAShiny: Interactive Topic Modeling and Bibliometric Analysis via Shiny

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.

Authors:Javier De La Hoz-M [aut, cre]

LDAShiny_1.0.0.tar.gz
LDAShiny_1.0.0.zip(r-4.7)LDAShiny_1.0.0.zip(r-4.6)LDAShiny_1.0.0.zip(r-4.5)
LDAShiny_1.0.0.tgz(r-4.6-any)LDAShiny_1.0.0.tgz(r-4.5-any)
LDAShiny_1.0.0.tar.gz(r-4.7-any)LDAShiny_1.0.0.tar.gz(r-4.6-any)
LDAShiny_1.0.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
LDAShiny/json (API)
NEWS

# Install 'LDAShiny' in R:
install.packages('LDAShiny', repos = c('https://javierdelahoz.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/javierdelahoz/ldashiny/issues

On CRAN:

Conda:

3.60 score 4 stars 3 scripts 213 downloads 14 exports 112 dependencies

Last updated from:50df9a5874. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK221
source / vignettesOK243
linux-release-x86_64OK224
macos-release-arm64OK151
macos-oldrel-arm64OK207
windows-develOK175
windows-releaseOK180
windows-oldrelOK168
wasm-releaseOK156

Exports:mod_about_servermod_about_uimod_import_bibliometrics_servermod_import_bibliometrics_uimod_inference_servermod_inference_uimod_lda_trainmod_lda_train_servermod_lda_train_uimod_preprocess_servermod_preprocess_uimod_trend_servermod_trend_uirun_LDAShiny

Dependencies:attemptbackportsbase64encBHbroombslibcachemcellrangerclicolourpickercommonmarkconfigcpp11crayoncrosstalkdata.tabledigestdplyrDTevaluatefarverfastmapfastmatchfloatfontawesomefsgenericsggplot2gluegolemgtablegtoolsherehighrhmshtmltoolshtmlwidgetshttpuvisobandISOcodesjquerylibjsonliteknitrlabelinglaterlatticelazyevallgrlifecyclemagrittrMatrixMatrixExtramemoisemimeminiUImlapiNLPopenxlsxotelpillarpkgconfigprettyunitsprogresspromisespurrrquantedaR6rappdirsRColorBrewerRcppRcppArmadilloRcppEigenRcppProgressreadxlrematchRhpcBLASctlrlangrmarkdownrprojrootrsparseRSpectraS7sassscalesshinyshinybusyshinydashboardshinyjsshinyWidgetsslamSnowballCsourcetoolsstopwordsstringistringrtext2vectextmineRtibbletidyrtidyselecttinytextmutf8vctrsviridisLitewithrwordcloudxfunxml2xtableyamlzip

Introduction to LDAShiny: Bibliometric Topic Modeling

Javier De La Hoz Maestre, María José Fernandez-Gomez, Susana Mendes

Rendered fromLDAShiny-introduction.Rmdusingknitr::rmarkdownon Jun 08 2026.

Last update: 2026-06-06
Started: 2026-06-06

Readme and manuals

Help Manual

Help pageTopics
About Servermod_about_server
About UImod_about_ui
Import Bibliometrics Servermod_import_bibliometrics_server
Import Bibliometrics UImod_import_bibliometrics_ui
LDA Inference Module Servermod_inference_server
LDA Inference Module UImod_inference_ui
Final LDA Model Training Modulemod_lda_train mod_lda_train_server mod_lda_train_ui
Text Preprocessing Module Servermod_preprocess_server
Text Preprocessing Module UImod_preprocess_ui
Topic Trend Analysis Module Servermod_trend_server
Topic Trend Analysis Module UImod_trend_ui
Run the LDAShiny Applicationrun_LDAShiny