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LDAShiny  

Interactive Topic Modeling and Bibliometric Analysis via Shiny
View on CRAN: Click here


Download and install LDAShiny package within the R console
Install from CRAN:
install.packages("LDAShiny")

Install from Github:
library("remotes")
install_github("cran/LDAShiny")

Install by package version:
library("remotes")
install_version("LDAShiny", "1.0.0")



Attach the package and use:
library("LDAShiny")
Maintained by
Javier De La Hoz Maestre
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2021-02-05
Latest Update:
Description:
Contains the development of a tool that provides a web-based graphical user interface (GUI) to perform a review of the scientific literature under the Bayesian approach of Latent Dirichlet Allocation (LDA)and machine learning algorithms. The application methodology is framed by the well known procedures in topic modelling on how to clean and process data. Contains methods described by Blei, David M., Andrew Y. Ng, and Michael I. Jordan (2003) Allocation"; Thomas L. Griffiths and Mark Steyvers (2004) ; Xiong Hui, et al (2019) .
How to cite:
Javier De La Hoz Maestre (2021). LDAShiny: Interactive Topic Modeling and Bibliometric Analysis via Shiny. R package version 1.0.0, https://cran.r-project.org/web/packages/LDAShiny. Accessed 29 Oct. 2026.
Previous versions and publish date:
0.9.2 (2021-02-05 09:40), 0.9.3 (2021-03-29 12:02), (2026-07-09 08:08)
Other packages that cited LDAShiny R package
View LDAShiny citation profile
Other R packages that LDAShiny depends, imports, suggests or enhances
Complete documentation for LDAShiny
Functions, R codes and Examples using the LDAShiny R package
Some associated functions: crude . removeSparseTerms . runLDAShiny . 
Some associated R codes: crude.R . removeSparseTerms.R . runLDAShiny.R .  Full LDAShiny package functions and examples
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