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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) <https://jmlr.org/papers/volume3/blei03a/blei03a.pdf> Allocation"; Thomas L. Griffiths and Mark Steyvers (2004) <doi:10.1073/pnas.0307752101> ; Xiong Hui, et al (2019) <doi:10.1016/j.cie.2019.06.010>.
Version: | 0.9.3 |
Imports: | beepr, broom, chinese.misc, dplyr, DT (≥ 0.15), highcharter, htmlwidgets, ldatuning, parallel, plotly, purrr, quanteda, shiny, shinyalert, shinyBS, shinycssloaders, shinydashboard, shinyjs, shinyWidgets, SnowballC, stringr, textmineR, tidyr, tidytext, tm, topicmodels |
Suggests: | knitr, RColorBrewer, rmarkdown, Rmpfr, scales, magrittr |
Published: | 2021-03-29 |
DOI: | 10.32614/CRAN.package.LDAShiny |
Author: | Javier De La Hoz Maestre
|
Maintainer: | Javier De La Hoz Maestre <jdelahozmaestre at gmail.com> |
BugReports: | https://github.com/JavierDeLaHoz/LDAShiny/issues |
License: | GPL-3 |
NeedsCompilation: | no |
CRAN checks: | LDAShiny results |
Reference manual: | LDAShiny.pdf |
Vignettes: |
A brief introduction to LDAShiny Una breve introducción a LDAShiny |
Package source: | LDAShiny_0.9.3.tar.gz |
Windows binaries: | r-devel: LDAShiny_0.9.3.zip, r-release: LDAShiny_0.9.3.zip, r-oldrel: LDAShiny_0.9.3.zip |
macOS binaries: | r-release (arm64): LDAShiny_0.9.3.tgz, r-oldrel (arm64): LDAShiny_0.9.3.tgz, r-release (x86_64): LDAShiny_0.9.3.tgz, r-oldrel (x86_64): LDAShiny_0.9.3.tgz |
Old sources: | LDAShiny archive |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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