shinyML: Compare Supervised Machine Learning Models Using Shiny App

Implementation of a shiny app to easily compare supervised machine learning model performances. You provide the data and configure each model parameter directly on the shiny app. Different supervised learning algorithms can be tested either on Spark or H2O frameworks to suit your regression and classification tasks. Implementation of available machine learning models on R has been done by Lantz (2013, ISBN:9781782162148).

Version: 1.0.1
Depends: dplyr, data.table
Imports: shiny (≥ 1.0.3), argonDash, argonR, shinyjs, h2o, shinyWidgets, dygraphs, plotly, sparklyr, tidyr, DT, ggplot2, shinycssloaders, lubridate, graphics
Suggests: knitr, rmarkdown, covr, testthat
Published: 2021-02-24
Author: Jean Bertin
Maintainer: Jean Bertin <jean.bertin at mines-paris.org>
BugReports: https://github.com/JeanBertinR/shinyML/issues
License: GPL-3
URL: https://jeanbertinr.github.io/shinyMLpackage/
NeedsCompilation: no
Materials: README NEWS
CRAN checks: shinyML results

Downloads:

Reference manual: shinyML.pdf
Package source: shinyML_1.0.1.tar.gz
Windows binaries: r-devel: shinyML_1.0.1.zip, r-release: shinyML_1.0.1.zip, r-oldrel: shinyML_1.0.1.zip
macOS binaries: r-release: shinyML_1.0.1.tgz, r-oldrel: shinyML_1.0.1.tgz
Old sources: shinyML archive

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