Implementation of a shiny app to easily compare supervised regression model performances. You provide the data and configure each model parameter directly on the shiny app. Four main supervised learning algorithms can be tested either on Spark or H2O frameworks to suit your regression problem on a given time series. Implementation of these time series forecasting methods on R has been done by Shmueli and Lichtendahl (2015, ISBN:0991576632).
| Version: | 0.2.0 |
| Depends: | dplyr, data.table |
| Imports: | shiny (≥ 1.0.3), shinydashboard, h2o, shinyWidgets, dygraphs, plotly, sparklyr, tidyr, DT, ggplot2, shinycssloaders |
| Suggests: | knitr, rmarkdown, covr, testthat |
| Published: | 2019-10-29 |
| Author: | Jean Bertin |
| Maintainer: | Jean Bertin <jean.bertin at gadz.org> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| Materials: | README NEWS |
| CRAN checks: | shinyML results |
| Reference manual: | shinyML.pdf |
| Vignettes: |
Getting started with shinyML |
| Package source: | shinyML_0.2.0.tar.gz |
| Windows binaries: | r-devel: shinyML_0.2.0.zip, r-release: shinyML_0.2.0.zip, r-oldrel: shinyML_0.2.0.zip |
| macOS binaries: | r-release: shinyML_0.2.0.tgz, r-oldrel: shinyML_0.2.0.tgz |
| Old sources: | shinyML archive |
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