Perform a supervised data analysis on a database through a 'shiny' graphical interface. It includes methods such as K-Nearest Neighbors, Decision Trees, ADA Boosting, Extreme Gradient Boosting, Random Forest, Neural Networks, Deep Learning, Support Vector Machines and Bayesian Methods.
Version: | 1.0.4 |
Depends: | R (≥ 3.5) |
Imports: | shinyAce (≥ 0.3.3), shinydashboardPlus (≥ 0.6.0), shinyWidgets (≥ 0.4.4), shinyjs (≥ 1.0), flexdashboard (≥ 0.5.1.1), tidyverse (≥ 1.2.1), neuralnet (≥ 1.44.2), rpart (≥ 4.1-13), rattle (≥ 5.2.0), xgboost (≥ 0.81.0.1), ada (≥ 2.0-5), zip (≥ 1.0.0), colourpicker (≥ 1.0), DT (≥ 0.5), randomForest (≥ 4.6-14), e1071 (≥ 1.7-0.1), kknn (≥ 1.3.1), scatterplot3d (≥ 0.3-41), corrplot (≥ 0.84), ROCR (≥ 1.0-7) |
Suggests: | shiny |
Published: | 2019-03-03 |
Author: | Oldemar Rodriguez R. with contributions from Diego Jimenez A. and Andres Navarro D. |
Maintainer: | Oldemar Rodriguez <oldemar.rodriguez at ucr.ac.cr> |
License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
URL: | http://www.promidat.com |
NeedsCompilation: | no |
CRAN checks: | predictoR results |
Reference manual: | predictoR.pdf |
Package source: | predictoR_1.0.4.tar.gz |
Windows binaries: | r-devel: predictoR_1.0.4.zip, r-release: predictoR_1.0.4.zip, r-oldrel: not available |
OS X binaries: | r-release: predictoR_1.0.4.tgz, r-oldrel: not available |
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