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: |
4.1.5 |
| Depends: |
R (≥ 4.1) |
| Imports: |
DT (≥ 0.27), dplyr (≥ 1.1.0), shiny (≥ 1.7.4), golem (≥
0.3.5), rlang (≥ 1.0.6), loadeR (≥ 1.0.1), config (≥ 0.3.1), glmnet (≥ 4.1-6), traineR (≥ 2.2.0), shinyjs (≥ 2.1.0), xgboost (≥ 1.7.3.1), shinyAce (≥ 0.4.2), echarts4r (≥
0.4.4), htmltools (≥ 0.5.4), rpart.plot (≥ 3.1.1), colourpicker (≥ 1.1.1), shinydashboard (≥ 0.7.2), shinycustomloader (≥ 0.9.0), shinydashboardPlus (≥ 2.0.3) |
| Published: |
2025-05-28 |
| DOI: |
10.32614/CRAN.package.predictoR |
| Author: |
Oldemar Rodriguez [aut, cre],
Diego Jiménez [ctb, prg],
Andrés Navarro [ctb, prg] |
| Maintainer: |
Oldemar Rodriguez <oldemar.rodriguez at ucr.ac.cr> |
| BugReports: |
https://github.com/PROMiDAT/predictoR/issues |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: |
https://promidat.website/ |
| NeedsCompilation: |
no |
| Language: |
en-US |
| CRAN checks: |
predictoR results |