Classification performed on Big Data. It uses concepts from compressive sensing, and implements ensemble predictor (i.e., 'SuperLearner') and knockoff filtering as the main machine learning and feature mining engines.
| Version: |
1.0.0 |
| Depends: |
R (≥ 3.3.0) |
| Imports: |
stats , utils , prettydoc , foreach , SuperLearner, parallel , doParallel |
| Suggests: |
knitr, rmarkdown , FNN , e1071 , missForest , knockoff , caret , smotefamily , xgboost , bartMachine , glmnet , randomForest |
| Published: |
2018-04-16 |
| Author: |
Simeone Marino [aut, cre],
Ivo Dinov [aut] |
| Maintainer: |
Simeone Marino <simeonem at umich.edu> |
| License: |
GPL-3 |
| URL: |
https://github.com/SOCR/CBDA |
| NeedsCompilation: |
no |
| CRAN checks: |
CBDA results |