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 |
| Reference manual: | CBDA.pdf |
| Vignettes: |
Guide to Compressive Big Data Analytics [CBDA] |
| Package source: | CBDA_1.0.0.tar.gz |
| Windows binaries: | r-devel: CBDA_1.0.0.zip, r-release: CBDA_1.0.0.zip, r-oldrel: CBDA_1.0.0.zip |
| macOS binaries: | r-release: CBDA_1.0.0.tgz, r-oldrel: CBDA_1.0.0.tgz |
Please use the canonical form https://CRAN.R-project.org/package=CBDA to link to this page.