CBDA: Compressive Big Data Analytics

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

Downloads:

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-devel-gcc8: CBDA_1.0.0.zip, r-release: CBDA_1.0.0.zip, r-oldrel: CBDA_1.0.0.zip
OS X binaries: r-release: CBDA_1.0.0.tgz, r-oldrel: CBDA_1.0.0.tgz

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