superml: Build Machine Learning Models Like Using Python's Scikit-Learn Library in R

The idea is to provide a standard interface to users who use both R and Python for building machine learning models. This package provides a scikit-learn's fit, predict interface to train machine learning models in R.

Version: 0.2.0
Depends: R (≥ 3.4), R6 (≥ 2.2)
Imports: data.table (≥ 1.10), assertthat (≥ 0.2), Metrics (≥ 0.1), xgboost (≥ 0.6), glmnet (≥ 2.0), parallel, kableExtra, tm (≥ 0.7), naivebayes (≥ 0.9), ClusterR (≥ 1.1), FNN (≥ 1.1), liquidSVM (≥ 1.2), ranger (≥ 0.10), caret (≥ 6.0), doParallel (≥ 1.0)
Suggests: knitr, rlang, testthat, rmarkdown
Published: 2019-01-07
Author: Manish Saraswat [aut, cre]
Maintainer: Manish Saraswat <manish06saraswat at>
License: GPL-3 | file LICENSE
NeedsCompilation: no
Materials: README NEWS
CRAN checks: superml results


Reference manual: superml.pdf
Vignettes: Introduction to SuperML
Package source: superml_0.2.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X binaries: r-release: superml_0.2.0.tgz, r-oldrel: not available
Old sources: superml archive


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