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: 0.5.7
Depends: R (≥ 3.6), R6 (≥ 2.2)
Imports: data.table (≥ 1.10), Rcpp (≥ 1.0), assertthat (≥ 0.2), Metrics (≥ 0.1)
LinkingTo: Rcpp, BH, RcppArmadillo
Suggests: knitr, rlang, testthat, rmarkdown, naivebayes (≥ 0.9), ClusterR (≥ 1.1), FNN (≥ 1.1), ranger (≥ 0.10), caret (≥ 6.0), xgboost (≥ 0.6), glmnet (≥ 2.0), e1071 (≥ 1.7)
Published: 2024-02-18
DOI: 10.32614/CRAN.package.superml
Author: Manish Saraswat [aut, cre]
Maintainer: Manish Saraswat <manish06saraswat at>
License: GPL-3 | file LICENSE
NeedsCompilation: yes
Materials: README NEWS
CRAN checks: superml results


Reference manual: superml.pdf
Vignettes: Guide to CountVectorizer
How to use TfidfVectorizer in R ?
Introduction to SuperML


Package source: superml_0.5.7.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): superml_0.5.7.tgz, r-oldrel (arm64): superml_0.5.7.tgz, r-release (x86_64): superml_0.5.7.tgz, r-oldrel (x86_64): superml_0.5.7.tgz
Old sources: superml archive


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