mikropml: User-Friendly R Package for Supervised Machine Learning Pipelines

An interface to build machine learning models for classification and regression problems. 'mikropml' implements the ML pipeline described by Topçuoğlu et al. (2020) <doi:10.1128/mBio.00434-20> with reasonable default options for data preprocessing, hyperparameter tuning, cross-validation, testing, model evaluation, and interpretation steps. See the website <http://www.schlosslab.org/mikropml/> for more information, documentation, and examples.

Version: 0.0.2
Depends: R (≥ 2.10)
Imports: caret, dplyr, e1071, glmnet, kernlab, MLmetrics, randomForest, rlang, rpart, stats, utils, xgboost
Suggests: doFuture, foreach, future, future.apply, ggplot2, knitr, purrr, rmarkdown, testthat, tidyr
Published: 2020-12-03
Author: Begüm Topçuoğlu ORCID iD [aut], Zena Lapp ORCID iD [aut], Kelly Sovacool ORCID iD [aut, cre], Evan Snitkin ORCID iD [aut], Jenna Wiens ORCID iD [aut], Patrick Schloss ORCID iD [aut], Nick Lesniak ORCID iD [ctb]
Maintainer: Kelly Sovacool <sovacool at umich.edu>
BugReports: https://github.com/SchlossLab/mikropml/issues
License: MIT + file LICENSE
URL: http://www.schlosslab.org/mikropml/, https://github.com/SchlossLab/mikropml
NeedsCompilation: no
Citation: mikropml citation info
Materials: README NEWS
CRAN checks: mikropml results


Reference manual: mikropml.pdf
Vignettes: Introduction to mikropml
mikropml paper
Package source: mikropml_0.0.2.tar.gz
Windows binaries: r-devel: mikropml_0.0.2.zip, r-release: mikropml_0.0.2.zip, r-oldrel: mikropml_0.0.2.zip
macOS binaries: r-release: mikropml_0.0.2.tgz, r-oldrel: mikropml_0.0.2.tgz
Old sources: mikropml archive


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