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 <https://www.schlosslab.org/mikropml/> for more information, documentation, and examples.

Version:	1.6.1
Depends:	R (≥ 4.1.0)
Imports:	caret, dplyr, e1071, glmnet, kernlab, MLmetrics, randomForest, rlang, rpart, stats, utils, xgboost
Suggests:	assertthat, doFuture, forcats, foreach, future, future.apply, furrr, ggplot2, knitr, progress, progressr, purrr, rmarkdown, rsample, testthat, tidyr
Published:	2023-08-21
Author:	Begüm Topçuoğlu [aut], Zena Lapp [aut], Kelly Sovacool [aut, cre], Evan Snitkin [aut], Jenna Wiens [aut], Patrick Schloss [aut], Nick Lesniak [ctb], Courtney Armour [ctb], Sarah Lucas [ctb]
Maintainer:	Kelly Sovacool <sovacool at umich.edu>
BugReports:	https://github.com/SchlossLab/mikropml/issues
License:	MIT + file LICENSE
URL:	https://www.schlosslab.org/mikropml/, https://github.com/SchlossLab/mikropml
NeedsCompilation:	no
Citation:	mikropml citation info
Materials:	README NEWS
CRAN checks:	mikropml results

Documentation:

Reference manual:	mikropml.pdf
Vignettes:	Introduction to mikropml mikropml paper

Downloads:

Package source:	mikropml_1.6.1.tar.gz
Windows binaries:	r-devel: mikropml_1.6.1.zip, r-release: mikropml_1.6.1.zip, r-oldrel: mikropml_1.6.1.zip
macOS binaries:	r-release (arm64): mikropml_1.6.1.tgz, r-oldrel (arm64): mikropml_1.6.1.tgz, r-release (x86_64): mikropml_1.6.1.tgz, r-oldrel (x86_64): mikropml_1.6.1.tgz
Old sources:	mikropml archive

Linking:

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