mikropml: User-Friendly R Package for Supervised Machine Learning Pipelines (original) (raw)

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:	1.7.0
Depends:	R (≥ 4.1.0)
Imports:	caret, dplyr, e1071, glmnet, kernlab, methods, MLmetrics, randomForest, rlang, rpart, S4Vectors, SingleCellExperiment, stats, SummarizedExperiment, tidyselect, TreeSummarizedExperiment, utils, xgboost
Suggests:	assertthat, doFuture, forcats, foreach, furrr, future, future.apply, ggplot2, knitr, progress, progressr, purrr, rmarkdown, roxygen2, rsample, styler, testthat, tidyr, usethis
Published:	2025-10-29
DOI:	10.32614/CRAN.package.mikropml
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], Tuomas Borman [ctb]
Maintainer:	Kelly Sovacool
BugReports:	https://github.com/SchlossLab/mikropml/issues
License:	MIT + file
URL:	https://www.schlosslab.org/mikropml/,https://github.com/SchlossLab/mikropml
NeedsCompilation:	no
Citation:	mikropml citation info
Materials:	README, NEWS
CRAN checks:	mikropml results

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