An interface to build machine learning models for classification and regression problems. 'mikropml' implements the ML pipeline described by TopçuoÄlu 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 DOI: 10.32614/CRAN.package.mikropml Author: Begüm TopçuoÄlu [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: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=mikropml to link to this page.
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