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 |
| DOI: | 10.32614/CRAN.package.mikropml |
| Author: | Begüm Topçuoğlu |
| 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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