meek-ROPE em el
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)
with reasonable default options for data preprocessing, hyperparameter
tuning, cross-validation, testing, model evaluation, and interpretation
steps. See the website for more
information, documentation, and examples.
You can install the latest release from CRAN:
install.packages('mikropml')or the development version from GitHub:
# install.packages("devtools")
devtools::install_github("SchlossLab/mikropml")or install from a terminal using conda:
conda install -c conda-forge r-mikropml- Imports: caret, dplyr, e1071, glmnet, kernlab, MLmetrics, randomForest, rlang, rpart, stats, utils, xgboost
- Suggests: doFuture, foreach, future, future.apply, ggplot2, knitr, progress, progressr, purrr, rmarkdown, testthat, tidyr
Check out the introductory
vignette
for a quick start tutorial. For a more in-depth discussion, read all
the vignettes
and/or take a look at the reference
documentation.
We also provide an example Snakemake
workflow for
running mikropml on an HPC.
If you come across a bug, open an issue and include a minimal reproducible example.
If you’d like to contribute, see our guidelines here.
Please note that the mikropml project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.
The mikropml package is licensed under the MIT license. Text and images included in this repository, including the mikropml logo, are licensed under the CC BY 4.0 license.
The word “mikrop” (pronounced “meek-ROPE”) is Turkish for “microbe”. This package was originally implemented as a machine learning pipeline for microbiome-based classification problems (see Topçuoğlu et al. 2020). We realized that these methods are applicable in many other fields too, but stuck with the name because we like it!