{"id":13857306,"url":"https://github.com/SchlossLab/mikropml","last_synced_at":"2025-07-13T21:32:17.155Z","repository":{"id":39918224,"uuid":"226981416","full_name":"SchlossLab/mikropml","owner":"SchlossLab","description":"User-Friendly R Package for Supervised Machine Learning Pipelines","archived":false,"fork":false,"pushed_at":"2025-02-26T04:48:03.000Z","size":150124,"stargazers_count":58,"open_issues_count":19,"forks_count":18,"subscribers_count":7,"default_branch":"main","last_synced_at":"2025-07-08T02:04:23.305Z","etag":null,"topics":["machine-learning","r-package","rstats"],"latest_commit_sha":null,"homepage":"http://www.schlosslab.org/mikropml","language":"R","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"other","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/SchlossLab.png","metadata":{"files":{"readme":"README.Rmd","changelog":"NEWS.md","contributing":".github/CONTRIBUTING.md","funding":null,"license":"LICENSE","code_of_conduct":".github/CODE_OF_CONDUCT.md","threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":".github/SUPPORT.md","governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2019-12-09T22:37:38.000Z","updated_at":"2025-06-02T19:03:07.000Z","dependencies_parsed_at":"2023-02-17T09:31:04.017Z","dependency_job_id":"b1d9e20b-1c77-4616-b1c5-db7495b205c2","html_url":"https://github.com/SchlossLab/mikropml","commit_stats":{"total_commits":1822,"total_committers":25,"mean_commits":72.88,"dds":0.4665203073545554,"last_synced_commit":"3dcc9bc0c49e0e65714fd9a1e0045a749ada76e8"},"previous_names":[],"tags_count":13,"template":false,"template_full_name":null,"purl":"pkg:github/SchlossLab/mikropml","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SchlossLab%2Fmikropml","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SchlossLab%2Fmikropml/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SchlossLab%2Fmikropml/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SchlossLab%2Fmikropml/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/SchlossLab","download_url":"https://codeload.github.com/SchlossLab/mikropml/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SchlossLab%2Fmikropml/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":265016759,"owners_count":23698368,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["machine-learning","r-package","rstats"],"created_at":"2024-08-05T03:01:32.982Z","updated_at":"2025-07-13T21:32:15.746Z","avatar_url":"https://github.com/SchlossLab.png","language":"R","funding_links":[],"categories":["R"],"sub_categories":[],"readme":"---\noutput:\n  github_document\n---\n\n\u003c!-- README.md is generated from README.Rmd. Please edit that file --\u003e\n\n```{r setup, include = FALSE}\nknitr::opts_chunk$set(\n  collapse = TRUE,\n  comment = \"#\u003e\",\n  fig.path = \"man/figures/README-\",\n  out.width = \"100%\"\n)\n```\n\n# mikropml \u003ca href='http://www.schlosslab.org/mikropml/'\u003e\u003cimg src='man/figures/logo.png' align=\"right\" height=\"120\" /\u003e\u003c/a\u003e\n\n\u003e meek-ROPE em el\n\nUser-Friendly R Package for Supervised Machine Learning Pipelines\n\n\u003c!-- badges: start --\u003e\n[![check](https://github.com/SchlossLab/mikropml/workflows/check/badge.svg)](https://github.com/SchlossLab/mikropml/actions?query=workflow%3Acheck+branch%3Amain)\n[![codecov](https://codecov.io/gh/SchlossLab/mikropml/branch/main/graph/badge.svg)](https://app.codecov.io/gh/SchlossLab/mikropml)\n[![license](https://img.shields.io/badge/license-MIT-blue.svg)](https://github.com/SchlossLab/mikropml/blob/main/LICENSE.md)\n[![CRAN](https://img.shields.io/cran/v/mikropml?color=blue\u0026label=CRAN\u0026logo=R)](https://CRAN.R-project.org/package=mikropml)\n[![Conda](https://img.shields.io/conda/vn/conda-forge/r-mikropml)](https://anaconda.org/conda-forge/r-mikropml)\n[![DOI](https://joss.theoj.org/papers/10.21105/joss.03073/status.svg)](https://doi.org/10.21105/joss.03073)\n\u003c!-- badges: end --\u003e\n\nAn interface to build machine learning models for classification and regression\nproblems. `mikropml` implements the ML pipeline described by [Topçuoğlu _et al._\n(2020)](https://doi.org/doi:10.1128/mBio.00434-20) with reasonable default\noptions for data preprocessing, hyperparameter tuning, cross-validation,\ntesting, model evaluation, and interpretation steps. See the\n[website](http://www.schlosslab.org/mikropml/) for more information,\ndocumentation, and examples.\n\n## Installation\n\nYou can install the latest release from\n[CRAN](https://cran.r-project.org/package=mikropml):\n\u003cimg src='https://cranlogs.r-pkg.org/badges/grand-total/mikropml' align='right'/\u003e\n\n```{r install_cran, eval = FALSE}\ninstall.packages('mikropml')\n```\n\nor the development version from \n[GitHub](https://github.com/SchlossLab/mikRopML):\n\n```{r install_github, eval = FALSE}\n# install.packages(\"devtools\")\ndevtools::install_github(\"SchlossLab/mikropml\")\n```\n\nor install from a terminal using\n[conda](https://docs.conda.io/projects/conda/en/latest/index.html)\nor [mamba](https://mamba.readthedocs.io/en/latest/):\n\u003cimg src='https://anaconda.org/conda-forge/r-mikropml/badges/downloads.svg' align='right'/\u003e\n\n```{bash conda, eval = FALSE}\nmamba install -c conda-forge r-mikropml\n```\n\n\n### Dependencies\n\n```{r deps, echo = FALSE, message = FALSE, warning = FALSE}\nlibrary(dplyr)\ndescription \u003c- utils::packageDescription('mikropml', \n                                         fields = c('Imports', 'Suggests'))\ndeps \u003c- lapply(names(description), \n               function (x) {\n                 paste0('- ', x, ': ', \n                        description[[x]] %\u003e% \n                          gsub(\"\\n\", \" \", .))}\n               ) %\u003e% \n  unlist() %\u003e% \n  paste(., collapse = '\\n')\n```\n\n`r deps`\n\n## Usage\n\nCheck out the [introductory\nvignette](http://www.schlosslab.org/mikropml/articles/introduction.html) for a\nquick start tutorial. For a more in-depth discussion, read [all the\nvignettes](http://www.schlosslab.org/mikropml/articles/index.html) and/or take a\nlook at the [reference\ndocumentation](http://www.schlosslab.org/mikropml/reference/index.html). \n\nYou can watch the Riffomonas Project series of \n[video tutorials](https://www.youtube.com/playlist?list=PLmNrK_nkqBpKpzb9-vI4V7SdXC-jXEcmg) \ncovering mikropml and other skills related to machine learning.\n\nWe also provide a \n[Snakemake workflow](https://github.com/SchlossLab/mikropml-snakemake-workflow) \nfor running `mikropml` locally or on an HPC.\nWe highly recommend running `mikropml` with Snakemake or another workflow \nmanagement system for reproducibility and scalability of ML analyses.\n\n\u003ca href=\"https://github.com/SchlossLab/mikropml-snakemake-workflow\"\u003e\n\u003cimg src=\"https://raw.githubusercontent.com/SchlossLab/mikropml-snakemake-workflow/main/figures/mikropml-snakemake-workflow.png\" \nheight=\"120\" align=\"center\" /\u003e\n\u003c/a\u003e\n\n## Help \u0026 Contributing\n\nIf you come across a bug, \n[open an issue](https://github.com/SchlossLab/mikropml/issues) \nand include a \n[minimal reproducible example](https://www.tidyverse.org/help/).\n\nIf you have questions, create a new post in \n[Discussions](https://github.com/SchlossLab/mikropml/discussions).\n\nIf you'd like to contribute, see our guidelines\n[here](http://www.schlosslab.org/mikropml/CONTRIBUTING.html).\n\n## Code of Conduct\n\nPlease note that the mikropml project is released with a [Contributor Code of\nConduct](http://www.schlosslab.org/mikropml/CODE_OF_CONDUCT.html). By\ncontributing to this project, you agree to abide by its terms.\n\n## License\n\nThe mikropml package is licensed under \n[the MIT license](https://github.com/SchlossLab/mikropml/blob/main/LICENSE.md).\nText and images included in this repository, including the mikropml logo, \nare licensed under the [CC BY 4.0 license](https://creativecommons.org/licenses/by/4.0/).\n\n## Citation\n\nTo cite mikropml in publications, use:\n\n\u003e ```{r cite_text, echo = FALSE, results = 'asis'}\n\u003e cat(format(citation('mikropml'), style = 'html'))\n\u003e ```\n\n\nA BibTeX entry for LaTeX users is:\n\n```{r cite_bibtex, echo = FALSE, comment = '', results = 'asis'}\ncat(\"```\\n\", format(citation('mikropml'), style = 'bibtex'), \"\\n```\")\n```\n\n## Why the name?\n\nThe word \"mikrop\" (pronounced \"meek-ROPE\") is Turkish for \"microbe\". This\npackage was originally implemented as a machine learning pipeline for\nmicrobiome-based classification problems (see [Topçuoğlu _et al._\n2020](https://doi.org/10.1128/mBio.00434-20)). We realized that these methods\nare applicable in many other fields too, but stuck with the name because we like\nit!\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FSchlossLab%2Fmikropml","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FSchlossLab%2Fmikropml","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FSchlossLab%2Fmikropml/lists"}