doi:10.1111/1755-0998.13495> and Fountain-Jones et al. (2024) <doi:10.22541/au.172676147.77148600/v1>.">

mrIML: Multi-Response (Multivariate) Interpretable Machine Learning (original) (raw)

Builds and interprets multi-response machine learning models using 'tidymodels' syntax. Users can supply a tidy model, and 'mrIML' automates the process of fitting multiple response models to multivariate data and applying interpretable machine learning techniques across them. For more details see Fountain-Jones (2021) <doi:10.1111/1755-0998.13495> and Fountain-Jones et al. (2024) <doi:10.22541/au.172676147.77148600/v1>.

Version: 2.1.0
Depends: R (≥ 3.5.0)
Imports: dplyr, magrittr, rlang, ggplot2, patchwork, purrr, recipes, rsample, tibble, tidyr, tidyselect, tune, workflows, yardstick, flashlight, future.apply, MetricsWeighted, finetune, hstats
Suggests: knitr, rmarkdown, testthat (≥ 3.0.0), ape, vegan, hardhat, ggrepel, themis, MRFcov, lme4, randomForest, ggnetwork, igraph, tidymodels, tidyverse, parsnip, gridExtra, future, generics, missForest, kernelshap, shapviz
Published: 2025-07-28
DOI: 10.32614/CRAN.package.mrIML
Author: Nick Fountain-JonesORCID iD [aut, cre, cph], Ryan Leadbetter ORCID iD [aut], Gustavo Machado ORCID iD [aut], Chris Kozakiewicz [aut], Nick Clark [aut]
Maintainer: Nick Fountain-Jones <nick.fountainjones at utas.edu.au>
BugReports: https://github.com/nickfountainjones/mrIML/issues
License: MIT + file
URL: https://github.com/nickfountainjones/mrIML
NeedsCompilation: no
Materials: README, NEWS
CRAN checks: mrIML results

Documentation:

Downloads:

Linking:

Please use the canonical formhttps://CRAN.R-project.org/package=mrIMLto link to this page.