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: |
2.2.0 |
| 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: |
2025-11-21 |
| DOI: |
10.32614/CRAN.package.mrIML |
| Author: |
Nick Fountain-Jones
 [aut, cre,
cph],
Ryan Leadbetter
[aut],
Gustavo Machado
[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 LICENSE |
| URL: |
https://github.com/nickfountainjones/mrIML |
| NeedsCompilation: |
no |
| Materials: |
README, NEWS |
| CRAN checks: |
mrIML results |