mrIML: Multi-Response (Multivariate) Interpretable Machine Learning
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年07月28日
Author:
Nick Fountain-Jones
ORCID 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>
NeedsCompilation:
no
Documentation:
Downloads:
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