Multi-Response (Multivariate) Interpretable Machine Learning
A cbind variation that ignores objects with zero dimention
Filter rare response variables from the data
Bootstrap mrIML model predictions
Generate a MrIML co-occurrence network
Investigate partial dependencies of a covariate for mrIML JSDMs (Joint...
Convert mrIML object into a flashlight object
mrIML: Multi-Response (Multivariate) Interpretable Machine Learning
Calculate general performance metrics of a mrIML model
Generates a multi-response predictive model
Calculate and visualize feature interactions
Bootstrap Partial Dependence Plots
Plot Model Performance Comparison
Generate SHAP (SHapley Additive exPlanations) Plots for Multiple Model...
Calculates and helps interpret variable importance for mrIML models.
Principal Component Analysis of mrIML variable importance
Pipe operator
Conversion to single column per locus from plink file via LEA function...
Calculates resistance components from a list of pairwise resistance su...
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>.
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