devianceResids()

Calculate binary outcome deviance residuals using probability predictions 
Features

Landscape and host feature (variable) data 
filterRareCommon()

Filter rare response variables from the data 
gfData

Regression data from Fitzpatrick et al 2014 
mrFlashlight()

mrFlashlight: Wrapper to run multiresponse model agnostic interpretable machine learning analyses. 
mrIMLperformance()

Wrapper to calculate performance metrics (Mathews correlation coefficient, sensitivity and specificity) for each model for each response variable. 
mrIMLpredicts()

Wrapper to generate multiresponse predictive models. 
mrInteractions()

Wrapper to calculate and plot summed interactions of features across response variables. Based on Greenwell et al 2018 
mrPlot_interactions()

Plots global interactions as well as individual response interaction importance. 
mrProfileplot()

mrProfileplot: Wrapper to plot mutltiresponse model agnostic profile plots (partial dependences and accumulated local effects). 
mrVip()

Wrapper to estimate modelagnostic variable importance for multiresponse models. 
plot_vi()

Plots global importance (i.e. cumulative importance of all features for all response variables) and individual response importance. 
readSnpsPed()

Conversion to single column per locus from plink file via LEA functionality 
resist_components()

Calculates resistance components from a list of pairwise resistance surfaces. 
Responsedata

Viral single nucleuotide polymorphism (SNP) data. 
vintTidy()

vintTidy: Interaction effects for tidymodel objects 
mrBenchmark()

Compare and benchmark disease outbreak risk among and within groups 
mrLocalExplainer()

Run local explanation methods for individual data points 
MrIMLconverts()

Converts MrIML objects into a format suitable for IML R package 
interpret_Mrvi()

Plots global importance (i.e. cumulative importance of all features for all response variables) and individual response importance. 
mrProfile2D()

Plots multiresponse 2D profile plots to help interpret interactions 
plot_vi_biosecurity()

Plots global importance (i.e. cumulative importance of all features for all response variables) and individual response importance. 