imr() fits the model and returns an object of class
"imr" with print(), summary(),
coef(), plot() and predict()
methods.cv_imr() evaluates predictive accuracy by repeated fold
splits using the fitted MCMC samples (AUC, concordance index or mean
squared error depending on the outcome type), and predict()
predicts new subjects (routing each to its availability subgroup;
platform_names defaults to the training order).plot() shows the inclusion-probability and
MRF-interaction heatmaps and the log-posterior trace;
plot_top_features() and plot_subgroup_sizes()
add a ranked-biomarker bar chart and a subgroup-size bar chart.simIMR, and a
getting-started vignette.verbose = TRUE for progress).NULL and is then drawn
from R’s RNG, so runs follow set.seed() like other
modelling functions.
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