Reference

Core model development workflow These functions prepare spectra for model development and allow for the streamlined testing of model performance.
plot_spectra()	Plot spectral data, highlighting outliers as identified using Mahalanobis distance
filter_spectra()	Filter spectral data frame based on Mahalanobis distance
aggregate_spectra()	Aggregate data based on grouping variables and a user-provided function
test_spectra()	Test the performance of spectral models
Internal use While these functions can be used on their own, they were developed to be called within test_spectra().
pretreat_spectra()	Pretreat spectral data according to user-designated method
format_cv()	Format multiple trials with or without overlapping genotypes into training and test sets according to user-provided cross validation scheme
train_spectra()	Train a model based predict reference values with spectral data
Model development and testing for routine use
save_model()	Save spectral prediction model and model performance statistics
predict_spectra()	Use provided model object to predict trait values with input dataset
Example Dataset
ikeogu.2017	Example vis-NIRS and reference dataset