API for pat-s/2019-feature-selection
Research Compendium package for the publication "Monitoring forest health using hyperspectral imagery: Does feature selection improve the performance of machine-learning techniques?"

Global functions
FeatureSelection Man page
FeatureSelection-package Man page
boxcox_response Man page Source code
calc_nri_indices Man page Source code
calc_veg_indices Man page Source code
calculate_vi Man page
clean_single_plots Man page Source code
copy_cloud Man page
copy_figures Source code
create_defoliation_map Man page
create_prediction_df Man page
create_prediction_map Man page Source code
data_preprocessing Man page
download Man page
download_aoi Man page Source code
download_forest_mask Man page Source code
download_hyperspectral Man page Source code
download_images Man page Source code
download_locations Man page Source code
download_trees Man page Source code
extra_to_plot Man page
extract_bands_to_plot Man page Source code
extract_coords Man page Source code
extract_indices_to_plot Man page Source code
feature_imp_parallel Man page Source code
file_cp_ts Man page Source code
get_coordinates Man page
get_records Man page Source code
inv_boxcox_rmse Man page Source code
log_response Man page Source code
mask_mosaic Man page
mask_vi Man page
mosaic_clouds Man page
mosaic_images Man page
mutate_defol Man page Source code
my_pairs Man page Source code
panel.cor Source code
panel.hist Man page Source code
predict_defoliation Man page
prediction_raster Man page
process_hyperspec Man page Source code
process_hyperspec_helper Man page Source code
ras_to_sf Man page
scale_defoliation Man page
split_into_feature_sets Man page Source code
stack_bands Man page Source code
standardize Man page Source code
train_wrapper Man page Source code
tune_ctrl_mbo_30n_70it Man page Source code
tune_ctrl_wrapper Man page Source code
tune_wrapper Man page Source code
unzip_images Man page Source code
pat-s/2019-feature-selection documentation built on Dec. 24, 2021, 8:40 a.m.