User-Friendly 'shiny' App for Bayesian Species Distribution Models

Documentation for package ‘glossa’ version 1.1.0

buffer_polygon	Enlarge/Buffer a Polygon
clean_coordinates	Clean Coordinates of Presence/Absence Data
contBoyce	Continuous Boyce Index (CBI) with weighting
create_coords_layer	Create Geographic Coordinate Layers
cross_validate_model	Cross-validation for BART model
evaluation_metrics	Evaluation metrics for model predictions
extract_noNA_cov_values	Extract Non-NA Covariate Values
fit_bart_model	Fit a BART Model Using Environmental Covariate Layers
generate_cv_folds	Generate cross-validation folds
generate_pa_buffer_out	Generate Pseudo-Absences Using Buffer-Out Strategy
generate_pa_random	Generate Random Pseudo-Absences
generate_pa_target_group	Generate Pseudo-Absences Using Target-Group Background
generate_pseudo_absences	Generate Pseudo-Absence Points Using Different Methods Based on Presence Points, Covariates, and Study Area Polygon
glossa_analysis	Main Analysis Function for GLOSSA Package
invert_polygon	Invert a Polygon
layer_mask	Apply Polygon Mask to Raster Layers
pa_optimal_cutoff	Optimal Cutoff for Presence-Absence Prediction
plot_cv_folds_points	Plot cross-validation fold assignments
predict_bart	Make Predictions Using a BART Model
remove_duplicate_points	Remove Duplicated Points from a Dataframe
remove_points_polygon	Remove Points Inside or Outside a Polygon
response_curve_bart	Calculate Response Curve Using BART Model
run_glossa	Run GLOSSA Shiny App
variable_importance	Variable Importance in BART Model