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

## Documentation for package ‘glossa’ version 1.1.0

- DESCRIPTION file.
- Package NEWS.

## Help Pages

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