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


[Up] [Top]

Documentation for package ‘glossa’ version 1.2.2

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