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glossa (version 1.2.2)

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

Description

A user-friendly 'shiny' application for Bayesian machine learning analysis of marine species distributions. GLOSSA (Global Ocean Species Spatio-temporal Analysis) uses Bayesian Additive Regression Trees (BART; Chipman, George, and McCulloch (2010) ) to model species distributions with intuitive workflows for data upload, processing, model fitting, and result visualization. It supports presence-absence and presence-only data (with pseudo-absence generation), spatial thinning, cross-validation, and scenario-based projections. GLOSSA is designed to facilitate ecological research by providing easy-to-use tools for analyzing and visualizing marine species distributions across different spatial and temporal scales.

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Version

Install

install.packages('glossa')

Monthly Downloads

499

Version

1.2.2

License

GPL-3

Issues

Pull Requests

Stars

Forks

Repository

https://github.com/iMARES-group/glossa

Homepage

https://iMARES-group.github.io/glossa/

Maintainer

Jorge Mestre-Tomás

Last Published

July 15th, 2025

Functions in glossa (1.2.2)

Export Glossa Model Results

Invert a Polygon

cross_validate_model

Cross-validation for BART model

downloadActionButton

Create a Download Action Button

generate_pseudo_absences

Generate Pseudo-Absence Points Using Different Methods Based on Presence Points, Covariates, and Study Area Polygon

get_covariate_names

Get Covariate Names

Compute specificity and sensitivity

extract_noNA_cov_values

Extract Non-NA Covariate Values

file_input_area_ui

Custom File Input UI

file_input_area_server

Server-side Logic for Custom File Input

clean_coordinates

Clean Coordinates of Presence/Absence Data

generate_pa_target_group

Generate Pseudo-Absences Using Target-Group Background

Calculate Youden's index

glossa_analysis

Main Analysis Function for GLOSSA Package

variable_importance

Variable Importance in BART Model

generate_prediction_plot

Generate Prediction Plot

plot_cv_folds_points

Plot cross-validation fold assignments

response_curve_bart

Calculate Response Curve Using BART Model

plot_cv_metrics

Plot cross-validation metrics

Run GLOSSA Shiny App

generate_pa_buffer_out

Generate Pseudo-Absences Using Buffer-Out Strategy

Create a Sparkline Value Box

Calculate the sensitivity for a given logit model

read_extent_polygon

Read and Validate Extent Polygon

Make Predictions Using a BART Model

generate_pa_random

Generate Random Pseudo-Absences

Compute the optimal probability cutoff score

pa_optimal_cutoff

Optimal Cutoff for Presence-Absence Prediction

read_layers_zip

Load Covariate Layers from ZIP Files

Apply Polygon Mask to Raster Layers

Misclassification Error

remove_duplicate_points

Remove Duplicated Points from a Dataframe

remove_points_polygon

Remove Points Inside or Outside a Polygon

validate_pa_fit_time

Validate Match Between Presence/Absence Files and Fit Layers

validate_layers_zip

Validate Layers Zip

read_presences_absences_csv

Read and validate presences/absences CSV file

validate_fit_projection_layers

Validate Fit and Projection Layers

Calculate the specificity for a given logit model

Create UI for Export Plot Button

Enlarge/Buffer a Polygon

evaluation_metrics

Evaluation metrics for model predictions

Continuous Boyce Index (CBI) with weighting

create_coords_layer

Create Geographic Coordinate Layers

Fit a BART Model Using Environmental Covariate Layers

export_plot_server

Server Logic for Export Plot Functionality

generate_cv_folds

Generate cross-validation folds