Learn R Programming

glossa (version 1.0.0)

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 Species Spatiotemporal 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.

Copy Link

Version

Install

install.packages('glossa')

Monthly Downloads

220

Version

1.0.0

License

GPL-3

Issues

Pull Requests

Stars

Forks

Maintainer

Jorge Mestre-Tomás

Last Published

October 15th, 2024

Functions in glossa (1.0.0)

glossa_analysis

Main Analysis Function for GLOSSA Package
extract_noNA_cov_values

Extract Non-NA Covariate Values
remove_duplicate_points

Remove Duplicated Points from a Dataframe
pa_optimal_cutoff

Optimal Cutoff for Presence-Absence Prediction
fit_bart_model

Fit a BART Model Using Environmental Covariate Layers
get_covariate_names

Get Covariate Names
glossa_export

Export Glossa Model Results
read_presences_absences_csv

Read and validate presences/absences CSV file
getFprTpr

Compute specificity and sensitivity
read_extent_polygon

Read and Validate Extent Polygon
read_layers_zip

Load Covariate Layers from ZIP Files
predict_bart

Make Predictions Using a BART Model
run_glossa

Run GLOSSA Shiny App
sensitivity

Calculate the sensitivity for a given logit model
remove_points_polygon

Remove Points Inside or Outside a Polygon
generate_pseudo_absences

Generate Pseudo-Absence Points Based on Presence Points, Covariates, and Study Area Polygon
response_curve_bart

Calculate Response Curve Using BART Model
youdensIndex

Calculate Youden's index
generate_prediction_plot

Generate Prediction Plot
sparkvalueBox

Create a Sparkline Value Box
specificity

Calculate the specificity for a given logit model
optimalCutoff

Compute the optimal probability cutoff score
generate_cv_plot

Generate Cross-Validation Plot
misClassError

Misclassification Error
validate_fit_projection_layers

Validate Fit and Projection Layers
validate_layers_zip

Validate Layers Zip
validate_pa_fit_time

Validate Match Between Presence/Absence Files and Fit Layers
variable_importance

Variable Importance in BART Model
export_plot_ui

Create UI for Export Plot Button
cv_bart

Cross-Validation for BART Model
clean_coordinates

Clean Coordinates of Presence/Absence Data
buffer_polygon

Enlarge/Buffer a Polygon
create_coords_layer

Create Geographic Coordinate Layers
invert_polygon

Invert a Polygon
layer_mask

Apply Polygon Mask to Raster Layers
downloadActionButton

Create a Download Action Button
file_input_area_ui

Custom File Input UI
export_plot_server

Server Logic for Export Plot Functionality
file_input_area_server

Server-side Logic for Custom File Input