read_layers_zip

This function loads covariate layers from a ZIP file, verifies their spatial characteristics, and returns them as a list of raster layers.

internal

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)
<doi:10.1214/09-AOAS285>) 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.

Jorge Mestre-Tomás

glossa

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

Alba Fuster-Alonso

read_layers_zip function

<dl><dt>file_path</dt>
<dd>Path to the ZIP file containing covariate layers.</dd>
<dt>extend</dt>
<dd>If TRUE it will take the largest extent, if FALSE the smallest.</dd>
<dt>first_layer</dt>
<dd>If TRUE it will return only the layers from the first timestamp.</dd>
<dt>show_modal</dt>
<dd>Optional. Logical. Whether to show a modal notification for warnings. Default is FALSE.</dd></dl>

Arguments

Load Covariate Layers from ZIP Files — read_layers_zip

<dl>

<dt>file_path</dt>
<dd>Path to the ZIP file containing covariate layers.</dd>


<dt>extend</dt>
<dd>If TRUE it will take the largest extent, if FALSE the smallest.</dd>


<dt>first_layer</dt>
<dd>If TRUE it will return only the layers from the first timestamp.</dd>


<dt>show_modal</dt>
<dd>Optional. Logical. Whether to show a modal notification for warnings. Default is FALSE.</dd>

</dl>

read_layers_zip: Load Covariate Layers from ZIP Files

Description

Usage

Value

Arguments