Package: glossa 1.2.4

Jorge Mestre-Tomás

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

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) <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. Optionally, pseudo-absences can be generated within the environmental space using the external package 'flexsdm' (not on CRAN), which can be downloaded from <https://github.com/sjevelazco/flexsdm>; this functionality is used conditionally when available and all core features work without it.

Authors:Jorge Mestre-Tomás [aut, cre], Alba Fuster-Alonso [aut]

glossa_1.2.4.tar.gz
glossa_1.2.4.zip(r-4.7)glossa_1.2.4.zip(r-4.6)glossa_1.2.4.zip(r-4.5)
glossa_1.2.4.tgz(r-4.6-any)glossa_1.2.4.tgz(r-4.5-any)
glossa_1.2.4.tar.gz(r-4.7-any)glossa_1.2.4.tar.gz(r-4.6-any)
glossa_1.2.4.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
glossa/json (API)
NEWS

# Install 'glossa' in R:
install.packages('glossa', repos = c('https://imares-group.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/imares-group/glossa/issues

Uses libs:
  • jags– Just Another Gibbs Sampler for Bayesian MCMC - binary JAGS is Just Another Gibbs Sampler. It is a program for analysis of Bayesian hierarchical models using Markov Chain Monte Carlo (MCMC) simulation not wholly unlike BUGS. JAGS was written with three aims in mind: * To have an engine for the BUGS language that runs on Unix * To be extensible, allowing users to write their own functions, distributions and samplers. * To be a plaftorm for experimentation with ideas in Bayesian modelling This package contains the 'jags' binary as well as the associated shared library modules loaded by the binary.
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

jagscpp

4.35 score 5 stars 10 scripts 211 downloads 47 exports 153 dependencies

Last updated from:582da71625. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK241
source / vignettesOK276
linux-release-x86_64OK237
macos-release-arm64OK169
macos-oldrel-arm64OK203
windows-develOK196
windows-releaseOK186
windows-oldrelOK193
wasm-releaseOK205

Exports:buffer_polygonclean_coordinatescontBoycecreate_coords_layercross_validate_modeldownloadActionButtonevaluation_metricsexport_plot_serverexport_plot_uiextract_noNA_cov_valuesfile_input_area_serverfile_input_area_uifit_bart_modelgenerate_cv_foldsgenerate_pa_buffer_outgenerate_pa_env_space_flexsdmgenerate_pa_randomgenerate_pa_target_groupgenerate_prediction_plotgenerate_pseudo_absencesget_covariate_namesgetFprTprglossa_analysisglossa_exportinvert_polygonlayer_maskmisClassErroroptimalCutoffpa_optimal_cutoffplot_cv_folds_pointsplot_cv_metricspredict_bartread_extent_polygonread_layers_zipread_presences_absences_csvremove_duplicate_pointsremove_points_polygonresponse_curve_bartrun_glossasensitivitysparkvalueBoxspecificityvalidate_fit_projection_layersvalidate_layers_zipvalidate_pa_fit_timevariable_importanceyoudensIndex

Dependencies:abindarrayhelpersaskpassautomapbackportsbase64encbayesplotBHblockCVbs4DashbslibcachemcheckmateclassclassIntclicodacodetoolscommonmarkcowplotcpp11crosstalkcurldata.tabledbartsDBIdigestdistributionaldoParalleldotCall64dplyrDTe1071evaluatefarverfastmapfieldsFNNfontawesomeforeachfreshfsfuturefuture.applygenericsGeoThinneRggdistggplot2ggridgesglobalsgluegstatgtablehighrhtmltoolshtmlwidgetshttpuvhttrintervalsisobanditeratorsjquerylibjsonliteKernSmoothknitrlabelinglaterlatticelazyevalleafletleaflet.providerslifecyclelistenvlitedownloomagrittrmapsmarkdownMASSmatrixStatsmcpmemoisemimenabornumDerivopensslotelparallellypatchworkpillarpkgconfigplyrpngposteriorpROCpromisesproxypurrrquadprogR6rappdirsrasterRColorBrewerRcppRcppEigenreshapereshape2rjagsrlangrmarkdownrstudioapis2S7sassscalessfsftimeshinyshinyWidgetssourcetoolsspspacetimespamsparklinestarsstringistringrsvglitesvUnitsyssystemfontstensorAterratextshapingtibbletidybayestidyrtidyselecttidyterratinytexunitsutf8vctrsviridisLitewaiterwithrwkxfunxtablextsyamlzipzoo

Readme and manuals

Help Manual

Help pageTopics
Enlarge/Buffer a Polygonbuffer_polygon
Clean Coordinates of Presence/Absence Dataclean_coordinates
Continuous Boyce Index (CBI) with weightingcontBoyce
Create Geographic Coordinate Layerscreate_coords_layer
Cross-validation for BART modelcross_validate_model
Evaluation metrics for model predictionsevaluation_metrics
Extract Non-NA Covariate Valuesextract_noNA_cov_values
Fit a BART Model Using Environmental Covariate Layersfit_bart_model
Generate cross-validation foldsgenerate_cv_folds
Generate Pseudo-Absences Using Buffer-Out Strategygenerate_pa_buffer_out
Generate Environmental-space Pseudo-Absences via flexsdm (per temporal stratum)generate_pa_env_space_flexsdm
Generate Random Pseudo-Absencesgenerate_pa_random
Generate Pseudo-Absences Using Target-Group Backgroundgenerate_pa_target_group
Generate Pseudo-Absence Points Using Different Methods Based on Presence Points, Covariates, and Study Area Polygongenerate_pseudo_absences
Main Analysis Function for GLOSSA Packageglossa_analysis
Invert a Polygoninvert_polygon
Apply Polygon Mask to Raster Layerslayer_mask
Optimal Cutoff for Presence-Absence Predictionpa_optimal_cutoff
Plot cross-validation fold assignmentsplot_cv_folds_points
Make Predictions Using a BART Modelpredict_bart
Remove Duplicated Points from a Dataframeremove_duplicate_points
Remove Points Inside or Outside a Polygonremove_points_polygon
Calculate Response Curve Using BART Modelresponse_curve_bart
Run GLOSSA Shiny Apprun_glossa
Variable Importance in BART Modelvariable_importance