Package: glossa 1.0.0
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 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.
Authors:
glossa_1.0.0.tar.gz
glossa_1.0.0.tar.gz(r-4.5-noble)glossa_1.0.0.tar.gz(r-4.4-noble)
glossa_1.0.0.tgz(r-4.4-emscripten)glossa_1.0.0.tgz(r-4.3-emscripten)
glossa.pdf |glossa.html✨
glossa/json (API)
NEWS
# Install 'glossa' in R: |
install.packages('glossa', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/imares-group/glossa/issues
Last updated 1 months agofrom:6f1dab8131. Checks:OK: 2. Indexed: no.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 15 2024 |
R-4.5-linux | OK | Nov 15 2024 |
Exports:buffer_polygonclean_coordinatescreate_coords_layercv_bartdownloadActionButtonexport_plot_serverexport_plot_uiextract_noNA_cov_valuesfile_input_area_serverfile_input_area_uifit_bart_modelgenerate_cv_plotgenerate_prediction_plotgenerate_pseudo_absencesget_covariate_namesgetFprTprglossa_analysisglossa_exportinvert_polygonlayer_maskmisClassErroroptimalCutoffpa_optimal_cutoffpredict_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:abindarrayhelpersaskpassbackportsbase64encbayesplotBHbs4DashbslibcachemcheckmateclassclassIntclicodacodetoolscolorspacecommonmarkcpp11crayoncrosstalkcurldata.tabledbartsDBIdigestdistributionaldotCall64dplyrDTe1071evaluatefansifarverfastmapfieldsfontawesomefreshfsfuturefuture.applygenericsGeoThinneRggdistggplot2ggridgesglobalsgluegtablehighrhtmltoolshtmlwidgetshttpuvhttrisobandjquerylibjsonliteKernSmoothknitrlabelinglaterlatticelazyevalleafletleaflet.providerslifecyclelistenvloomagrittrmapsmarkdownMASSMatrixmatrixStatsmcpmemoisemgcvmimemunsellnabornlmenumDerivopensslparallellypatchworkpillarpkgconfigplyrpngposteriorpROCpromisesproxypurrrquadprogR6rappdirsrasterRColorBrewerRcppRcppEigenreshape2rjagsrlangrmarkdownrstudioapis2sassscalessfshinyshinyWidgetssourcetoolsspspamsparklinestringistringrsvglitesvUnitsyssystemfontstensorAterratibbletidybayestidyrtidyselecttidyterratinytexunitsutf8vctrsviridisLitewaiterwithrwkxfunxtableyamlzip
Readme and manuals
Help Manual
Help page | Topics |
---|---|
Enlarge/Buffer a Polygon | buffer_polygon |
Clean Coordinates of Presence/Absence Data | clean_coordinates |
Create Geographic Coordinate Layers | create_coords_layer |
Cross-Validation for BART Model | cv_bart |
Extract Non-NA Covariate Values | extract_noNA_cov_values |
Fit a BART Model Using Environmental Covariate Layers | fit_bart_model |
Generate Pseudo-Absence Points Based on Presence Points, Covariates, and Study Area Polygon | generate_pseudo_absences |
Main Analysis Function for GLOSSA Package | glossa_analysis |
Invert a Polygon | invert_polygon |
Apply Polygon Mask to Raster Layers | layer_mask |
Optimal Cutoff for Presence-Absence Prediction | pa_optimal_cutoff |
Make Predictions Using a BART Model | predict_bart |
Remove Duplicated Points from a Dataframe | remove_duplicate_points |
Remove Points Inside or Outside a Polygon | remove_points_polygon |
Calculate Response Curve Using BART Model | response_curve_bart |
Run GLOSSA Shiny App | run_glossa |
Variable Importance in BART Model | variable_importance |