Package: gcube 1.4.4

Ward Langeraert

gcube: Simulating Biodiversity Data Cubes

This R package provides a simulation framework for biodiversity data cubes. This can start from simulating multiple species distributed in a landscape over a temporal scope. In a second phase, the simulation of a variety of observation processes and effort can generate actual occurrence datasets. Based on their (simulated) spatial uncertainty, occurrences can then be designated to a grid to form a data cube.

Authors:Ward Langeraert [aut, cre], Wissam Barhdadi [ctb], Dimitri Brosens [ctb], Rocìo Cortès [ctb], Peter Desmet [ctb], Michele Di Musciano [ctb], Chandra Earl [ctb], Sanne Govaert [ctb], Pieter Huybrechts [ctb], Matilde Martini [ctb], Arthur Rodrigues [ctb], Toon Van Daele [ctb], Annegreet Veeken [ctb], Mukhtar Muhammed Yahaya [ctb], Research Institute for Nature and Forest [cph, pbl], European Union [fnd]

gcube_1.4.4.tar.gz
gcube_1.4.4.zip(r-4.7)gcube_1.4.4.zip(r-4.6)gcube_1.4.4.zip(r-4.5)
gcube_1.4.4.tgz(r-4.6-any)gcube_1.4.4.tgz(r-4.5-any)
gcube_1.4.4.tar.gz(r-4.7-any)gcube_1.4.4.tar.gz(r-4.6-any)
gcube_1.4.4.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
gcube/json (API)

# Install 'gcube' in R:
install.packages('gcube', repos = c('https://b-cubed-eu.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/b-cubed-eu/gcube/issues

Pkgdown/docs site:https://b-cubed-eu.github.io

On CRAN:

Conda:

biodiversity-informaticsdata-cubessimulations

4.62 score 11 stars 23 scripts 21 exports 52 dependencies

Last updated from:df0044a877. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK341
source / vignettesOK240
linux-release-x86_64OK299
macos-release-arm64OK240
macos-oldrel-arm64OK210
windows-develOK290
windows-releaseOK303
windows-oldrelOK276
wasm-releaseOK148

Exports:add_coordinate_uncertaintyapply_manual_sampling_biasapply_polygon_sampling_biascreate_spatial_patternfilter_observationsgenerate_taxonomygrid_designationmap_add_coordinate_uncertaintymap_filter_observationsmap_grid_designationmap_sample_observationsmap_simulate_occurrencesmap_simulation_functionssample_from_binormal_circlesample_from_uniform_circlesample_observationssample_occurrences_from_rastersimulate_occurrencessimulate_random_walksimulate_timeseriesvirtualsample_to_sf

Dependencies:abindassertthatclassclassIntcliclustercpp11DBIdplyre1071FNNgenericsgluegstatintervalsKernSmoothlatticelifecyclemagrittrMASSMatrixmgcvmnormtnlmepermutepillarpkgconfigproxypurrrR6Rcpprlangs2sfsftimespspacetimestarsstringistringrterratibbletidyrtidyselectunitsutf8vctrsveganwithrwkxtszoo

Readme and manuals

Help Manual

Help pageTopics
Add coordinate uncertainty to observationsadd_coordinate_uncertainty
Apply manual sampling bias to occurrences via a gridapply_manual_sampling_bias
Apply sampling bias to occurrences via a polygonapply_polygon_sampling_bias
Create spatial pattern within a polygoncreate_spatial_pattern
Filter detected occurrencesfilter_observations
Generate a taxonomic hierarchygenerate_taxonomy
Observations to grid designation to create a data cubegrid_designation
Map 'add_coordinate_uncertainty()' over multiple speciesmap_add_coordinate_uncertainty
Map 'filter_observations()' over multiple speciesmap_filter_observations
Map 'grid_designation()' over multiple speciesmap_grid_designation
Map 'sample_observations()' over multiple speciesmap_sample_observations
Map 'simulate_occurrences()' over multiple speciesmap_simulate_occurrences
Map a cube simulation function over multiple rows of a dataframemap_simulation_functions
Sample from a circle using the bivariate Normal distributionsample_from_binormal_circle
Sample from a circle using the Uniform distributionsample_from_uniform_circle
Sample observations from a larger occurrence datasetsample_observations
Sample occurrences from spatial random fieldsample_occurrences_from_raster
Simulate species occurrences within a spatiotemporal scopesimulate_occurrences
Simulate a random walk over timesimulate_random_walk
Simulate timeseries for species occurrencessimulate_timeseries
Convert virtualspecies sample to 'sf' formatvirtualsample_to_sf