Package: S4DM 0.0.2
S4DM: Small Sample Size Species Distribution Modeling
Implements a set of distribution modeling methods that are suited to species with small sample sizes (e.g., poorly sampled species or rare species). While these methods can also be used on well-sampled taxa, they are united by the fact that they can be utilized with relatively few data points. More details on the currently implemented methodologies can be found in Drake and Richards (2018) <doi:10.1002/ecs2.2373>, Drake (2015) <doi:10.1098/rsif.2015.0086>, and Drake (2014) <doi:10.1890/ES13-00202.1>.
Authors:
S4DM_0.0.2.tar.gz
S4DM_0.0.2.zip(r-4.5)S4DM_0.0.2.zip(r-4.4)S4DM_0.0.2.zip(r-4.3)
S4DM_0.0.2.tgz(r-4.5-any)S4DM_0.0.2.tgz(r-4.4-any)S4DM_0.0.2.tgz(r-4.3-any)
S4DM_0.0.2.tar.gz(r-4.5-noble)S4DM_0.0.2.tar.gz(r-4.4-noble)
S4DM_0.0.2.tgz(r-4.4-emscripten)S4DM_0.0.2.tgz(r-4.3-emscripten)
S4DM.pdf |S4DM.html✨
S4DM/json (API)
NEWS
| # Install 'S4DM' in R: |
| install.packages('S4DM', repos = c('https://bmaitner.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/bmaitner/s4dm/issues
- sample_points - Example S4DM occurrence data
open-sciencerange-modellingrare-speciesspecies-distribution-modelingspecies-distribution-modelling
Last updated 2 months agofrom:fcf3bcc33e. Checks:6 OK, 3 NOTE. Indexed: yes.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Mar 08 2025 |
| R-4.5-win | NOTE | Mar 08 2025 |
| R-4.5-mac | NOTE | Mar 08 2025 |
| R-4.5-linux | NOTE | Mar 08 2025 |
| R-4.4-win | OK | Mar 08 2025 |
| R-4.4-mac | OK | Mar 08 2025 |
| R-4.4-linux | OK | Mar 08 2025 |
| R-4.3-win | OK | Mar 08 2025 |
| R-4.3-mac | OK | Mar 08 2025 |
Exports:ensemble_range_mapevaluate_range_mapfit_density_ratiofit_plug_and_playget_env_bgget_env_presmake_range_mapproject_density_ratioproject_plug_and_playsdm_thresholdstratify_randomstratify_spatial
Dependencies:abindassertthatBHbootclassclassIntclicodetoolscorpcorcubatureDBIdensratioDEoptimRdplyre1071fansifit.modelsflexclustforeachgenericsgeometryglmnetglueiteratorskde1dkernlabKernSmoothlatticelifecyclelinproglpSolvemagicmagrittrMASSMatrixMatrixModelsmaxnetmodeltoolsmvtnormnppcaPPpillarpkgconfigplyrpROCproxyquadprogquantregR6randtoolboxrbibutilsRcppRcppEigenRcppProgressRcppThreadRdpackrlangrngWELLrobustrobustbaserrcovrvinecopulibs2sfshapeSparseMsurvivalterratibbletidyselectunitsutf8vctrswdmwithrwk
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Generate ensemble predictions from S4DM range maps | ensemble_range_map |
| Evaluate S4DM range map quality | evaluate_range_map |
| Fit density-ratio distribution models in a plug-and-play framework. | fit_density_ratio |
| Fit presence-background distribution models in a plug-and-play framework. | fit_plug_and_play |
| Extract background data for SDM fitting. | get_env_bg |
| Extract presence data for SDM fitting. | get_env_pres |
| Generate Response Curves | get_response_curves |
| Make a range map using plug-and-play modeling. | make_range_map |
| Projects fitted density-ratio distribution models onto new covariates. | project_density_ratio |
| Projects fitted plug-and-play distribution models onto new covariates. | project_plug_and_play |
| Example S4DM occurrence data | sample_points |
| Thresholds a continuous relative occurrence rate raster to create a binary raster. | sdm_threshold |
| Split data for k-fold spatially stratified cross validation | stratify_random |
| Split data for k-fold spatially stratified cross validation | stratify_spatial |
