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Project Status: Concept – Minimal or no implementation has been done yet, or the repository is only intended to be a limited example, demo, or proof-of-concept. R-CMD-check Codecov test coverage lint check-cran-metadata

How to contribute to sdmverse

Why?

sdmverse is a collaborative metapackage for collecting and visualizing metadata for R packages focusing on species distribution models (SDMs). Thus, sdmverse depends on contributions and reviews from maintainers and developers of SDM packages maintainers to keep up-to-date.

The package metadata is located in independent YAML files in inst/extdata/packages/. For example, take a look at SSDM metadata.

How?

If you want to contribute, please fork this repository on GitHub. Then, on GitHub, in RStudio, or with your favorite editor, create a YAML file in inst/extdata/packages/ named {your_package}.yaml. Copy and fill in the contents of the file example.yaml. Details of the various metadata fields are given below. Once your file is ready and online on your GitHub fork, open a pull request to contribute to sdmverse named "my_packagesubmission". Please provide in your pull request message a quick list, and if possible, a brief description of the functions of your package corresponding to each field for which you have answered yes. You can then ask an editor to start the review process. Remember that the review process is voluntary, so try to be understanding with reviewers and editors. Once the review process has been successfully completed, we will be happy to include your package in the sdmverse to help users discover it. In addition, you will be added to the sdmverse contributors and we may ask for your help as a reviewer for future submissions.

What?

Here you will find details of each metadata field to be filled in. If your package is on CRAN, please use exactly the same text in the common fields.

  • name : Name of package (same as CRAN)
  • title: Title of package (same as CRAN)
  • version: Version of package (same as CRAN)
  • author: Author(s) of package (same as CRAN)
  • maintainer: Maintainer of package (same as CRAN)
  • cran: yes/no
  • github: GitHub URL of package (if none, leave blank)
  • description: Description of package (same as DESCRIPTION file and on CRAN)
  • occ_acquisition: Function(s) to download occurrence data (yes/no).
  • occ_cleaning: Function(s) to clean occurrence data (remove errors, fix georeferencing, etc.; yes/no)
  • data_integration: Function(s) that statistically incorporate multiple types of data (e.g., presence/absence and presence-only; abundance and presence-only; etc.)
  • env_collinearity: Function(s) to explore or address collinearity of environmental data (yes/no)
  • env_process: Functions to process environmental data (interpolation, resampling, etc.; yes/no)
  • bias: Function(s) to assess and/or correct biases in data (e.g., spatially thinning occurrences or weighting background to address occurrence sampling bias; yes/no)
  • study_region: Function(s) to define the study area (yes/no)
  • backg_sample: Function(s) to sample background/pseudoabsence records (yes/no)
  • data_partitioning: Function(s) to partition data for model evaluation (yes/no)
  • mod_fit: Function(s) to fit models (yes/no)
  • mod_tuning: Function(s) to tune models (yes/no)
  • mod_ensemble: Function(s) to generate ensemble models (yes/no)
  • mod_stack: Function(s) to stack multiple single-species models to estimate community composition / diversity (yes/no)
  • mod_evaluate: Function(s) to evaluate model performance (yes/no)
  • mod_multispecies: Function(s) that model multiple species at once to estimate community composition / diversity (i.e., not a “stacked model; yes/no)
  • mod_mechanistic: Function(s) that account for physiological, genetic, and/or trait-based components of species-environmental relationships (i.e., not just correlative; yes/no)
  • pred_general: Function(s) to make model predictions (yes/no)
  • pred_extrapolation: Function(s) to address or visualize prediction extrapolation (yes/no)
  • pred_inspect: Function(s) to inspect or visualize the behavior of the model prediction (response curves, variable importance, xAI, etc.; yes/no)
  • post_processing: Function(s) to post-process SDM predictions (dispersal or population simulation, niche overlap, estimation of biodiversity, etc.; yes/no)
  • gui: Includes a graphical user interface (yes/no)
  • metadata: Function(s) to record analysis metadata (yes/no)
  • manuscript_citation: Full citation of associated manuscript(s) (if none, leave blank; if multiple, separate with semicolons)
  • manuscript_doi: DOI for manuscripts in previous field (if multiple, separate with semicolons)

Thanks! ❤️ ❤️ ❤️

sdmverse Team