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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 review contributions to sdmverse?

Why?

sdmverse is a collaborative metapackage for collecting and visualizing metadata for R packages focusing on species distribution models (SDMs). This package depends on the contributions, reviews, and edits of SDM package maintainers.

Metadata for packages are found in YAML files located in inst/extdata/packages/. For example, take a look at SSDM metadata.

How?

If you have agreed to review a contribution, we'd like to first thank you, as this repository could not function without your help!

In the online Pull Request form on GitHub, use the Add your review button if you have been designated by the editor. Please perform a line-by-line review of the package metadata to be reviewed in inst/extdata/packages/{the_package}.yaml. Once this review is complete, make a decision in the interface (Comment only, Approve, or Request changes) with a message justifying your choice for the editor. In particular, we ask you to confirm that the fields with values of "yes" designated by the package maintainer are indeed reflective of the submitted package's functionality. If necessary, you can use the interface to request specific details from the package maintainer. Details of the various metadata fields are given below.

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 (iterate model fitting over different hyperparameter/variable combinations; 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