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Distribution Modeling","monthly_downloads":710,"total_dependent_repos":1,"total_dependent_packages":1,"readme":"SSDM: Stacked species distribution modelling\n================\n\n[![R-CMD-check](https://github.com/sylvainschmitt/SSDM/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/sylvainschmitt/SSDM/actions/workflows/R-CMD-check.yaml)\n[![Downloads](http://cranlogs.r-pkg.org/badges/SSDM?color=brightgreen)](https://www.r-pkg.org/pkg/SSDM) \n[![Codecov test coverage](https://codecov.io/gh/sylvainschmitt/SSDM/branch/master/graph/badge.svg)](https://app.codecov.io/gh/sylvainschmitt/SSDM?branch=master) [![Research software impact](http://depsy.org/api/package/cran/SSDM/badge.svg)](http://depsy.org/package/r/SSDM)\n\nSSDM is a package to map species richness and endemism based on stacked species distribution models (SSDM). Individual SDMs can be created using a single or multiple algorithms (ensemble SDMs). For each species, an SDM can yield a habitat suitability map, a binary map, a between-algorithm variance map, and can assess variable importance, algorithm accuracy, and between-algorithm correlation. Methods to stack individual SDMs include summing individual probabilities and thresholding then summing. Thresholding can be based on a specific evaluation metric or by drawing repeatedly from a Bernouilli distribution. The SSDM package also provides a user-friendly interface `gui`.\n\nFor a full list of changes see [`NEWS`](./NEWS.md).\n\nInstallation\n============\n\nPlease be aware that SSDM package use a lot of dependencies (see [`DESCRIPTION`](./DESCRIPTION))\n\n### Install from Github\n\nYou can install the latest version of **SSDM** from Github using the [`devtools`](https://github.com/r-lib/devtools) package:\n\n``` r\nif (!requireNamespace(\"devtools\", quietly = TRUE))\n  install.packages(\"devtools\")\n\ndevtools::install_github(\"sylvainschmitt/SSDM\")\n```\n\n### Install from CRAN\n\nThe stable version of **SSDM**, is available on CRAN:\n\n``` r\ninstall.packages(\"SSDM\")\n```\n\n*We advise users to install from github. Due to CRAN policies and the development of SSDM, many new features and bugfixes may be available on CRAN later.*\n\nUsage\n=====\n\nAfter installing, **SSDM** package, you can launch the graphical user interface by typing gui() in the console.\n\n\n[**Click to enlarge**](https://raw.githubusercontent.com/sylvainschmitt/SSDM/master/examples/SSDM.gif)\n\n![Screenshot](https://raw.githubusercontent.com/sylvainschmitt/SSDM/master/examples/SSDM.gif)\n\nFunctionnalities\n================\n\nSSDM provides five categories of functions (that you can find in details below): Data preparation, Modelling main functions, Model main methods, Model classes, and Miscellaneous.\n\n### Data preparation\n\n-   `load_occ`: Load occurrence data\n-   `load_var`: Load environmental variables\n\n### Modelling main functions\n\n-   `modelling`: Build an SDM using a single algorithm\n-   `ensemble_modelling`: Build an SDM that assembles multiple algorithms\n-   `stack_modelling`: Build an SSDMs that assembles multiple algorithms and species\n\n### Model main methods\n\n-   `ensemble,Algorithm.SDM-method`: Build an ensemble SDM\n-   `stacking,Ensemble.SDM-method`: Build an SSDM\n-   `update,Stacked.SDM-method`: Update a previous SSDM with new occurrence data\n\n### Model classes\n\n-   `Algorithm.SDM`: S4 class to represent SDMs\n-   `Ensemble.SDM`: S4 class to represent ensemble SDMs\n-   `Stacked.SDM`: S4 class to represent SSDMs\n\n### Miscellanous\n\n-   `gui`: user-friendly interface for SSDM package\n-   `plot.model`: Plot SDMs\n-   `save.model`: Save SDMs\n-   `load.model`: Load SDMs\n","funding_links":[],"readme_doi_urls":[],"works":{},"citation_counts":{},"total_citations":0,"keywords_from_contributors":["ecology","forests","species"],"project_url":"https://ost.ecosyste.ms/api/v1/projects/159584","html_url":"https://ost.ecosyste.ms/projects/159584"}