Skip to content

Commit

Permalink
Browse files Browse the repository at this point in the history
  • Loading branch information
lukasbaumbach committed Apr 30, 2020
2 parents 383bb61 + ec1a30b commit d8152bd
Showing 1 changed file with 1 addition and 1 deletion.
2 changes: 1 addition & 1 deletion README.md
Original file line number Diff line number Diff line change
@@ -1,7 +1,7 @@
SSDM: Stacked species distribution modelling
================

[![Travis-CI Build Status](https://travis-ci.org/sylvainschmitt/SSDM.svg?branch=master)](https://travis-ci.org/sylvainschmitt/SSDM)[![CRAN](https://www.r-pkg.org/badges/version/SSDM)](https://CRAN.R-project.org/package=SSDM) [![Downloads](http://cranlogs.r-pkg.org/badges/SSDM?color=brightgreen)](http://www.r-pkg.org/pkg/SSDM) [![Coverage Status](https://img.shields.io/codecov/c/github/sylvainschmitt/SSDM/master.svg)](https://codecov.io/github/sylvainschmitt/SSDM?branch=master) [![Research software impact](http://depsy.org/api/package/cran/SSDM/badge.svg)](http://depsy.org/package/r/SSDM)
[![Travis-CI Build Status](https://travis-ci.org/sylvainschmitt/SSDM.svg?branch=master)](https://travis-ci.org/sylvainschmitt/SSDM)[![CRAN](https://www.r-pkg.org/badges/version/SSDM)](https://CRAN.R-project.org/package=SSDM) [![Downloads](http://cranlogs.r-pkg.org/badges/SSDM?color=brightgreen)](http://www.r-pkg.org/pkg/SSDM) [![Coverage Status](https://img.shields.io/codecov/c/github/sylvainschmitt/SSDM/master.svg)](https://codecov.io/github/sylvainschmitt/SSDM?branch=master) [![Research software impact](http://depsy.org/api/package/cran/SSDM/badge.svg)](http://depsy.org/package/r/SSDM) [![Gitter](https://badges.gitter.im/S-SDM/community.svg)](https://gitter.im/S-SDM/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge)

SSDM 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`.

Expand Down

0 comments on commit d8152bd

Please sign in to comment.