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https://github.com/sylvainschmitt/SSDM
Stacked Species Distribution Modelling R package
https://github.com/sylvainschmitt/SSDM
Last synced: 3 months ago
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Stacked Species Distribution Modelling R package
- Host: GitHub
- URL: https://github.com/sylvainschmitt/SSDM
- Owner: sylvainschmitt
- License: other
- Created: 2015-12-17T04:24:52.000Z (almost 9 years ago)
- Default Branch: master
- Last Pushed: 2023-10-24T12:03:57.000Z (about 1 year ago)
- Last Synced: 2024-03-22T12:52:45.869Z (8 months ago)
- Language: HTML
- Size: 5.37 MB
- Stars: 40
- Watchers: 3
- Forks: 17
- Open Issues: 26
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- open-sustainable-technology - SSDM - A package to map species richness and endemism based on stacked species distribution models. (Biosphere / Species Distribution Modeling)
README
SSDM: Stacked species distribution modelling
================
[](https://github.com/sylvainschmitt/SSDM/actions/workflows/R-CMD-check.yaml)
[](https://www.r-pkg.org/pkg/SSDM)
[](https://app.codecov.io/gh/sylvainschmitt/SSDM?branch=master) [](http://depsy.org/package/r/SSDM)
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`.
For a full list of changes see [`NEWS`](./NEWS.md).
Installation
============
Please be aware that SSDM package use a lot of dependencies (see [`DESCRIPTION`](./DESCRIPTION))
### Install from Github
You can install the latest version of **SSDM** from Github using the [`devtools`](https://github.com/r-lib/devtools) package:
``` r
if (!requireNamespace("devtools", quietly = TRUE))
install.packages("devtools")
devtools::install_github("sylvainschmitt/SSDM")
```
### Install from CRAN
The stable version of **SSDM**, is available on CRAN:
``` r
install.packages("SSDM")
```
*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.*
Usage
=====
After installing, **SSDM** package, you can launch the graphical user interface by typing gui() in the console.
[**Click to enlarge**](https://raw.githubusercontent.com/sylvainschmitt/SSDM/master/examples/SSDM.gif)
Functionnalities
================
SSDM provides five categories of functions (that you can find in details below): Data preparation, Modelling main functions, Model main methods, Model classes, and Miscellaneous.
### Data preparation
- `load_occ`: Load occurrence data
- `load_var`: Load environmental variables
### Modelling main functions
- `modelling`: Build an SDM using a single algorithm
- `ensemble_modelling`: Build an SDM that assembles multiple algorithms
- `stack_modelling`: Build an SSDMs that assembles multiple algorithms and species
### Model main methods
- `ensemble,Algorithm.SDM-method`: Build an ensemble SDM
- `stacking,Ensemble.SDM-method`: Build an SSDM
- `update,Stacked.SDM-method`: Update a previous SSDM with new occurrence data
### Model classes
- `Algorithm.SDM`: S4 class to represent SDMs
- `Ensemble.SDM`: S4 class to represent ensemble SDMs
- `Stacked.SDM`: S4 class to represent SSDMs
### Miscellanous
- `gui`: user-friendly interface for SSDM package
- `plot.model`: Plot SDMs
- `save.model`: Save SDMs
- `load.model`: Load SDMs