https://github.com/bmaitner/s4dm
An R package focused on Species Distribution Modelling techniques for species with small sample sizes
Science Score: 36.0%
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Keywords
Repository
An R package focused on Species Distribution Modelling techniques for species with small sample sizes
Basic Info
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- Stars: 3
- Watchers: 1
- Forks: 1
- Open Issues: 1
- Releases: 1
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Metadata Files
README.md
Small Sample Size Species Distribution Modeling
The S4DM R package
This repository contains an R package that implements Species Distribution Modeling methods which work even when there are relatively few occurrence records (as is the case for poorly-sample or range-restricted species). These methods were primarily developed by the Drake lab, and include three types of methods: 1) Plug-and-play models, 2) environmental-range models, and 3) density-ratio models. Most of the important functions in this package are wrappers around existing functions that handle density estimation or density-ratio estimation. Much of this code was created by modifying existing code at https://github.com/DrakeLab/PlugNPlay in order to make functions more modular and extensible.
How it works
The package is build on a hierarchy of modular functions, each of which calls on lower-level functions:
- The highest-level functions are
make_range_mapandevaluate_range_map, which are wrappers for... - The next-highest-level functions,
fit_plug_and_play,fit_density_ratio,project_plug_and_play, andproject_density_ratio, which are wrappers for ... - Internal modules such as
pnp_kdeordr_ulsif. These modules both model the environmental covariates and predict values at environmental covariates from fitted models. These modules are largely wrappers around existing functions for fitting density functions or density-ratios. Modules beginning with "pnp" pertain to density functions while models beginning with "dr" pertain to density ratio functions.
This hierarchical structure built on low-level internal modules is designed to allow for the easy addition of new methods by adding small, self-contained modules. The highest-level functions are intended only for quick-and-dirty analyses or quick visualizations. We recommend that users focus on the "fit" and "project" functions for work intended for publication.
What is Plug-and-Play?
In general usage, the term plug-and-play (PNP) refers to software or hardware that can be connected without any additional setup or configuration. In the context of species distribution models, plug-and-play is a framework developed by Drake and Richards (https://esajournals.onlinelibrary.wiley.com/doi/full/10.1002/ecs2.2373) that recognizes that species distribution models can be constructed by "plugging in" any methods that can estimate density functions.
Owner
- Name: Brian Maitner
- Login: bmaitner
- Kind: user
- Repositories: 21
- Profile: https://github.com/bmaitner
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- Issues event: 1
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Last Year
- Issues event: 1
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- Push event: 28
Dependencies
- corpcor * imports
- densratio * imports
- flexclust * imports
- geometry * imports
- kernlab * imports
- maxnet * imports
- mvtnorm * imports
- np * imports
- pROC * imports
- robust * imports
- rvinecopulib * imports
- sf * imports
- terra * imports
- BIEN * suggests
- geodata * suggests
- ggplot2 * suggests
- knitr * suggests
- tidyterra * suggests